Wetlands: Ecology, Conservation and Restoration

WETLANDS: ECOLOGY, CONSERVATION AND RESTORATION No part of this digital document may be reproduced, stored in a retrie...

Author: Raymundo E. Russo

401 downloads 3654 Views 6MB Size Report

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!
Report copyright / DMCA form

DOWNLOAD PDF

WETLANDS: ECOLOGY, CONSERVATION AND RESTORATION

No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services.

WETLANDS: ECOLOGY, CONSERVATION AND RESTORATION

RAYMUNDO E. RUSSO EDITOR

Nova Science Publishers, Inc. New York

Copyright © 2008 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Wetlands : ecology, conservation, and restoration / Raymundo E. Russo (editor). p. cm. ISBN 978-1-60876-354-2 (E-Book) 1. Wetland ecology. 2. Wetland conservation. 3. Wetland restoration. I. Russo, Raymundo E. QH541.5.M3W4836 2008 577.68--dc22 2008030635

Published by Nova Science Publishers, Inc.

New York

CONTENTS Preface

vii

Expert Commentary Two Alternative Modes for Diffuse Pollution Control by Wetlands Chen Qingfeng, Shan Baoqing and Ma Junjian

1

Short Communication Multiangular Imaging of Wetlands in New England Lesley-Ann L. Dupigny-Giroux and Eden Furtak-Cole

7

Research and Review Articles Chapter 1

Wetlands: Water “Living Filters”? Ana Dordio, A. J. Palace Carvalho and Ana Paula Pinto

Chapter 2

Remote Sensing Data for Regional Wetland Mapping in the United States: Trends and Future Prospects Megan W. Lang and Greg W. McCarty

Chapter 3

Chapter 4

Transforming Useless Swamps into Valuable Wetlands: Evaluating America’s Policy, 1970-2008 Andrea K.Gerlak and Jeanne N. Clarke Dynamics of Coastal Wetlands and Land Use Changes in the Watershed: Implications for the Biodiversity Miguel Ángel Esteve, M. Francisca Carreño, Francisco Robledano, Julia Martínez-Fernández and Jesús Miñano

Chapter 5

Pathogen Removal in Constructed Wetlands Kela P. Weber and Raymond L. Legge

Chapter 6

The Role of Harvest and Plant Decomposition in Constructed Wetlands Juan A. Álvarez and Eloy Bécares

15

73

113

133

177

213

vi Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Contents Nutrition and Toxicity of Inorganic Substances from Wastewater in Constructed Wetlands Zhenhua Zhang, Zed Rengel and Kathy Meney A Conceptual and Methodological Framework for the Study of Vegetated Fluvial Landscape Evolutionary Trajectories Dov Corenblit, Johannes Steiger, Eric Tabacchi and Angela M. Gurnell Macrophyte Morphological Response to the Industrial Effluent Toxicity in a Constructed Wetland H. R. Hadad, M. M. Mufarrege, M. Pinciroli, G. Di Luca, V. del Sastre and M. A. Maine Phytoremediation Processes for Water and Air Pollution Control in the Aspects of Nutrient and Carbon Dioxide Removals Jae Seong Rhee, Yonghui Song, Fasheng Li and Janjit Iamchaturapatr

271

295

325

Phytoplankton Biomass Regulation in Contrasting Environmental States of Temporary Pools Silvia Martín, Marta Rodríguez and David G. Angeler

359

Can Tern Migrants Coexist with Urban Development and Estuarine Recreational Activities? Ken Chan, Jill Dening and Marja-Leena Malinen

373

Chapter 13

Agricultural Wetlands R. Kröger

Chapter 14

Profiling Cover Cycle Dynamics for Prairie Pothole Wetland Landscapes Rebecca L. Phillips and Ofer Beeri

Index

247

391

407 419

PREFACE Wetlands are lands where saturation with water is the dominant factor determining the nature of soil development and the types of plant and animal communities living in the soil and on its surface. Wetlands vary widely because of regional and local differences in soils, topography, climate, hydrology, water chemistry, vegetation, and other factors, including human disturbance. Indeed, wetlands are found from the tundra to the tropics and on every continent except Antarctica. This new book brings together the latest research in the field. Short Communication - Multiple view angles (MVA) or multiangular imaging represents a yet to be explored use of the remote sensing of wetlands. The ability to view the landscape off-nadir (traditionally the surface is viewed at right angles) allows for the quantification of moisture stress, species separation and the proportion of vegetation to standing water in these ecosystems. This commentary will focus on the ratio of two broadband wavelengths (nearinfrared to blue) derived from multiangular images acquired by the Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) of wetlands across New England. The resulting insights into the photointerpretation, monitoring and mapping of wetlands will be highlighted. Chapter 1 - Human societies have indirectly used natural wetlands as wastewater discharge sites for many centuries. Observations of the wastewater depuration capacity of natural wetlands have led to a greater understanding of the potential of these ecosystems for pollutant assimilation and have stimulated the development of artificial wetlands systems for treatment of wastewaters from a variety of sources. Constructed wetlands, in contrast to natural wetlands, are human-made systems that are designed, built and operated to emulate wetlands or functions of natural wetlands for human desires or needs. Constructed wetlands have recently received considerable attention as low cost, efficient means to clean-up not only municipal wastewaters but also point and non-point wastewaters, such as acid mine drainage, agricultural effluents, landfill leachates, petrochemicals, as well as industrial effluents. Currently, untreated wastewater discharge in the natural wetlands sites is becoming an increasingly abandoned practice whereas the use of constructed wetlands for treatment of wastewater is an emerging technology worldwide. However, natural wetlands still play an important role in the improvement of water quality as they act as buffer zones surrounding water bodies and as a polishing stage for the effluents from conventional municipal wastewater treatment plants, before they reach the receiving water streams. In fact, one of the emerging issues in environmental science has been the inefficiency of wastewater treatment plants to remove several xenobiotic organic compounds such as pesticides and pharmaceutical residues and consequent contamination of the receiving water bodies. Recent

viii

Raymundo E. Russo

studies have shown that wetlands systems were able to efficiently remove many of these compounds, thus reaffirming the importance of the role which can be played by wetlands in water quality preservation. The aim of this work is to present a review on the application of wetlands as “living filters” for water purification. Emphasis was focused on the removal of micropollutants, especially xenobiotic organic compounds such as pharmaceuticals residues, which are not efficiently removed by conventional municipal wastewater treatment plants. Furthermore, the role of wetlands as protection zones which contribute to the improvement of the aquatic ecosystems’ quality will be discussed. Chapter 2 - Historically, the biologic, aesthetic, and economic values of wetlands were largely unappreciated. Wetlands within the United States have been and are continuing to disappear rapidly. Efforts are being made to conserve remaining wetlands and many regulatory policies have been adopted in support of this goal. To regulate the loss, preservation, and/or restoration of wetlands and to judge the effectiveness of these regulatory efforts in preserving associated ecosystem services, wetlands must be routinely monitored. Wetland mapping is an essential part of this monitoring program and much effort has been made by the US state and federal governments, as well as other organizations, to provide quality wetland map products. Wetland maps can serve a variety of purposes including regulation and natural resource management. They can also be used to parameterize models that quantify water quality and quantity, as well as the provision of wetland ecosystem services, at the watershed scale. Wetland hydrology is the most important abiotic factor controlling ecosystem function and extent, and it should therefore be a vital part of any wetland mapping or monitoring program. New approaches are needed to not only map wetlands, but also to monitor wetland hydrology as it varies in response to weather, vegetation phenology, surrounding landuse change, and other anthropogenic forces including climate change. Recently developed remote sensing technologies and techniques have the potential to improve the detail and reliability of wetland maps and the ability to monitor important parameters such as hydrology. Various types of remotely sensed data (e.g., aerial photographs, multispectral, hyperspectral, passive microwave, radar, and lidar) have different capabilities with specific advantages and disadvantages for wetland mapping at the regional scale. Although aerial photographs were traditionally used to map wetlands and infer hydrology, fine-resolution optical images are now available more frequently as commercial agencies increase satellite coverage (e.g., Quickbird and IKONOS). However, optical data, such as aerial photographs and multispectral satellite images have limitations, including their inability to detect hydrology below dense vegetative canopies and their limited ability to detect variations in hydrology (i.e., inundation and soil moisture). The restrictions of optical data are increasingly being compensated for with the use of new technologies, including synthetic aperture radar, lidar, and geospatial modeling. The availability of these new data sources is increasing rapidly. For example, many states in the US are now collecting synoptic state-wide coverages of lidar data. The sources, strengths, and limitations of different types of remotely sensed data are reviewed in this chapter, as well as the importance of temporal and spatial resolution necessary for regional scale wetland mapping efforts. The potential of multi-temporal, multi-sensor approaches that capitalize on geospatial modeling are emphasized for meeting current wetland mapping challenges. Chapter 3 - This paper traces the evolution of America’s wetland policy beginning with passage of the Clean Water Act (CWA) of 1972. This law, for the first time, established a

Preface

ix

federal program to protect wetlands, dramatically elevating the value of these ecosystems. However, despite attitudinal changes and new governmental programs, the nation continues to lose its potentially valuable wetlands -- albeit at a slower rate than was the case in the 1970s and prior to the passage of the CWA. This chapter offers an objective evaluation of the federal wetlands protection policy. The authors place this evaluation within a broad societal context, showing that since 1970 there have occurred sweeping demographic, economic, and political changes that clearly have impacted the extent of wetlands in the United States. They argue that Section 404 has failed to reverse the net loss of wetlands in the U.S. Moreover, it has evolved into a policy lightening rod within the water resources arena and been a major factor in Congress’ failure to revise and reauthorize the Clean Water Act. Finally, the authors offer some recommendations designed to improve the policy, arguing for heightened wetlands protection through partnerships and acquisitions. Chapter 4 - The Mediterranean coastal landscapes have suffered significant changes along the last decades due to the agricultural intensification and tourist development. Such changes have modified the water flows and specifically the hydrological regime of wetlands, as has occurred in the Mar Menor (Southeast Spain). The Mar Menor coastal lagoon and associated wetlands present noticeable ecological and biodiversity values. However, the landuse changes in the watershed and the consequent changes in the water and nutrient flows along the period 1980-2005 are threatening the conservation of these wetlands. A dynamic model has been developed to simulate the key environmental and socio-economic factors driving the export of nutrients to the Mar Menor lagoon and associated wetlands, where some eutrophication processes have appeared. In this chapter the changes in the vegetal and faunistic assemblages are analysed. Vegetal communities are studied by means of remote sensing techniques, which have provided information about the changes in area and habitat composition of the wetlands along the considered period. This has shown that the habitats more negatively affected by the hydrological changes are those most threatened in the international context and with a highest interest from the point of view of biodiversity conservation. It has also been possible to verify the direct relationships between all these changes at wetlands scale and the agricultural changes at the watershed scale. Two faunistic communities especially sensitive to these ecosystemic changes have also been studied: i) Wandering beetles and ii) Birds (waterbirds and steppe passerines). Wandering beetles (Coleoptera) were studied with pitfall traps in 1984, 1992 and 2003 and steppe passeriforms with line transects in several years along the period. In both communities evident changes have been observed. Regarding beetles, the most halophilous species have been favoured, some of them especially relevant due to its rarity in the European context. The ratio Carabidae/Tenebrionidae has shown to be a good indicator of the hydrological changes of the wetlands. Waterbirds have shown dramatic changes in their relative abundances within the lagoon, with a long-term decline in the most characteristic original species, increases in generalist piscivores and a recent appearance and rapid growth of the herbivores guild. In the case of steppe passeriforms, this community has been negatively affected, especially some species like Melanocorypha calandra. The family Alaudidae has lost importance to the benefit of the families Turdidae and Fringillidae. These changes can be considered a loss of value in relation with the original passeriform community, since the wetland qualifies as a Specially Protected Area under the EU’s Bird Directive, precisely on the basis of its genuine steppe bird assemblage.

x

Raymundo E. Russo

In conclusion, the changes at wetlands scale clearly reflect the hydrological modifications at the watershed scale and have significant effects on the most characteristic biodiversity of the wetlands of coastal arid systems. Chapter 5 - Conventional secondary and tertiary wastewater treatment methods include activated sludge, trickling filters, slow sand filtration, chlorination, ozonation and UV radiation. Chlorination being the most widely used pathogen disinfection method is presently under scrutiny as chlorination can produce carcinogenic trihalomethanes when natural organic matter is present in the wastewater. Constructed wetlands (CWs) have proven to be an effective treatment alternative for the removal and inactivation of pathogens in wastewaters. Constructed wetlands have low principle and operating costs and are fairly simple to design and implement, making them an attractive wastewater treatment alternative when compared to conventional secondary or tertiary treatment processes. Constructed wetlands designed for pathogen treatment are most often preceded by filtration or sedimentation. Pathogen removal efficiencies upwards of 99.99% have been reported by multiple authors employing many different constructed wetland designs. Constructed wetland design tends to be based largely on rule of thumb sizing, as the specific mechanisms and fundamental variables involved in pathogen removal are only vaguely understood. Suggested mechanisms of pathogen treatment in CWs include but are not restricted to sedimentation, natural die-off, temperature, oxidation, predation, unfavourable water chemistry, biofilm interaction, mechanical filtration, exposure to biocides and UV radiation. Pathogen removal has been shown to correlate well with hydraulic retention time. Use of first order decay kinetics is the preferred method to describe and predict pathogen removal in CWs. A severe lack of attention has been given to the comparative quantification of the specific mechanisms contributing to pathogen treatment in constructed wetlands. Small-scale controllable constructed wetland systems are identified as systems which can be used in conducting well-designed controlled experiments where fundamental mechanisms and variables involved in pathogen removal can be comparatively quantified. It is proposed that if the fundamental mechanisms and variables affecting pathogen removal in constructed wetlands are better understood and quantified the large performance variations reported for similarly designed treatment wetland systems can be better explained, engineered and controlled. Chapter 6 - Upon decomposition, at the end of the summer and during the autumn, wetland vegetation releases organic carbon into the wetland system. A part of this organic matter remains in the wetland, and is degraded at different rates during the rest of the year. Therefore, litter decomposition has important consequences on constructed wetlands because it is related to the autochthonous production of organic matter, clogging rates in surface-flow wetlands, and terrestrialization in free-water surface wetlands. The effect of harvest was studied in two free-water surface-flow wetlands. Both wetlands were planted with Typha latifolia with one of the wetlands harvested. On the other hand, decomposition rates of Typha latifolia were quantified during both winter and summer in the non-harvested surface constructed wetland using the litter bag technique. Nutrient concentrations were always lower in the effluent of the harvested wetland, indicating nitrogen and phosphorus release by decomposition of vegetation, in the non-harvested system. In addition, harvesting reduced the effluent TSS and BOD concentrations by 37.3% and 49.2%, respectively, when compared to the non-harvested wetland in spring. Seasonal background concentrations (C*) in the wetlands, increased from winter to spring and decreased again in summer. Organic load and nutrients produced per gram of Typha were evaluated by using in-

Preface

xi

situ Typha degradation experiments. Taking into account the experiments of litter bag technique, no significant differences were found in both variables among the different mesh sizes, with the exception of the control bags in winter. Meso or macrofauna did not play any role in plant decomposition. Decomposition rates were significantly different between winter and summer when considering each mesh size separately. Decomposition rates from adjusted exponential models ranged from 0.0014 to 0.0026 d-1 in winter (5ºC), and from 0.0043 to 0.0052 d-1 in summer (20ºC). Typha decomposition rates were compared with others macrophytes. From these decomposition rates, it is estimated that 31% of the initial mass of plant detritus would remain in the system after one year. Based on the research conducted during several experiments, harvesting can be recommended as an operational and management strategy in warm climates and diluted wastewater conditions. Chapter 7 - The use of constructed wetlands for purification of wastewater has received increasing attention around the world. A variety of wetland plant species (including ornamental ones) as either a monoculture or species mixes are used in constructed wetlands. Plants play an extremely important role in removing pollutants from wastewater. Although there is considerable information on plant productivity, biomass and nutrient dynamics in natural and fertilized wetlands, most studies on constructed wetlands for treatment of wastewaters have only addressed general aspects of plant growth and nutrient accumulation. Nutrition and toxicity of inorganic substances such as nitrogen, sulphur, salts and metals in wastewater on wetland plants has not been fully investigated and their interactive effects and environmental cycling in constructed wetlands remain poorly understood. Nitrogen nutrition is the most important factor influencing plant performance in constructed wetlands, but higher NH4-N may become toxic to wetland plants. Sulphur is an essential nutrient for plant growth, but under waterlogged conditions sulphate is reduced to hydrogen sulphide that is highly toxic to wetland plants. Many metals in wastewater are essential micronutrients for wetland plants, but become toxic if their concentration exceeds a specific critical point. A proper amount of salts is essential for plant growth, but high concentrations of salts, particularly sodium chloride in wastewater have harmful effects on plant growth. Wetland plant species have differential capacity to take up nutrients, different preference for nitrogen forms and have evolved various adaptive mechanisms protecting them against toxicity of inorganic substances. Given that plants are an integral part of constructed wetlands, the selection of suitable species, improvement of cultivations and determination of factors affecting growth are needed to produce healthy and effective wetland ecosystems. Understanding biogeochemical cycling in wetlands as well as nutrition and toxicity of inorganic substances from wastewater on plant development and function may help reduce performance variability and enhance pollutant removal in constructed wetlands. Chapter 8 - This chapter presents a conceptual and methodological framework to study temporal and spatial changes of fluvial landforms and associated plant communities and to identify the underlying causes of either progressive or sudden changes. Mutual interactions and feedbacks between hydrogeomorphic processes, fluvial landforms and vegetation dynamics are considered within this framework, leading to the analysis of biogeomorphic (i.e., landforms and associated vegetation communities) evolution trajectories within the fluvial corridor and to the evaluation of their consequences for ecological and geomorphic forms and processes.

xii

Raymundo E. Russo

First, fundamental aspects linked to the conceptual model of Fluvial Biogeomorphic Succession (FBS model) proposed by the authors (cf. Corenblit et al. 2007) are presented. This model describes the most dominant biogeomorphic succession trajectory of temperate rivers under current bioclimatic and anthropogenic conditions, starting from the rejuvenated state (bare sediment within the channel after a destructive flood). This dynamic model involves a characteristic sequence of four biogeomorphic phases where interactions of hydrogeomorphic processes and vegetation dynamics are either strong or weak according to different spatiotemporal configurations. The characteristic evolutionary trajectory corresponds to a progressive shift from the dominance of allogenic (hydrogeomorphic) processes to the dominance of autogenic (ecological) processes. It is marked by a development of specific stabilised vegetated landforms such as banks, islands and floodplains. In particular, the cyclic dynamics of the biogeomorphic succession (i.e., frequency and magnitude of rejuvenation and maturation processes), incorporating critical thresholds are discussed. Second, a conceptual tool for the description and analysis of potential fluvial landscape evolutionary trajectories is proposed. This conceptual tool is a discrete three dimensional biogeomorphic phase-space composed of five key-stages of vegetation development (bare sediment; seedlings and saplings; adult herbs; adult shrubs; adult trees) within four distinct zones of the river corridor, exposed to four distinct levels of hydrogeomorphic disturbance (permanent submerged area; high flood-frequency area; low flood-frequency floodplain; nonsubmersible area). The four main processes controlling shifts between biogeomorphic configurations within the phase-space are related to the critical role of pioneer vegetation within fluvial landscape dynamics. Finally, a methodological basis to test and to refine the model using a probabilistic transition analysis combining the biogeomorphic phase-space, empirical field data, GIS and remote sensing at local and regional scales is proposed and its applications for river management are discussed. Chapter 9 – This chapter describes the morphological variations of floating and rooted macrophytes growing in a wetland constructed for the treatment of industrial wastewater and in natural wetlands of the Middle Paraná River floodplain, Argentina. Cross-sectional areas (CSA) of the root, stele and of metaxylem vessels and the total metaxylem CSA were measured. In addition, parameters such as dry biomass, chlorophyll concentration, and metal (Cr, Ni and Zn) and nutrient (P) concentrations were compared. During the first months of operation of the constructed wetland, only sewage was poured and floating macrophytes were dominant. After five years of operation, Typha domingensis was the dominant species in the constructed wetland. In this species, biomass and height of the plants at the inlet and outlet were significantly higher than in the natural wetlands. The plants growing at the inlet showed root and stele CSA values significantly higher than those for the plants growing at the outlet and in natural wetlands. The total metaxylem vessels CSA of the inlet plants were significantly higher than those obtained in the outlet and natural wetlands owing to the plants of this site showed the highest number of metaxylem vessels. In order to determine the morphological changes as an adaptive response to the contaminants present in the effluent, greenhouse experiments were carried out with P. stratiotes and E. crassipes. In P. stratiotes, Ni and Cr+Ni+Zn treatments were the most toxic ones, in which biomass, chlorophyll and the internal morphological parameters of roots decreased significantly, while in E. crassipes Ni caused toxic effects in the internal as well as the external morphology. The modifications

Preface

xiii

recorded account for the adaptability of T. domingensis to the conditions prevailing in the constructed wetland, which allowed it to become the dominant species. This chapter may contribute to the design and mainteinance of constructed wetlands that include the macrophytes studied. Chapter 10 - The growth of industries and major agricultural enterprises (especially food industries) supplying the human demands for their increasing population causes an annihilation of water ecosystems and an augmentation of water pollutions. These are the main sources of nutrient supplements in water resources. Excess nutrients led to the eutrophication phenomena and in many cases the deterioration of public health. While the role of carbon dioxide (CO2) gas in global climate change has become well-known, which is one of the most important environmental issues of our day, therefore it is necessary to develop technologies for the minimization of CO2 discharging into the atmosphere. Although CO2 occurs naturally in the atmosphere, its current atmospheric concentrations have been greatly affected by human activities. One ecological method used for treating polluted water containing high nutrients and encouraging CO2 sequestration is treatment wetlands, where various aquatic plants are used for purifying the water and wastewater from excess nutrients and also withdrawing the anthropogenic CO2 from polluted atmosphere into plant’s biomass by photosynthesis process. Although wetland area around the world has diminished and continues to lose due to economic development, agriculture, and other landscape alterations, recently many of these losses are compensated by construction of new wetlands due to an our increasing understanding of wetland functions and values on global environment. Chapter 11 - Although abiotic forces play a fundamental role in community and process regulation of disturbed wetland ecosystems, biotic interaction is increasingly recognised for having important regulatory feedback effects. This chapter reports on the context-specific role of biotic and abiotic regulation of phytoplankton biomass in temporary ponds. Contamination of artificial ponds with different application concentrations of a fire retardant resulted in alterations of the trophic status, primary producer and zooplankton communities in treatment ponds. Principal component analyses suggested that facilitation of phytoplankton biomass through cladocerans was the most important controlling factor in nutrient-limited control ponds. These biotic interaction effects disappeared in retardant treatment ponds where phytoplankton biomass was almost exclusively controlled by water depth fluctuation. This context-specific, eutrophication-mediated physical control of algal biomass in treatment ponds adds a new dimension to the traditional perspective of resource and consumer control of phytoplankton in alternative ecosystem states in lakes. The context-dependent interplay of physical and biotic processes in wetlands will likely influence applied issues and challenge wetland management and restoration. Chapter 12 - Urbanisation and recreational activities are two of the major causes of population declines of species, and throughout the world they continue to spread and intensify at a rapid rate. The two are often linked—an increase in recreational activities is often associated with nearby growth in residential development and vice versa. Developmental growth is greatest in places of high tourism value, such as in coastal areas with sandy shores. Sandy coasts are popular with beach walking and jogging, swimming, off-road vehicles, boating, ecotourism, and other outdoor activities. The most concentrated activities are in estuaries with sandbanks and intertidal flats that are protected from the open ocean. Yet the same estuaries are often sensitive ecosystems, commonly frequented by a variety of resident

xiv

Raymundo E. Russo

and migrant birds that use the areas to breed, forage, or roost. Increasing incidents of human disturbance can affect breeding behavior, feeding patterns, opportunities for rest, and decline in estuarine bird abundance. The direct impact on reproduction in breeding birds is obvious, but survival of migratory species is also affected through ineffective build-up of requisite fat reserves to successfully undertake their migratory journey. For both resident and migrant birds, disturbance could result in reduced feeding time, lowering the necessary fat reserves for survival. Chapter 13 - Increased agricultural production, land drainage and resultant land use changes have increased loads of non-point source pollutants being discharged into aquatic ecosystems. Estimates suggest that non-point source pollution (NPS) contributes over 65% of the total pollution load to inland surface waters, including 332,000 km of rivers, 215,000 ha of lakes and 1.5 x 106 ha of estuaries. There are two types of agricultural wetlands that could mitigate NPS pollution: constructed wetlands and surface drainage ditches. Constructed wetlands are commonly used to mitigate increased nutrient, biological oxygen demand, and pesticide loads prior to entering receiving waters. However, some farmers will forgo the practice of constructing a wetland for routing water because of associated costs of construction, maintenance and loss of land in agricultural production. Agricultural drainage ditches are management tools put in place by farmers to rapidly remove standing water from their farmland. Drainage ditch function is simply one of drainage; however, research has shown that surface vegetated drainage ditches are primary intercept wetlands characterized by an ephemerally inundated hydroperiod, developed hydro-soils and a suite of facultative hydrophytes. Studies in the mid-South US have shown vegetated surface drainage ditches to reduce both pesticide and nutrients loads within the ditch prior to effluent reaching receiving waters. This is increasingly important in today’s landscape where fertilizer and pesticide applications are still high. Pollutant reduction capacity within ditches may be improved with temporal and spatial manipulation of water residence at critical junctions of non-point pollutant loss throughout the year. Primary interception, transformation and mitigation of agricultural pollutants has far reaching consequences for aquatic ecosystem health, downstream eutrophication, and coastal dynamics such as hypoxia, commercial fisheries and economic development. Chapter 14 - Over 3 million wetlands populate the U.S. portion of the Prairie Pothole Region (PPR), where conservation goals include restoration and preservation of the cover cycle. The cover cycle is characterized by seasonal and annual changes in vegetation and open water and is closely coupled to climate and natural ecosystem functions. A complete cover cycle include periods of time when high waters drown hydric vegetation during deluge and periods where hydric vegetation expands as waters dry-down during drought. Changes in wetland cover may occur on weekly, monthly, or annual time-scales. These dynamics contribute to a rich diversity of habitats that support more waterfowl than any other region in North America. In addition temporal dynamics, PPR wetlands rarely function as single entities because of shared surface and/or groundwater hydrology. This spatial interdependence requires PPR wetland functional assessments represent populations of wetlands, commonly referred to as “profiles.” Synoptic data profiling cover cycle stage and return time for populations of wetlands would scaffold large-scale investigations of ecosystems services, habitat status, and sensitivity to climate change. This chapter describes application of previously developed tools for synoptic delineation of wetland water and hydric vegetation cover to classify cover cycle for thousands of wetland

Preface

xv

basins within a single satellite image (10,000-30,000 km2 of land area). Using satellite data layers in geographic information systems (GIS), wetland profiles developed using current (2007) wetland cover data are compared with profiles developed using National Wetland Inventory (NWI) data from 1980. Results underscore the dynamic nature of these ecosystems and the need for current observations when setting conservation goals, monitoring restoration effectiveness, and evaluating anthropogenic impacts.

In: Wetlands: Ecology, Conservation and Restoration ISBN: 978-1-60456-995-7 Editor: Raymundo E. Russo © 2008 Nova Science Publishers, Inc.

Expert Commentary

TWO ALTERNATIVE MODES FOR DIFFUSE POLLUTION CONTROL BY WETLANDS Chen Qingfeng∗1, Shan Baoqing2 and Ma Junjian1 1

Shandong Analysis and Test Center, Jinan, 250014, China Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China

2

Diffuse pollution has been identified as an important cause of surface water quality degradation (Novotny, 1999). Some researches indicate that it is responsible for the transport of sediment, nutrients, heavy metals, oils, hydrocarbons and pesticides (Deletic, 1998; Schreiber et al., 2001; Lazzarotto et al., 2005). Furthermore, stormwater pollution can also have a profound effect on the ecological health of streams and reservoirs and is one of the main reasons for ecosystem degradation (Yin and Mao, 2002). There are many ecological engineering techniques, such as buffer zones, ponds, wetlands and riparian zones currently in use, and wetlands have been shown to be effective in removing pollutants from runoff water (Mitsch et al., 2002). In order to improve treatment efficiency, wetlands can be used as treatment trains. According to the flow route, the control chains can be designed in on-line and off-line treatment trains (Bardin et al., 2001; Michael and John, 2003; Shan et al., 2006; Paolo et al., 2006; Chen et al., 2007). For the on-line treatment train, all of the runoff from a storm routes through all the system structures, which are distributed on the runoff route. The treatment train may have lower pollutant removal efficiency than off-line treatment train if the system storage is not large enough to hold all the runoff from a significant storm event. For the off-line treatment train, the system structures are distributed away from the runoff route. The treatment train is designed to intercept the “first flush”, which has much higher concentration of pollutants in the initial runoff. The later runoff, with lower concentration of pollutants, overflows the catchment directly. The off-line treatment train requires less land area and it is an economical and effective measure for the control of runoff pollution in urban areas. Every mode plays an important role in stabilizing the adjacent ecosystems and reducing the load of runoff pollution.

∗

Email:[email protected]

2

Chen Qingfeng, Shan Baoqing and Ma Junjian

In the process of diffuse pollution control, the selection of the mode is the key step. The selection of the two alternative modes for diffuse pollution control is based on concern with native topography, climate, storm water volume and available land area of the catchment (Figure 1).

Figure 1. The flow chart of the two alternative modes selection for diffuse pollution control.

If there is enough available land area in the catchment, then both modes can be selected. Otherwise, the offline mode may be the only choice for diffuse pollution control. Furthermore, the online mode may be the better choice if reuse of rainwater, additional biologic habitat, and aesthetics value are taken into consideration. In other conditions, the offline mode may be an effective choice for diffuse pollution control. In every mode, many ecological engineering techniques can be included. However, the application of the two modes in urban zoos has received little research attention. A detailed study was carried out from April 2003 to August 2005 in Wuhan City Zoo, which is surrounded by Moshui Lake. In this study, two catchments were selected to study the characteristics and performances of the online and offline modes in Wuhan City Zoo. For this purpose, an online pond-wetlands system in the Orangutan House Catchment, and an offline filtering ditch-pond system in the Canine House Catchment, were designed to control the small point and diffuse sources of pollution in the urban zoo. In the Orangutan House Catchment, an online pond wetlands system was used to control pollution from small point and diffuse sources. All the engineering constructions were built to adjust the flow rate of storm water and the kinetic energy of runoff on the runoff route. From upland to downstream, the landscape structures included upland grassland, orangutan house, sediment tank (ST), pond (P), the first wetland (W1) and the second wetland (W2). For the huge storage capacity of the pond-wetlands system (1071m3), most of runoff was able to be stored temporarily and purified by physical, chemical and biologic processes in the wetlands.

Two Alternative Modes for Diffuse Pollution Control by Wetlands

3

The online mode flow of the catchment is shown in Figure 2. Through grids, S2 was initially stored in ST on dry days. During rainfall events, all runoff, coming from S1 and S3, as well as S2, flowed through ST, P, W1 and W2 sequentially and then drained into Moshui Lake. In order to save water, the rainwater, stored in the pond-wetlands system, can be reused for flushing the animal house and irrigating the grassland.

Figure 2. Online mode for diffuse pollution control in the Orangutan House Catchment.

Without enough available land area for water treatment constructions, an offline filtering ditch-pond system was designed to control diffuse pollution in the Canine House Catchment. The off-line treatment train was composed of some pretreatment equipments and a filtering ditch–pond system. The pretreatment equipments include a transport ditch, grids and a sediment tank. The filtering ditch–pond system consists of a filtering ditch and two ponds. This system has a storage capacity of 115m3 and can store the initial 13.7mm runoff depths in a storm. Four species of hydrophytes, including Phragmites communis Trirn., Acorus calamus Linn., Alternanthera philoxeroides and Canna generalis, were planted in the ponds. The landscape structures in the catchment include upland grassland, storm transport ditch (T), Canine House, filtering ditch (FD) and ponds (P) from upland to downstream. FD was underground and rebuilt by an old flue, is 83 m in length, 0.5 m in width, and 1.2 min depth. It has three sections: sediment zone, filtration zone and storage zone. There are 9 subsections in the filtration zone and each subsection is filled with one of the following media: gravel, aluminite stone, bulky sand, cobblestone, ceramic granule, silver sand, turf, steel slag and vermiculite. All the ecological engineering constructions were finished in April 2004. According to the pollution characteristics, topography, available land area and climate in the catchment, the off-line treatment train was designed to separate the ‘first flush’ from the runoff. The sketch map of the off-line treatment train is shown in Figure 3. Because the main type of land use is upland (61.4%) in the catchment, the off-line treatment train works in a natural process and requires no power. Through the grids, wastewater from flushing the animal houses (S2) was initially stored in the sediment tank (ST) and overflowed to filtering ditch–pond system for decontamination on dry days. During rainy days, the initial runoff, coming from upland runoff (S1) and roof runoff (S3), as well as wastewater (S2), was diverted to the filtering ditch (FD) for filtration and adsorption. After that, the runoff water overflowed into ponds for further decontamination

4

Chen Qingfeng, Shan Baoqing and Ma Junjian

and then, in the final stage, drained into Lake Moshui. The later runoff, with lower concentration of pollutants, was discharged into the lake directly.

Figure 3. Offline mode for diffuse pollution control in Canine House Catchment.

The results showed that the two modes both improved runoff water quality and had high retention rates for water and pollutants. In the outflows, the event mean concentrations (EMCs) of total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) were reduced by 88%, 59%, 46% and 71% for the online mode, and those were 75%, 50%, 50% and 74% for the offline mode. The annual retention rates of pollutant loads for the online mode were 94.9%–98.5% in the three study years; those for the offline mode were 70.5%–86.4%. Based on calculation, the online mode was able to store the runoff of 66.7 mm rainfall completely, and the offline mode could store that of 31.3 mm rainfall. In addition, the online mode can provide an effective way for rainwater utilization and good habitats for aquatic wildlives, and has an excellent aesthetics value for recreationsal pastimes. The offline mode can save land resources and may be an effective and economical measure for diffuse pollution control in urban areas.

REFERENCES Bardin, JP, Barraud, S, Chocat, B, 2001. Uncertainty in measuring the event pollutant removal performance of on-line detention tanks with permanent outflow. Urban Water 3, 91–106. Chen QF, Shan BQ, Yin CQ, Hu CX. An off-line Filtering Ditch-pond system for Diffuse Pollution Control at Wuhan City Zoo. Ecological Engineering, 2007, 30(4):373-380. Chen QF, Shan BQ, Yin CQ, Hu CX. Two Alternative Modes of Diffuse Pollution Control in an Urban Tourist Area. Journal of Environment Science, 2007, 19(10):1067-1073. Deletic, A, 1998. The first flush load of urban surface runoff. Water Res. 32 (8), 2462–2470. Lazzarotto, P, Prasuhn, V, Butscher, E., Crespi, C., Flu¨ hler, H., Stamm, C., 2005. Phosphorus export dynamics from two Swiss grassland catchments. J. Hydrol. 304, 139– 150. Michael Jr., John, H., 2003. Nutrients in salmon hatchery wastewater and its removal through the use of a wetland constructed to treat off-line setting pond effluent. Aquaculture 226, 213–225. Mitsch, WJ, Lefeuvre, JC, Bouchard, VB, 2002. Ecological engineering—applied to river and wetland restoration. Ecol. Eng. 10, 119–130. Novotny, V, 1999. Integrating diffuse pollution control and water body restoration into watershed management. J. Am. Water Resour. Assoc. 35 (4), 717–727.

Two Alternative Modes for Diffuse Pollution Control by Wetlands

5

Paolo, SC, Gaspare, V, 2006. Simulation of the operation of detention tanks. Water Res. 40 (1), 83–90. Schreiber, J.D., Rebich, R.A., Cooper, C.M., 2001. Dynamics of diffuse pollution from US southern catchements. Wat. Res. 35(10), 2534–2542. Shan BQ, Chen QF, Yin CQ, 2006. On-line control of stormwater pollution by pond-wetlands composite system in urban tourist area[J]. Acta Scientiae Circumstantiae, 26(7): 1068– 1075 (in Chinese). Yin, CQ, Mao, ZP, 2002. Nonpoint pollution control for rural areas of China with ecological engineering technologies. Chin. J. Appl. Ecol. 13 (2), 229–232 (in Chinese).


Short Communication

MULTIANGULAR IMAGING OF WETLANDS IN NEW ENGLAND Lesley-Ann L. Dupigny-Giroux∗ and Eden Furtak-Cole University of Vermont, Department of Geography 200 Old Mill Building, Burlington, VT 05405-0114, 802-656-2156

ABSTRACT Multiple view angles (MVA) or multiangular imaging represents a yet to be explored use of the remote sensing of wetlands. The ability to view the landscape off-nadir (traditionally the surface is viewed at right angles) allows for the quantification of moisture stress, species separation and the proportion of vegetation to standing water in these ecosystems. This commentary will focus on the ratio of two broadband wavelengths (near-infrared to blue) derived from multiangular images acquired by the Airborne Multiangle Imaging SpectroRadiometer (AirMISR) of wetlands across New England. The resulting insights into the photointerpretation, monitoring and mapping of wetlands will be highlighted.

1. INTRODUCTION Multiple view angles (MVA) or multiangular imaging of terrestrial ecosystems has been shown to provide multispectral data not observed from the nadir or other single view angles only, due to the highly anisotropic reflectance of vegetation (Asner et al., 1998). Vegetation parameters may not be the most sensitive to the nadir view angle (Privette, 1995). Other studies have explored the relationship between a sensor’s field of view and vegetation structure (Widlowski et al., 2004; see Diner et al., 2005 for a full description of these studies), land cover classifications (Hyman and Barnsley, 1997) and the role of sub-pixel heterogeneity (Zhang et al. (2002a, b), as well as view angle and reflectance anisostropy at the red wavelengths (Pinty et al., 2002). ∗

E-mail: [email protected]

8

Lesley-Ann L. Dupigny-Giroux and Eden Furtak-Cole

In a recent study (Dupigny-Giroux, 2007), multiangular images from the Airborne Multiangle Imaging SpectroRadiometer (AirMISR) of the Howland Forest in Maine, were used for land use/land cover (LULC) separability under varying moisture conditions in the humid, continental environment of central Maine. The study extended original work by DupignyGiroux and Lewis (1999) that used the ratio of near-infrared/blue wavelengths plotted against surface temperatures to describe vegetation and moisture stress for the semiarid Brazilian nordeste (northeast). This study complements work by Silva Xavier and Soares Galvão (2005) who used Principal Components Analysis of Multi-angle Imaging SpectroRadiometer (MISR) data from the Amazon to discriminate land cover types. Results of the Dupigny-Giroux (2007) indicated that the NIR/blue ratio at multiple view angles was able to discriminate variations among wetland, aquatic vegetation and the extent of moisture stress. Contributions of the study included an expansion of the recommended combination of the 15-30˚ solar illumination angle and nadir viewing angle for optimally recording benthic features (Dobson et al., 1995); a sensitivity of the NIR/blue ratio to species type and vigour, water/vegetation proportions and moisture gradients across emergent wetlands; and the distinction between aquatic macrophytes and terrestrial vegetation that are often similar individual wavelengths (Valta-Hulkkonen et al., 2003). The study suggested potential uses of the multi-angular ratio including improved mapping of wetlands in humid temperate regions (Bicheron et al., 1997; Barnsley et al., 1997); the avoidance of false change detection due to drought or water draw down (U.S. Fish and Wildlife Service, 2004) and; the improved photointerpretation of evergreen forested wetlands and more xeric ecosystems (Tiner, 2003). In this commentary, the methodology of the Dupigny-Giroux (2007) study was applied to wetlands at two other experimental forests in New England to explore the applicability of the technique across disparate wetland types and microclimates.

2. DATA AND METHODOLOGY 2.1. AirMISR Program TheAirMISR instrument is a pushbroom imager that is mounted on the NASA ER-2 aircraft flying at an altitude of 20km over selected temperate and tropical study areas. It used a single camera on a pivoting gimbal mount to collect data at the nine viewing angles used on the spaceborne MISR instrument. These angles are nadir (An), 26.1˚ fore (Af) and aft (Aa), 45.6˚ fore (Bf) and aft (Ba), 60.0˚ fore (Cf) and aft (Ca) and 70.5˚ fore (Df) and aft (Da). The swath width of the imagery varied from 11km at nadir to 32km for the D cameras. Four spectral bands were centered at 446nm, 558nm, 672nm and 867nm (blue, green, red and nearinfrared) (Diner et al., 1998). The data used in this commentary were collected over three experimental forests in New England in August 2003 (Figure 1). The Bartlett Experimental Forest in north-central New Hampshire and the Harvard Forest in western Maine were both flown on 24 August, with data acquisition over Howland Forest in central Maine on 28 August. All three sites are well instrumented with standard meteorological equipment, biomass and carbon sequestration

Multiangular Imaging of Wetlands in New England

9

measurements to support long term experiments including NASA’s Forest Ecosystem Dynamics Project.

Howland Forest Bartlett Forest Harvard Forest

Figure 1. Locations of the three experimental forests in New England.

Only the data from the north-south runs and A-C cameras over each site were used due to data inhomogeneities. Georectified radiance product (L1B2) data were resampled to a 27.5m grid in the UTM (Universal Transverse Mercator) projection and available online from the Langely Distributed Active Archive Center (DAAC). Actual radiances were computed using the AirMISR tool. An IR minimum check was performed for each viewing angle. Ancillary digital data were acquired from the National Wetlands Inventory, Maine GIS, New Hampshire GIS and Massachusetts GIS.

2.2. Wetlands of the Study Sites The wetlands observed at the three study sites varied by extent, species composition, tidal regimes and permanence of water. The Howland Forest site decreases in elevation from over 120m in the north to about 19m in the south, with palustrine, estuarine, evergreen as well as broad-leaf deciduous and persistent emergent wetlands. To the west, the Bartlett Experimental Forest site located in the White Mountains National Forest ranges in elevation from 59m to 1868 m, an upland area characterized by broadleaf deciduous forests with predominantly palustrine forested broad-leaf deciduous and needle-leaf evergreen wetlands. The Harvard Forest site was lower in elevation (9-548m) and characterized by both palustrine evergreen and freshwater forested shrub wetlands.

10

Lesley-Ann L. Dupigny-Giroux and Eden Furtak-Cole

3. RESULTS AND APPLICABILITY OF MVA TO WETLAND STUDIES The relationship between wetlands and view angles can be analyzed by scatterplots (not shown) of camera pairs on which the 45˚ and best fit lines have been plotted. For the three experimental forests, there was a high degree of correlation between the high camera view angles (An, Af, Aa and Bf). The straight line relationship denotes a moisture gradient from mesic regions (low ratios) to xeric ones (high ones). The relationship is most extensive (with points at both ends of the 1:1 line) for the palustrine, estuarine wetlands of the Howland forest and less so for the other two regions. Differences in species composition and tidal flow regimes were marked across the three forests, influencing the view angles that were most useful for wetland discrimination. For example, at the Howland Forest wetlands, the scatterplot of the Af and Bf forward viewing angles (R2=0.904) was particularly well suited to highlighting moisture stress across forested wetlands, stress that was not observable at nadir (Dupigny-Giroux, 2007). This may be due to the fact that these seasonally flooded wetlands tend to be wetter for shorter durations during the growing season (Tiner, 2003). At the Harvard Forest, both the scatterplots of the high forward view angles (Af and Bf) as well as the nadir (An) and Af pair had the most significant best fit lines (R2 values of 0.946 and 0.957 respectively). The An-Af pairing was marginally better in that it deviated less from the 1:1 line than the Af-Bf pairing. For the Bartlett Forest wetlands, the regression statistics for camera pairs were quite low (R2 80%

(Brooks et al., 2000)

VSSF

24%

(Meuleman et al., 2003)

VSSF

59.5%

(Vymazal, 2007)

VSSF (pilot-scale)

< 47.4 %

(Sleytr et al., 2007)

Phosphorus may also be bound to the substrate of the SSF mainly as a consequence of adsorption and precipitation reactions with calcium, aluminum and iron in the substrate. The capacity of a CWS to remove phosphorus from wastewaters may then be dependent of substrate characteristics (contents in Al, Fe and Ca ions, grain size distributions, pH and specific area). Various artificial media have been tested in order to improve the P-removal in CWS among which are, for example, light expanded clay aggregates (LECA), wollastonite, vermiculite, diatomaceous earth, blast furnace slag and limestone (Brooks et al., 2000; Johansson and Gustafsson, 2000; Brix et al., 2001; Oovel et al., 2007). The removal of phosphorus through adsorption and precipitation can be significant (Vohla et al., 2005) but it is important to realize that these processes are saturable and adsorption decreases over time (Vymazal, 2007). In addition, daily pH variations due to the respiration/photosynthesis cycles may be responsible for cycles of phosphorus precipitation/resolubilization.

4.4. Pathogen Removal Waterborne diseases remain a major hazard in many parts of the world. The important organisms from a public health point of view are the pathogenic bacteria and viruses. Protozoan pathogens and helminth worms are also of particular importance in tropical and subtropical countries (Rivera et al., 1995; Cooper et al., 1996; Vymazal et al., 1998b).

46

Ana Dordio, A. J. Palace Carvalho and Ana Paula Pinto

Wastewater discharges are the major source of contamination by faecal pathogenic microorganisms in rivers and coastal waters posing a risk to public health (Mason, 2002; Sleytr et al., 2007). The treatment of wastewater pathogens in CWS is essentially a two stage process. Most pathogens are particles ranging from very small viruses to the large eggs and cysts of helminths. One of the stages of pathogen treatment is particle removal. This occurs via the same processes as for removal of suspended solids, namely sedimentation, filtration, surface adhesion and aggregation. A series of other processes are important in influencing the viability of pathogens as infectious agents which may occur in a stage either before or after pathogenic particles removal. The major mechanisms in this stage are the hostility of the environmental conditions (temperature, pH, dissolved oxygen concentration, redox potential, salinity, turbity), predation by nematodes, protists and zooplankton and infection by other organisms, antibiosis, exposure to UV radiation and natural die-off (Metcalf and Eddy, 1991; Cooper et al., 1996; Kadlec and Knight, 1996; Ottova et al., 1997; Vymazal et al., 1998a). The efficiency of CWS concerning the removal of microorganisms, especially indicator microorganisms like coliforms and enterococci, is a topic that has been thoroughly investigated (Kadlec and Knight, 1996; Perkins and Hunter, 2000; Langergraber and Haberl, 2001; Hench et al., 2003). In table 8, a small sample of such studies is presented as an illustrative display of the typical efficiencies achieved by several types of CWS. Reported faecal bacteria removal efficiency in CWS is generally high, usually exceeding 85%, and is usually higher for faecal coliforms and somewhat lower for faecal streptococci (Vymazal, 2005b). Is should however be noted that, in spite of high removal efficiencies, if the number of bacteria at the inflow is very large, at the outflow bacteria number may still be too high to meet wastewater quality criteria. Treatment efficiencies depend on several design and operational parameters including the type of CWS, hydraulic regime, type of vegetation, hydraulic residence time, hydraulic and mass loading rate, substrate, and temperature. The efficiency of pathogens treatment does show some variation according to the CWS type and observed efficiencies can in most cases be ranked in the order: hybrid wetlands > SSF wetlands > FWS wetlands (Vymazal, 2005b). These differences may be related to the larger contact area among water, bacteria and substrate, which is much bigger in SSF constructed wetlands compared to FWS (Sleytr et al., 2007) therefore enhancing the process rates of the system (Langergraber and Haberl, 2001). Wetland vegetation plays a crucial role in increasing the efficiency removal of pathogen in CWS. Wetland vegetation improves the trapping efficiency for small particles like viruses by increasing the surface area of biofilms in the flow path. Many species can also release exudates having antimicrobial properties or which can enhance the development in the rhizosphere of populations of bacteria with antibiotic activity (e.g. Pseudomonas).

Wetlands: Water “Living Filters”?

47

Table 8. Removal of pathogens by different types of CWS Indicator microorganism s Faecal coliform (FC)

Faecal streptococci (FS)

Type of CWS

Removal efficiencies

References

Hybrid systems (review)

99.4 %

(Vymazal, 2005b)

FWS

85 – 94 %

(Perkins and Hunter, 2000)

FWS

52 %

(Cameron et al., 2003)

FWS

> 99 %

(García et al., 2008)

FWS (review)

85.6 %

(Vymazal, 2005b)

Hierarchical Mosaic of Aquatic Ecosystems (HMAE®)

99.997 %

(Ansola et al., 2003)

Set of single-family constructed wetland (review)

88 %

(Steer et al., 2002)

SSF

92 %

(Vymazal, 2005c)

SSF

91 %

(Mashauri et al., 2000)

SSF

93 – 98 %

(Karathanasis et al., 2003)

SSF

99 %


SSF

99.999 %

(Soto et al., 1999)

SSF (review)

91.5 %

(Vymazal, 2005b)

SSF

> 99 %

(Hench et al., 2003)

Hybrid systems (review)

97.7 %

(Vymazal, 2005b)

FWS

82 – 90 %


FWS

99 %


FWS (review)

84 %

(Vymazal, 2005b)

SSF

99 %


SSF

> 98 %

(Mantovi et al., 2003)

SSF

93 - 98 %

(Karathanasis et al., 2003)

SSF

83 – 90 %


SSF (review)

92.6 %

(Vymazal, 2005b)

SSF

99.999 %

(Soto et al., 1999)

48


For example, it has been shown that root excretions of species such as Scirpus lacustris and Phragmites australis can diminish the populations of faecal indicators and pathogenic bacteria (Vymazal, 2005b). In addition, the presence of oxygen in the water column (produced by photosynthetic activity of submerged plants and algae in FWS, or released in the rhizosphere through the roots of macrophytes in SSF) creates unfavorable life conditions for enteric bacteria which are either facultative or obligate anaerobic. Effects of the hydraulic retention time are very simple: the longer the wastewater remains in the system, the longer bacteria remains exposed to unfavorable conditions.

4.5. Metals Removal Metals are naturally present in the environment. However, human activities are responsible for a significant increase in their concentration levels up to a point where they begin to pose an environmental and public health problem. Beyond the natural sources, contamination with metals is mainly associated with such activities as soil disturbance, mining, manufacturing, urbanization, burning of fossil fuels and use of manufactured products such as paints, pesticides, sacrificial anodes and anti-foulants. In small doses some metals are, in fact, essential to some biological processes (e.g. copper, chromium, nickel, zinc). For example, at low concentrations, copper is a micronutrient of plants essential to the photosynthetic electron transport system. However, at higher concentrations, it is marketed as an effective herbicide. In addition to concentration, the chemical form is also associated with a greater metal toxicity. For example, the methylated form of mercury is much more toxic than inorganic mercury (Mitra, 1986). Toxic effects by metals are varied and sometimes diffuse and difficult to characterize. In some cases, for very high metal concentrations, toxicity may be acute and ultimately lethal. However, usually toxicity by metals will cause chronic effects resulting from a long-term exposure. Examples of chronic health effects include cancer, disruption of the endocrine system, liver and kidney damage, disorders of the nervous system, damage to the immune systems, and birth defects. Some metals are not easily eliminated by the organisms and, therefore, they have the potential for bioaccumulation and biomagnification. This constitutes one of the major problems with metal contamination, with the potentiation of the metals' chronic toxicity along the food chain. In CWS a variety of processes may provide routes for metal retention in the CWS components and their elimination from the wastewater. The main mechanisms occurring in each of the compartments (solid medium, aqueous medium and vegetation) are illustrated in figure 4. The substrate is generally considered to be a sink for metals anthropogenically introduced in the environment. A major fraction of the elements entering the CWS will rapidly be adsorbed onto the solid phase, where a number of physical and chemical processes will determine the strength of metal retention. A small proportion of the metals can, however, remain dissolved and become available for plant uptake. In CWS another important role in metal removal is played by plants through several processes which include filtration, adsorption, cation exchange, uptake, and root-induced chemical changes in the rhizosphere (Dunbabin and Bowmer, 1992; Chen et al., 2000; Vandecasteele et al., 2005).


49

Figure 4. Metal removal mechanisms in CWS (adapted from Cooper et al. (1996)).

Long-term metal sequestration by plants depends on the rate of uptake, rates of translocation and retention within individual tissue types, and the rate and mode of tissue decomposition (Kadlec and Knight, 1996). Studies report the highest amounts of metals in the roots, while leaf tissue has the second highest concentrations followed by stems and rhizomes (Burke et al., 2000). Microorganisms may also play a relevant role in heavy metal removal. Such contribution may occur through their metabolism with the modification of the oxidation states of metals which in turn may lead to other transformations, such as precipitation, that effectively remove them from the wastewater. A more detailed account follows of the several physical, chemical and biological processes which concur for heavy metal removal in a CWS.

4.5.1. Physical Removal Processes Sedimentation – this has long been recognized as one of the main processes in removal of heavy metals from wastewater in natural and constructed wetlands (Kadlec and Knight, 1996; Hammer, 1997; ITRC, 2003). Sedimentation is a physical process which follows other mechanisms (precipitation/co-precipitation and flocculation) whereby heavy metals aggregate into particles large enough to sink (Walker and Hurl, 2002). In this way heavy metals are removed from wastewater and trapped in the wetland sediments, thus protecting the ultimate receiving water bodies, i.e. aquatic ecosystem (Sheoran and Sheoran, 2006). Efficiency of sedimentation is proportional to the particle settling velocity and the length of wetland. 4.5.2. Chemical Removal Processes In addition to physical removal processes a wide range of chemical processes are involved in the removal of heavy metals in the wetlands:

50


Sorption – among the chemical processes, sorption is one of the most important removal processes in wetlands, which results in the transfer of ions from water to the soil/sediments and a short-term retention or long-term immobilization of several classes of contaminants (Sheoran and Sheoran, 2006). In sediments heavy metals are adsorbed by either cation exchange or chemisorption (Sheoran and Sheoran, 2006). In the former process the metal cation will exchange with other small cations (such as, Na+, K+, NH4+, etc) in their positions in the mineral structures of clays and negatively charged groups of humic acids. Therefore the capacity of soils for retention of metal cations, expressed as cation exchange capacity (CEC) increases with increasing content in clay and organic matter. Chemisorption is a process which involves the formation of chemical bonds with the surface, frequently through complexation/chelation phenomena. The adsorption capacity by cation exchange or non-specific adsorption depends upon the physico-chemical environment of the medium (e.g. pH, the properties of the metals concerned and the concentration and properties of other metals and soluble ligands present) (Debusk et al., 1996; Sheoran and Sheoran, 2006). Therefore, heavy metals speciation may change with time as the sediment conditions change (Groudev et al., 1999; Wiebner et al., 2005; Sheoran and Sheoran, 2006). Much of the heavy metals can be easily adsorbed onto particulate matter in the wetland and subsequently be removed from the water by sedimentation. Lead and copper in general tends to be adsorbed most strongly while zinc, nickel and cadmium are usually held weakly which implies that these metals are likely to be more labile and bio-available (Sheoran and Sheoran, 2006).The adsorption of metals varies with the fluctuation of pH in the outflow water (Machemer and Wildeman, 1992). Precipitated hydroxides may also act as adsorption sites for phytotoxic metals present in the water compartment of the wetland (Wood, 1990). Oxidation and hydrolysis of metals – The states of oxidation of a metal will have a marked influence in its chemical behavior in water. In particular, under some oxidation states a metal may typically hydrolyze to form insoluble oxides or hydroxides whereas in other oxidation states it can be more soluble. Such is the case, for example, of iron, aluminum and manganese which can form insoluble compounds through hydrolysis (sometimes following oxidation processes). This leads to the formation of a variety of oxides, hydroxides and oxyhydroxides (Woulds and Ngwenya, 2004; Sheoran and Sheoran, 2006) Iron removal depends on pH, redox potential and the presence of various anions. In alkaline conditions Fe2+ is a highly soluble cation in water with low content of dissolved oxygen. On the other hand, the form Fe3+ is insoluble except in very acid conditions (pH < 3.5). Manganese removal is the most difficult to be achieved because its oxidation takes place at a pH close to 8 (Stumm and Morgan, 1981). In this case bacteria play an important role in the oxidation of Mn by catalyzing the oxidation of Mn2+ to Mn4+. On the other hand, aluminum removal is purely governed by pH. Aluminum hydroxides will precipitate at pH above 5.0-6.0. Precipitation and co-precipitation – is a major process of heavy metals removal in wetland sediments. The formation of insoluble metal precipitates is one of many factors limiting the bioavailability of heavy metals to many aquatic ecosystems. Precipitation depends on the solubility product (Ks) of the metal involved, pH of the wetland and concentration of metal ions and relevant anions (Brady and Weil, 2002).


51

Co-precipitation is an adsorptive phenomenon also frequent in wetland sediments. Heavy metals commonly co-precipitate with secondary minerals. Copper, nickel, manganese, and other metals are co-precipitated in Fe oxides and cobalt, iron and nickel are co-precipitated in manganese oxides (Stumm and Morgan, 1981). In addition arsenic and zinc were reported to be retained on iron plaques at the surface of plant roots (Otte et al., 1995). Oxiferric hydroxide surfaces are positively charged under acidic pH conditions and negatively charged under alkaline pH conditions. Thus, adsorption and removal of oxyanions such as arsenic, antimony and selenium, through iron co-precipitation, is favored under acidic pH conditions (Brix, 1993). Alkaline conditions are favorable for co-precipitation of cationic metals such as copper, zinc, nickel and cadmium. Thus metals may become associated with iron and manganese oxides as a result of co-precipitation and adsorption phenomena (Stumm and Morgan, 1981). The process is presumed not to be very important in long-term removal and retention of metals because iron and manganese oxides, being redox sensitive, many redissolve following changes in oxygen concentration (Sheoran and Sheoran, 2006). In addition to oxides, hydroxides and oxihydroxides resulting from hydrolysis, other typical insoluble metal compounds include carbonates and sulfides. Conditions exist for precipitation of heavy metal carbonates when the bicarbonate concentration in water is high. Carbonate formation can take place when bacterial production of bicarbonate alkalinity in wetland sediments is substantial (ITRC, 2003). Carbonate precipitation is especially effective for the removal of lead and nickel (Lin, 1995), but Sobolewski (1999) some authors reports significant quantities of copper and manganese carbonates accumulated in some natural wetlands. Wetlands with appropriate substrate may promote the growth of sulfate reducing bacteria in anaerobic conditions. These bacteria will generate hydrogen sulfide which reacts with most heavy metals leading to formation of highly insoluble metal sulfides (Stumm and Morgan, 1981). These provide for long-term metal removal, remaining permanently in wetland sediments as long as they are not re-oxidized (Sobolewski, 1999). Metals such as copper, lead, zinc, cadmium, and arsenic may form highly insoluble sulfides in contact with low concentration of H2S (ITRC, 2003). Field results suggest that upon start up of a constructed wetland, the adsorption of dissolved metals onto organic sites in the substrate material will be an important process but over time sulfide precipitation becomes the dominant process for metal removal (Machemer and Wildeman, 1992).

4.5.3. Biological Removal Processes Biological removal is perhaps the most important pathway for heavy metal removal in the wetlands where plant uptake plays probably the most widely recognized role. While sediments of wetlands form primary sinks for heavy metals (Gray et al., 2000), macrophytes may absorb heavy metals through roots and also shoots. It has been proposed that the processes used by plants are not necessarily the same for different species and for different metals. Submerged rooted plants may have high potential for the metals phytoextraction from sediments as well as water, while floating plants can extract metals only from water (Sriyaraj and Shutes, 2001). Among such processes can be mentioned: sorption by roots (a combination of physical and chemical processes such as chelation, ion exchange and chemical precipitation), and the biological processes including

52


translocation to the aerial part and precipitation induced by root exudates or by microorganisms. The rate of metal removal by plants varies widely, depending on plant growth rate and concentration of the heavy metals in plant tissue. The rate of metal uptake per unit area of the wetland is often much higher for herbaceous plants, or macrophytes such as duckweed (Lemna minor) (Zayed et al., 1998), salix (Stoltz and Greger, 2002), cattail (Typha latifolia) and common reed (Phragmites australis) (Sheoran and Sheoran, 2006). Some of these species can tolerate high concentrations of several metals in their body mass without showing negative effects on the growth (Sheoran and Sheoran, 2006). There are also some examples in the literature indicating that some species may have the ability to accumulate only specific heavy metals, e.g. the Spirodela polyrhiza for Zn (Markert, 1993). Constructed wetlands with well grown Cyperus alternifolius and Vallarsia exaltata have been reported to be an effective tool in phytoremediation of cadmium, copper, manganese, zinc and lead (Cheng et al., 2002). Microorganisms also provide a measurable amount of heavy metal uptake and storage; it is their metabolic processes that play the most significant role in removal of heavy metals (Ledin and Pedersen, 1996; Russell et al., 2003; Hallberg and Johnson, 2005; Sheoran and Sheoran, 2006). Reduction of metals to non-mobile forms by microbial activity in wetlands has been reported by Sobolewski (1999). Metals like chromium and uranium become immobilized when reduced through processes biologically catalyzed by microorganisms (Fude et al., 1994). In table 9 are presented illustrative results found in the literature concerning the removal efficiencies for several heavy metals obtained in different constructed wetlands systems. Table 9. Heavy metals removal efficiencies in different types of CWS Pollutant

Type of CWS

Reduction rate

References

Lead

CWS (small-scale plot)

≥ 90%

(Liu et al., 2007)

HSSF

≥ 70%


FWS

67 %

(Maine et al., 2006)

FWS

48 %


Grenhouse experiment

> 43 %

(Hadad et al., 2007)

HSSF

59 %


HSSF

49 %

(Lesage et al., 2007)

VSSF

≥ 80%

(Lee and Scholz, 2007)

HSSF

79 %


VSSF

≥ 95 %

(Lee and Scholz, 2007)

Iron

FWS

95 %


Aluminum

HSSF

93%

(Lesage et al., 2007)

Nickel

Copper


53

Pollutant

Type of CWS

Reduction rate

References

Chromium

FWS

58 %


FWS

86 %



100%


HSSF

52%



≥ 90%

(Liu et al., 2007)


≥ 35%


HSSF

86 %



≥ 90%

(Liu et al., 2007)

HSSF

24 %


Zinc

Cadmium

Removal efficiencies reported in CWS studies present some variation, from quite low values (~ 25%) to nearly complete removal of some metals. In general, however, the efficiencies are high (> 70%) but these will depend, as usual, on varied factors such as the influent metal loads, the type of vegetation used, the CWS type and on environmental conditions. Obviously, better removals will be achieved when the systems are specifically designed and optimized to solve well-defined metal contamination problems such as mine drainage, where well-known metal accumulator plants will be used preferably, in comparison with systems designed for broader treatment targets where metals are only possibly one among several types of pollutants to remove from wastewaters. As elemental substances, metal cations are naturally non-biodegradable and, for their permanent elimination from the system, the portion of metals removed by plant uptake will require a periodic plant harvesting. For CWS designed to treat high loads of metal inputs, the harvested plant biomass should afterwards be disposed as hazardous waste and receive appropriate treatment.

4.6. Organic Xenobiotics Removal Organic xenobiotics include a large range of synthetic organic compounds, such as phthalates, polychlorinated biphenyls (PCBs), dioxins, polycyclic aromatic hydrocarbons (PAHs), pesticides, sulfonated azo dyes, alkylphenols, bisphenols and pharmaceuticals and personal care products (PPCPs) (Wu, 1999; Mason, 2002; Fent et al., 2006). Several of these substances have been released in increasing amounts in the environment since decades, and, due to the low degradation rate of many of these compounds, a significant increase of their background concentrations has been observed in the different environmental compartments (Tyler et al., 1998; Skakkebaeck et al., 2000). A growing environmental concern has been emerging in recent years, because of the high toxicity and high persistence of most of these substances in the environment and in biological systems. Even though they occur only at very low concentrations in the environment, and their threats to aquatic life and public health are still not completely understood, nevertheless, sub-lethal effects of these compounds over long-term exposure may cause significant damage to aquatic life, particularly considering that some of these compounds may cause significant

54


endocrine disruption, impair reproduction functions of animals or even be carcinogenic, mutagenic or teratogenic (Wu, 1999; Mason, 2002). Furthermore, the high lipophilicity of many of these xenobiotics greatly enhances their biomagnification, thereby posing potential health hazards on predators at higher trophic levels (including human beings). The major ecological concern of xenobiotics is their ability to impair reproductive functions and subsequently threaten survival of the species. In fact, there is growing evidence from laboratory and field studies showing that exposure to trace amounts (µg/L – ng/L level) of certain xenobiotic organic compounds (e.g. halogenated hydrocarbons, PCBs, DDT, TBT) may cause reduced gonad development, disruption of normal metabolism of sex hormones (including gonadotropins), arrest of sperm maturation and block a variety of “oestrogen-like” effects on female reproductive systems in fish, birds, reptiles and mammals. This in turn, may lead to reproductive dysfunction such as delayed sex maturity, reduced fertility and hatch rate, depression in secondary sexual characteristics, alternation of sex behavior and viability of offspring (Wu, 1999). Due to long environmental and biological half lives, recovery from the effects of many xenobiotic compounds is expected to be slow. Indeed, it has been shown that some 15 years were required to remove the negative effects of DDT on reproduction of the white tail eagles in the Baltics, and another 10 years for the population to recover (HELCOM, 1996). Furthermore, despite a decrease in environmental concentrations, the adverse effects may remain in the ecosystem for a much longer period. In the Baltics, DDE decline to 10% of the original levels in 1984, but increased again afterwards, and the egg shells of fish eating birds, which had begun to return to normal, have recently become thinner again. Thus, the downward trend was halted after the ban, and may be due to the recycling of persistent chemicals in sediment (HELCOM, 1996). A variety of sources may be the origin for the presence of organic xenobiotics in water bodies. A number of xenobiotics classes (phthalates, pesticides, PCBs and bisphenols) are industrial products, used worldwide in several applications and are therefore ubiquitous pollutants (Safe, 1994; Stales et al., 1997; Mason, 2002). Other kinds of compounds (dioxins and PAHs) are not commercial products, but are formed as by-products of various industrial and combustion processes; they are transported from atmosphere to soil and water bodies by the atmospheric runoff or deposited on the soil during the dry period and then go through the water cycle by land runoff (Birkett and Lester, 2003). Several studies have shown that a vast range of these xenobiotics are present in the effluents from domestic and industrial conventional wastewater treatment plants (WWTPs) (Birkett and Lester, 2003) which indicates that they resist removal by conventional wastewater treatment processes and may persist in the environment even after going through WWTPs. These are designed to deal with bulk substances that arrive regularly in large quantities (TSS, organic matter and nutrients) and many of these organic xenobiotics show a different chemical behavior for which the conventional processes are not well-suited. The different studies show that the WWTPs removal rates vary according to compound nature, WWTPs overall performance, and environmental conditions. This is also of potential concern about treated wastewater reuse for non industrial applications, such as irrigation of crops and aquaculture, since these pollutants may become a source of contamination of the food chain.


55

4.6.1. Removal Processes in CWS Organic xenobiotics removal by CWS involves several interdependent processes which may be classified as abiotic (physical or chemical) or biotic (microbial or phytological). The primary abiotic and biotic processes that participate in removing organic xenobiotics from contaminated water in a CWS are described in table 10 (Evans and Furlong, 2003; Pilon-Smits, 2005). Table 10. Abiotic and biotic processes involved in xenobiotics removal in CWS Processes

Description

Sorption

Including adsorption and absorption, the chemical processes occurring at the surfaces of plants and substrate that result in a short-term retention or long-term immobilization of xenobiotics The chemical breakdown of organics by the action of water, a process which frequently is pH-dependent Degradation/oxidation of organic xenobiotics by the action of sunlight Modification, which sometimes may be quite substantial, of the xenobiotics due to the action of oxidizing (frequently dissolved oxygen) or reducing agents. Sometimes a redox reaction is a first step leading to removal by other processes, such as precipitation or volatilization. Redox reactions are also frequently brought about by biotic agents such as bacteria, or enzymatically catalyzed Many organic compounds have low water solubility and, especially those exhibiting acid-base properties, may convert into insoluble forms by pH changes Removal of particulate matter and suspended solids Release of some organic xenobiotics, as vapors, which occurs when these compounds have significant vapor pressures

Abiotic

Hydrolysis Photodegradation/ oxidation Oxidation/reduction

Precipitation

Settling and sedimentation Volatilization

Biotic Aerobic/anaerobic biodegradation Phytodegradation

Rhizodegradation Phytovolatilization/ evapotranspiration

Metabolic processes of microorganisms, which play a significant role in organic xenobiotics removal in CWS Breakdown of organic xenobiotic, either internally, having first been taken up by the plants, or externally, using enzymes excreted by them Plants provide root exudates that enhance microbial degradation of some organic xenobiotics Uptake and transpiration of volatile organic xenobiotics through the leaves

The contribution of each process to the overall efficiency of the system will be very dependent on a wide variety of factors relative not only to each CWS component characteristics but also to the properties of the organic xenobiotics, the characteristics of the wastewater and the environmental conditions.

56


4.6.1.1. Factors Affecting Organic Xenobiotics Removal Efficiency in a CWS The degree to which each process will contribute to the overall removal of the organic xenobiotics from contaminated waters in CWS is in turn dependent on the physico-chemical properties of these compounds (e.g., water solubility, sorptive affinity), characteristics of the substrate (e.g., pH, organic matter content, redox status), the plants species, effluent characteristics (e.g., pH, dissolved organic matter, electrolyte composition) as well as other environmental conditions (e.g., temperature, moisture). Some of the most important organic xenobiotics properties that affect their behavior and removal in CWS are its molecular structure, polarity, ionization constant (pKa), water solubility, sorption coefficient (Kd), octanol-water partition coefficient (Kow), volatility and chemical stability. The texture of a soil or substrate is an extremely important characteristic in the sorption process. If the substrate is made up of mostly clay and organic matter a significant amount of sorption will take place. Clay, especially intermixed with organic particles, by far adsorbs the most out of the main types of texture (e.g. silt and sand) because of its small particle size, high surface area and high surface charge. The content in organic matter also has a strong influence in the sorptive properties of the mineral media, mainly due to the presence of humic acids which form a large portion of their composition (Brady and Weil, 2002). These huge organic molecules are characterized by hydrophobic regions suitable for the adsorption of non-polar or weakly polar hydrophobic xenobiotics. However, they also contain numerous chemically active functional groups some of which confer or enrich the ion exchange properties, both cationic and anionic, and surface charge to the substrate which makes it also suitable for the adsorption of polar compounds (Brady and Weil, 2002). The sorption of polar or ionic xenobiotics is also very significant on some silicate clay minerals having substantial surface charge and ion exchange properties. In these materials the pH-changes can affect the sorption processes due to impacts on the pHdependent ion exchange capacities of these variable charge components (humic organic matter included). Some of the wastewater characteristics which will strongly influence how the organic xenobiotics will distribute between the aqueous medium and the matrix include the dissolved organic matter (DOM) content, the wastewater’s pH and its electrolyte composition. The amount and composition of the DOM may have an important influence on the compound’s solubility, and conversely on its sorption by the matrix. DOM may form complexes with the xenobiotic and enhance to its water affinity or it can compete for sorption sites in the substrate’s surface (Muller et al., 2007). In the case of organic xenobiotics with acid-base properties, the pH of the wastewater will determine the form in which they will be present in solution (either ionic or neutral) which, in turn, will affect its water solubility and sorptive affinity for the substrate. The importance of the wastewater’s pH is not restricted to the influence it has on the protonation state of the organic xenobiotics but it will also affect the surface charge of the substrate and, thus, influence the electrostatics-based sorption of ionic forms of the compounds. The presence in the wastewater of certain inorganic ions may introduce additional enhancement or disfavor of the organic xenobiotics sorption onto the matrix. Certain inorganic ions such as phosphate will frequently compete for ion exchange sorption sites in the mineral matrix. On the other hand, complexation phenomena can either contribute, in


57

some cases, to increase the solubility of some xenobiotics or, in other cases, to facilitate binding of the compounds to a mineral surface.

4.6.1.2. Organic Xenobiotics Removal by the Substrate Many phenomena relevant for organic xenobiotics removal occur within the substrate compartment or is influenced by the media characteristics. However, only in sorption processes does the substrate play an actually active role. Latest research on CW systems has been focusing on its ability for removing xenobiotic compounds like which are, in general, not very efficiently removed in the conventional treatment processes of the wastewater treatment plants (Moore et al., 2000; Schulz and Peall, 2001; Hannink et al., 2002; Cheng et al., 2002; Stearman et al., 2003; Haberl et al., 2003; Schulz, 2004; Matamoros et al., 2007). However, the media used have been mainly gravel, sand or local soils (Schulz and Peall, 2001; Stearman et al., 2003; Matamoros et al., 2007) and little importance has been given to the selection of more appropriate materials which can enhance the removal of this type of compounds by sorption. Nevertheless, some materials like LECA, which is being increasingly used as substrate in CWs have already been tested with success for its sorption capacity of some organic xenobiotics like MCPA, clofibric acid, ibuprofen and carbamazepine (Dordio et al., 2007; Dordio et al., 2008a). Clay minerals and organic matter are generally identified as the two most important solid matrix constituents that confer the xenobiotics retention properties to the substrate. However, substantially different sorption mechanisms are responsible for the sorption of non-polar and polar/ionic xenobiotics. 4.6.1.3. Organic xenobiotics removal by the microorganisms Biological removal of organic pollutants in a CWS depends on their bioavailability, which depends on their chemical properties, the CWS physico-chemical characteristics and the environmental conditions (Pilon-Smits, 2005; Collins et al., 2006). Microorganisms usually play an important role in the removal mechanisms of some organic xenobiotics in the CWS The degradation rate of organic xenobiotic and the extent of microbial growth during degradation is highly influenced by the xenobiotic chemical structure (Dua et al., 2002). Structurally simple compounds with high water solubility and low adsorptivity are usually more similar to the naturally occurring substances which are usually used as energy sources by the microorganisms and are easily degraded. In contrast, xenobiotics with chemical structures very different from the naturally occurring compounds are often degraded slowly since microorganisms do not possess suitable degrading genes. In these cases degradation by non-specific enzymes may still occur but at a slow rate by nonspecific reactions which do not support microbial growth (co-metabolism) (Seffernick and Wackett, 2001). Microbial xenobiotic degradation is also strongly influenced by the support medium where the microorganisms are. Temperature, pH, oxygen, presence of toxic substances and nutrients available within the CWS are expected to play a very important role in the removal efficiency. Microorganisms with the ability to degrade a wide variety of compounds, like benzene, phenol, naphthalene, atrazine, nitroaromatics, biphenyls, polychlorinated biphenyls (PCBs) and chlorobenzoates, have been isolated and characterized (Dua et al., 2002). Although

58


simple aromatic compounds are biodegradable by a variety of degradative pathways, their halogenated counterparts are more resistant to bacterial attacks and often necessitate the evolution of novel pathways (Dua et al., 2002). Among the halogenated compounds, the chlorinated compounds are the most extensively studied (Dua et al., 2002). The presence of chlorine atoms on the aromatic nucleus is known to greatly retard the rate of degradation. Most of the information available on the biodegradation of chlorinated compounds is on oxidative degradation, since aerobic culture techniques are relatively simple, compared with anaerobic culture methods. Also, aerobic processes are considered the most efficient and generally applicable (Dua et al., 2002). Aerobic degradation is dependent on the presence of molecular oxygen and is catalyzed by enzymes that have evolved for the catabolism of natural substrates and exhibit low specificities.

4.6.1.4. Organic Xenobiotics Removal by the Plants Plants have an important role in the biotic processes of organics removal in CWS, involving numerous biological processes of which many details still remain to be known or understood. Depending on the pollutant properties, organics may be degraded in the roots zone through the plant’s stimulation of microbial activity or by direct uptake by the plant, followed by degradation, sequestration or volatilization (Evans and Furlong, 2003; PilonSmits, 2005). Plants action in pollutant removal takes different names depending on where the processes predominantly take place. If the organic pollutants are degraded by the microorganisms within the plants’ rhizosphere (and under its influence) the process is called phytostimulation or rhizodegradation. If the plants degrade the organic pollutants directly within their tissues via their own enzymatic activities, then the process is called phytodegradation; other pollutants can leave the plant in a volatile form, and this is called phytovolatilization (Evans and Furlong, 2003; Pilon-Smits, 2005). Many organic pollutants can be readily taken up by plants but, as consequence of many of them being xenobiotic, there are no specific transporters for these compounds in plant membranes. Therefore, organic xenobiotic move into and within plant tissues via diffusion (passive uptake) through cell walls and membranes (Dietz and Schnoor, 2001; Pilon-Smits, 2005). The flux is driven by the water potential gradient created throughout the plant during transpiration, which depends on the plants characteristics and the CWS environmental conditions. Translocation of the compounds is highly dependent on their water solubility and hydrophobicity. There may exist an optimal hydrophobicity that allows the organic compound to bind to the lipid bilayer of the membrane but not too strongly so that transport can still be facilitated. Direct uptake is usually an efficient removal mechanism for moderately hydrophobic organic chemicals (log Kow = 0.5 – 3). These include most chlorinated solvents, BTEX (benzene, toluene, ethylbenzene and xylene), many pesticides, and short-chain aliphatic chemicals. Hydrophobic chemicals (log Kow > 3) are bound so strongly to the surface of roots and soils that they cannot be translocated easily within the plant, and chemicals that are quite water soluble (log Kow < 0.5) are not sufficiently sorbed to roots nor actively transported through plant membranes (Dietz and Schnoor, 2001; Pilon-Smits, 2005). Hydrophobic chemicals (log Kow > 3) are candidates for phytostabilization and/or rhizosphere bioremediation by virtue of their long residence times in the root zone (Dietz and Schnoor, 2001; Pilon-Smits, 2005).


59

Organic compounds which are sorbed to roots can be translocated to other plant tissues and, subsequently, they may be volatilized, they may undergo partial/complete degradation, or they may be transformed to less toxic, especially less phytotoxic, compounds and bound in plant tissues. In general, most organics appear to undergo some degree of transformation in plant cells before being sequestered in vacuoles or bound to insoluble cellular structures such as lignin. Metabolism of pesticides was extensively studied many years ago. More recently, the metabolism of nonagricultural xenobiotics such as trichloroethylene (TCE), TNT, glyceroltrinitrate (GTN), PAHs, PCBs and other chlorinated compounds has also been studied. It was shown that most of these compounds are metabolized but only a few chemicals appear to be fully mineralized. Some plant metabolites of pollutants may be more toxic than the original compounds, making plants less attractive compared with bacteria, which totally degrade organic pollutants. In the rhizosphere take place important physiological and biochemical processes involved in the removal of contaminants which are induced by the interaction between plants, microorganisms and the solid matrix. Plants sustain large microbial populations in the rhizosphere by secreting a variety of products (exudates, mucigels, dead cell material, etc), which is known as rhizodeposition (Stottmeister et al., 2003). Consequently, favorable plant characteristics for phytoremediation are large and dense root systems which also have high levels of degrading enzymes. The chemical composition of the exudates is very diverse and varies with the plant species, but they normally include sugars, organic acids and vitamins. It has been shown that this availability of nutrient in the immediate proximity of the roots makes the microbial population much larger in the rhizosphere than in the bulk soil, and that these larger populations increase the degradation of organic compounds (Yu et al., 2003; Sun et al., 2004). Classes of organic compounds that are more rapidly degraded in the rhizosphere than in bulk soil include polycyclic aromatic hydrocarbons (PAHs), total petroleum hydrocarbons, chlorinated pesticides as well as other chlorinated compounds like polychlorinated biphenyls (PCBs), explosives such as TNT and RDX, organophosphate insecticides, and surfactants. One of the most important characteristics of the macrophyte plants is their ability to supply their root system with oxygen from the atmosphere. The oxygen is released mainly around the root tips promoting detoxification of harmful organic pollutants by oxidation. While these oxidative reactions take place in the immediate surroundings of the plants roots, anaerobic reactions are taking place just a few microns away (Sundaravadivel and Vigneswaran, 2001). The oxidative protective film formed on the root surface protects these sensitive areas from being damaged by toxic wastewater components in the anoxic, usually extremely reduced, rhizosphere (Stottmeister et al., 2003). For the purpose of phytoremediation, plant species have to be selected based on the criteria of their tolerance to the pollutants toxicity and their capacity to lower the pollutants concentration in water through the variety of the processes which have been described above. Aquatic macrophytes seem to be especially resistant to a great variety of these substances in concentrations normally encountered in typical wastewater compositions. In addition, in numerous studies did these plant species exhibit the capability to reduce the concentrations of various xenobiotics in water. For example, the uptake of explosives such as RDX and TNT from the environment has been observed in several macrophytes (Typha spp., Carex spp., Scirpus spp., Myriophyllum aquaticum, Phalaris spp.) in numerous studies (Best et al., 1997; Best et al., 1999). Typha spp. and Phragmites spp. are also known to uptake petroleum

60


hydrocarbons (Haberl et al., 2003) and extensive studies have been conducted on pesticides uptake (Dordio et al., 2008b).

4.6.1.5. CWS Developed for Organic Xenobiotics Removal Many constructed wetlands applications have been consisting of domestic wastewater treatment where the BOD and COD parameters have been used as a cumulative measure of the amounts of organic compounds. However, a more specialized use for the removal of specific organic compounds or classes of compounds has been developing as a growing type of CWS applications. A significant experience already exists with wastewater from the petroleum industry, food processing industry, pesticide contaminated agricultural runoff, landfill leachates, and waters containing surfactants, solvents, and mineral oils. Specific compounds which have received successful treatment in CWS include a range of petroleum hydrocarbons, among which are BTEX and PAHs, organic solvents (in particular the more problematic chlorinated compounds), explosives such as TNT and RDX, PCBs and textile dyes. In table 11, an overview of recently published studies using fully assembled CWS serves to show the vitality and diversity of work that is currently being done in this field. Table 11. Several classes of organic xenobiotics removal in CWS Organic xenobiotic

Type of CW

Vegetation

References

VSSF FWS VSSF HSSF Hybrid system FWS

(Eke and Scholz, 2008) (Haberl et al., 2003) (Bedessem et al., 2007) (Giraud et al., 2001) (Machate et al., 1997) (Gessner et al., 2005)

VSSF (pilotscale)

Phragmites australis Typha latifolia Emergent plants Phragmites australis Typha spp., Scirpus lacustris Typha latifolia, Schoenoplectus tabernaemontani Salix spp. , Scirpus spp., Juncus spp., Phragmites australis

Chlorinated solvents Monochlorobenzene

HSSF

Phragmites australis

TCE and PCE

VSSF

TCE

Wetland microcosms

Common fen plants, wintertolerant sedges and rushes Populus deltoides, Typha latifolia

Petroleum hydrocarbons/ PAHs Benzene BTEX PAHs Petroleum hydrocarbons

Explosives RDX TNT

Pesticides Alachlor

(Haberl et al., 2003)

(Braeckevelt et al., 2007) (Amon et al., 2007) (Bankston et al., 2002)

SSF mesocosms FWS HSSF (pilotscale)

Typha laxmanil Submergent and emergent plants Phragmites australis

(Low et al., 2008) (Best et al., 2001) (Haberl et al., 2003)

SSF


(Matamoros et al., 2007)


61

Organic xenobiotic

Type of CW

Vegetation

References

Atrazine

CW cells CW mesocosms VSSF

(Runes et al., 2003) (Moore et al., 2000) (McKinlay and Kasperek, 1999)

Azinphos-methyl

CW

Endosulfan

Flow-trough wetlands CW

Typha latifolia Primarily Juncus spp. Schoenoplectus lacustris, Typha latifolia, Iris pseudacorus and Phragmites australis Typha capensis, Juncus kraussii, and Cyperus dives Typha capensis, Juncus kraussii and Cyperus dives Sparganium erectum, Phragmites australis, Phalaris arundinacea, Myostis scorpioides, Urtica dioica Phragmites australis

Mecoprop

SSF

(Schulz et al., 2003) (Schulz and Peall, 2001) (Braskerud and Haarstad, 2003)

(Matamoros et al., 2007) (Moore et al., 2006)

Methyl parathion

CW mesocosms

Simazine Others Acid orange 7 (AO7) dye Benzoic acid PCBs Several organic solvents Surfactants

CW cells

Juncus effusus and Leersia oryzoides Scirpus validus

VSSF


(Davies et al., 2005)

SSF FWS SSF Hybrid system

Scirpus validus Typha latifolia Juncus effusus, Carex lurida, Iris pseudocarus, Pondeteric cordata Phragmites australis

SSF

Zantedeschia aethiopica

(Zachritz et al., 1996) (Haberl et al., 2003) (Grove and Stein, 2005) (Bulc and Ojstrsek, 2008) (Belmont and Metcalfe, 2003)

(Stearman et al., 2003)

The case studies for treatment of waters contaminated with special organic compounds display a variety of applications for constructed wetlands. The reported removal efficiencies of organic xenobiotics show in many cases surprisingly good results, with values above 70% being frequently achieved in these studies. However, very little is commonly known about the exact pathways of the xenobiotics removal. Given the diversity of chemical characteristics of these compounds, which despite being classified under a common designation of xenobiotics are in fact formed by widely unrelated families of chemical substances, it is of no surprise that several very different mechanisms are responsible for removal of different xenobiotic. A comprehensive description of xenobiotic removal in CWS is therefore not an easy task to accomplish and these systems are still largely operated as a “black box”. Although much of the design of CWS in the past has been done with little knowledge of the roles played by each component and how its function could be optimized, treating the system primarily as a black box, nowadays the study base that has been accumulating is beginning to be applied. We now can see a much greater variety of plant species, matrix materials and wetlands designs being introduced. The goals of the target contaminants to remove in CWS are also becoming progressively more ambitious.

62


5. NEW TRENDS IN CONSTRUCTED WETLANDS SYSTEMS APPLICATIONS In the past, CWS have been used mainly as wastewater treatment alternatives or complementary to the conventional treatment for domestic wastewaters of small communities. Thus, CWS have been mostly applied in the reduction of TSS, organic matter, excess of nutrients and faecal microorganisms. This is somewhat overlapping with the design goals of WWTPs, but with the benefits of low cost and low maintenance which are characteristic of CWS. More recently, CWS applications to deal with more specific types of pollution (such as that caused by xenobiotics, as described in the previous section) have been meeting a larger interest and have been the subject of an increasing number of studies. CWS have been proving to be efficient and cost-effective solutions for the removal of some pollutants, where the use of conventional wastewater treatment processes does not satisfactorily solve the problem and a resort to more advanced treatments are too costly to become viable alternatives on a larger scale. Among the several types of organic xenobiotics, pharmaceuticals have been recently attracting much attention of the international scientific community and emerging as a new important class of environmental contaminants. Compounds used in human and veterinary medicine are being continuously introduced in the environment, mainly due to improper disposal of unused or expired drugs, and through metabolic excretion. Some of these pharmaceutical residues are discharged directly in the environment without going through appropriate treatment, but even those receiving appropriate disposal in WWTPs in many cases are not effectively removed by the conventional wastewater treatment processes (Halling-Sørensen et al., 1998; Daughton and Ternes, 1999; Heberer, 2002; Fent et al., 2006). In spite of being contaminants of many water bodies for already quite a long time (Garrison et al., 1976; Hignite and Azarnoff, 1977), environmental issues related with pharmaceuticals contamination are only recently becoming subject of more intensive study (Daughton and Ternes, 1999; Petrovic et al., 2003; Fent et al., 2006; Kemper, 2008). The low concentrations of these compounds in the environment, which are typically at trace levels (ng/L to low μg/L), associated to the unavailability, until recently, of suitably sensitive methods of analysis for these low concentration ranges has been the main reason for the late interest on the environmental problems raised by these compounds. Over the latest years, in numerous monitoring studies, residues of lipid regulating drugs, analgesics and antiinflammatory drugs, antibiotics, hormones, chemotherapy agents, antidiabetics, neuroactive compounds and beta-blocking heart drugs have all been detected in wastewaters, surfacewaters and even groundwaters worldwide (Daughton and Ternes, 1999; Fent et al., 2006; Hernando et al., 2006). Despite the trace level concentrations of pharmaceutical residues, such low amounts can sometimes be large enough to induce toxic effects on organisms (Halling-Sørensen et al., 1998; Ferrari et al., 2003; Fent et al., 2006; Crane et al., 2006; Hernando et al., 2006). A major problem is caused by the very nature of some pharmaceuticals since these compounds are designed to have very specific modes of action and biological effects, and many are persistent in the body. Because of their physicochemical and biological properties, when released into environment, it may be possible for them to cause serious impacts on non-target


63

species, e.g., aquatic and terrestrial organisms. Concern has been mostly focused on antibiotics that may cause resistance among bacteria or steroids that can induce estrogenic effects on aquatic species (Halling-Sørensen et al., 1998; Fent et al., 2006). It was a surprise when in 2004, diclofenac, an analgesic and anti-inflammatory drug frequently detected in WWTPs effluents and water courses was found to be the culprit of the nearly extinction of some species of Asian vultures (Oaks et al., 2004). The metabolites and degradation byproducts of pharmaceuticals are also of concern, because many of them have a toxicity which in many cases is similar to or even higher than the parent compounds (Fent et al., 2006). Besides toxicity, the element of persistence is of particular importance when considering the environmental significance of pharmaceuticals. Unlike persistent organic pollutants like pesticides, many pharmaceuticals are not lipophilic, so they do not bioaccumulate in the environment. However, some of those are “pseudo persistent pollutants” due to their continuous introduction in the environment. While not persistent in terms of a long half-life, these chemicals are constantly entering the environment, resulting in long-term exposure for the aquatic ecosystem. Potential synergetic and chronic effects have been mostly overlooked in the past, but recent ecotoxicological studies indicate that pharmaceutical residues pose a major threat especially for the aquatic species due to the continuous life-long exposure to which they are subjected (Fent et al., 2006) . The possible development of antibiotic-resistant bacteria, the genotoxic effects of some drugs, and endocrine disruption by therapeutically administered synthetic and natural hormones have all been discussed (Halling-Sørensen et al., 1998; Daughton and Ternes, 1999; Fent et al., 2006), but very little is known about possible long-term subtle effects on non-target organisms. The foreseeable environmental consequences of high environmental loads of pharmaceuticals points out to the urgent need of finding ways to retain and remove these pollutants before they reach the waterbodies. Optimization of the WWTP processes has been tried by increasing sludge residence times, and some advanced technologies have been evaluated to decrease their discharge into water bodies, e.g. oxidative processes, activated carbon and membrane filtration (Andreozzi et al., 2002; Fent et al., 2006; Esplugas et al., 2007; Kim et al., 2007; Snyder et al., 2007). However, despite the sometimes high removal efficiencies attained, these processes are generally not cost-effective on a large scale (Fent et al., 2006) . Due to the recent emergence of this environmental problem, CWS have not yet been fully evaluated for the removal of pharmaceutical residues. While they are being used efficiently (as shown in the previous section) for removing several other types of organic xenobiotics (Williams, 2002; Hannink et al., 2002; Braskerud and Haarstad, 2003; Haberl et al., 2003), fewer studies exist on their use for pharmaceuticals depuration (Gross et al., 2004; Matamoros et al., 2005; Matamoros and Bayona, 2006; Matamoros et al., 2007; Dordio et al., 2008a; Matamoros et al., 2008b). Pharmaceutical substances span a wide range of chemical behaviors: a diversity of acidbase properties provide for both easily ionizable as well as neutral compounds; while some compounds are very water soluble and hydrophilic, others are significantly hydrophobic/lipophilic; some compounds are very stable to a wide range of redox conditions whereas others are readily oxidizable or reduced. Conversely, the effective mechanisms of pharmaceutical removal in a CWS are also varied. Easily bio-degradable pharmaceuticals are most effectively removed by the biotic components of a CWS. The action of the rhizostimulated microbial populations on one hand,

64


and uptake by plant on the other, are both possible mechanisms (as described in the previous section, only now acting on much lower concentration levels of pollutants which is per se a relevant issue for the system’s efficiency) that can be responsible for pharmaceuticals removal, depending on the compounds properties. An aspect to consider when designing a CWS for treatment of these types of substances and when selecting the type of biotic components is the assessment of the long-term tolerance to possible toxic effects which may be caused by pharmaceutical residues as well as other xenobiotics. Removal of more recalcitrant pharmaceuticals has also been observed in CWS (Gross et al., 2004; Matamoros et al., 2005; Matamoros and Bayona, 2006; Matamoros et al., 2008a; Matamoros et al., 2008b). Sorption by the substrate plays a major role in such cases. However, the characteristics of the substrate are crucial for the effectiveness of this process, and some materials do provide for substantial removal efficiencies (Dordio et al., 2007; Dordio et al., 2008a) whereas other do not (Matamoros et al., 2005; Matamoros et al., 2008a; Matamoros et al., 2008b). Therefore, a careful selection of the type of substrate which displays great affinity for the type of compounds to be removed by the CWS is an essential step in the system’s optimization and the achievement of acceptable efficiencies. Ultimately, the optimization of a CWS for the removal of more specific targets requires a basic knowledge of the processes involved in the pollutants removal and the interactions between the CWS components. New trends in CWS research are moving towards the study of such processes and interactions and focus on the selection and optimization of the components for more specific applications.

CONCLUSION Wetlands have long been known to have water depuration capabilities and, in fact, have been frequently used as wastewater discharge sites. However, in spite of these characteristics, such practice has led over the years to a substantial degradation of many of these ecosystems and in many countries the discharge of wastewaters in natural wetlands is no longer allowed. Furthermore, drainage of wetlands for agricultural purposes has also contributed to the decline in the quality of these regions. For the purpose of the restoration of some of these ecosystems or creation of habitats for wetlands wildlife, the construction of artificial wetlands has been carried out in many former natural wetlands areas. In addition, constructed wetlands have also been developed to imitate the functions provided by natural wetlands, in particular their wastewater treatment capabilities, which can also contribute to the preservation of natural wetlands and neighboring water bodies, and as well perform wastewater treatment in a more controlled and optimized environment to achieve better wastewater treatment efficiencies. Constructed treatment wetlands systems are a biotechnology for wastewater treatment that is becoming increasingly popular as an alternative to conventional treatments or integrated in conventional systems as a secondary or tertiary treatment step. Low cost and low maintenance are some of its most attractive characteristics along with the aesthetic and landscape enhancement qualities, although they do have the disadvantages of requiring substantially more land area occupation and showing a less reproducible behavior than conventional technologies.


65

These systems are now becoming a mature technology for the removal of suspended solids, organic matter and nutrients. Focus is now moving into the removal of more specific and recalcitrant compounds for which the conventional treatment systems are not effective. CWS are nowadays being increasingly used for the cleanup of specific pollutant types such as organic xenobiotics and new challenges have been emerging such as the removal of pharmaceuticals and other micropollutants which present new problems to be solved. Often CWS have been looked at as a “black box” where only influent and effluent pollutants concentrations where measured and where no more in-depth investigations were run. To use CWS as an efficient response to these new challenges this “black-box” approach to CWS operation has to be abandoned and a thorough understanding of the variety of processes involved in pollutants removal in CWS as well as the way the CWS components interact is direly needed. This has been in fact an effort which has been increasingly undertaken in the most recent years in the area of CWS research and development, not only in field studies but also in numerous lab studies as well. Already a considerable base of knowledge exists about the CWS components and the removal processes for which they are responsible. Still, there is plenty of room for further research, as is the case of the most advanced genetic engineering technology applied both to the vegetation as to the microbial components, but also in the more traditional research subjects such as the advantages of polyculture vegetation or use of composite substrates. Additional areas to explore may be how the components may interact to provide synergetic enhancements for pollutants removal. In conclusion, the results obtained in the research currently being conducted in this area are mostly very positive and show the potential of this promising technology even for the type of wastewater contamination that the conventional systems have not been able to cope with. Naturally constructed treatment wetlands systems have their own shortcomings and these will not be the one-size-fits-all solution to every wastewater contamination problem but it can certainly be in some cases an alternative, with advantages, to conventional wastewater treatment (e.g., for the case of single-family or small community municipal wastewaters) and in other cases complimentary to such conventional treatment. Ultimately, the services provided by constructed wetlands may also contribute to a wider recognition of the value of natural wetlands and the role they play in the preservation of water quality in their surroundings.

REFERENCES Amon, J. P.; Agrawal, A.; Shelley, M. L.; Opperman, B. C.; Enright, M. P.; Clemmer, N. D.; Slusser, T.; Lach, J.; Sobolewski, T.; Gruner, W.; Entingh, A. C. Ecol. Eng. 2007, 30, 5166. Andreozzi, R.; Marotta, R.; Pinto, G.; Pollio, A. Water Res. 2002, 36, 2869-2877. Ansola, G.; González, J. M.; Cortijo, R.; de Luis, E. Ecol. Eng. 2003, 21, 43-52. Bankston, J. L.; Sola, D. L.; Komor, A. T.; Dwyer, D. F. Water Res. 2002, 36, 1539-1546. Barr, M. J.; Robinson, H. D. Waste Manage. Res. 1999, 17, 498-504. Bedessem, M. E.; Ferro, A. M.; Hiegel, T. Water Environ. Res. 2007, 79, 581-586. Belmont, M. A.; Metcalfe, C. D. Ecol. Eng. 2003, 21, 233-247.

66


Best, E. P. H.; Miller, J. L.; Larson, S. L. Water Sci. Technol. 2001, 44, 515-521. Best, E. P. H.; Sprecher, S. L.; Larson, S. L.; Fredrickson, H. L.; Bader, D. F. Chemosphere. 1999, 38, 3383-3396. Best, E. P. H.; Zappi, M. E.; Fredrickson, H. L.; Sprecher, S. L.; Larson, S. L.; Ochman, M. Ann. NY Acad. Sci. 1997, 829, 179-194. Birkett, J. W.; Lester, J. N. Endocrine Disruptors in Wastewater and Sludge Treatment Processes; CRC Press: Boca Raton, FL, USA, 2003. Brady, N. C.; Weil, R. R. The Nature and Properties of Soils; Prentice Hall: New Jersey, NJ, USA, 2002. Braeckevelt, M.; Rokadia, H.; Imfeld, G.; Stelzer, N.; Paschke, H.; Kuschk, P.; Kastner, M.; Richnow, H. H.; Weber, S. Environ. Pollut. 2007, 148, 428-437. Braskerud, B. C. Ecol. Eng. 2002, 18, 351-370. Braskerud, B. C.; Haarstad, K. Water Sci. Technol. 2003, 48, 267-274. Brix, H. In Constructed Wetlands for Water Quality Improvement; Moshiri, G. A.; Ed.; Lewis Publishers: Boca Raton, FL, USA, 1993, pp 9-22. Brix, H.; Arias, C. A.; del Bubba, M. Water Sci. Technol. 2001, 44, 47-54. Brodie, G. A.; Hammer, D. A.; Tomijanovich, D. A. In Constructed Wetlands for Wastewater Treatment; Hammer, D. A.; Ed.; Lewis Publishers: Chelsea, MI, USA, 1989. Brooks, A. S.; Rozenwald, M. N.; Geohring, L. D.; Lion, L. W.; Steenhuis, T. S. Ecol. Eng. 2000, 15, 121-132. Bulc, T. G.; Ojstrsek, A. J. Hazard. Mater. 2008, In Press, Corrected Proof. Burke, D. J.; Weis, J. S.; Weis, P. Estuar. Coast. Shelf Sci. 2000, 51, 153-159. Cameron, K.; Madramootoo, C.; Crolla, A.; Kinsley, C. Water Res. 2003, 37, 2803-2812. Carleton, J. N.; Grizzard, T. J.; Godrej, A. N.; Post, H. E. Water Res. 2001, 35, 1552-1562. Carty, A.; Scholz, M.; Heal, K.; Gouriveau, F.; Mustafa, A. Bioresour. Technol. In Press, Corrected Proof. Chen, H. M.; Zheng, C. R.; Tu, C.; Shen, Z. G. Chemosphere. 2000, 41, 229-234. Chen, T. Y.; Kao, C. M.; Yeh, T. Y.; Chien, H. Y.; Chao, A. C. Chemosphere. 2006, 64, 497502. Cheng, S. P.; Grosse, W.; Karrenbrock, F.; Thoennessen, M. Ecol. Eng. 2002, 18, 317-325. Collins, C.; Fryer, M.; Grosso, A. Environ. Sci. Technol. 2006, 40, 45-52. Cooper, P. J. Chart. Inst. Water Environ. Manage. 2001, 15, 79-85. Cooper, P. F.; Job, G. D.; Green, M. B.; Shutes, R. B. E. Reed Beds and Constructed Wetlands for Wastewater Treatment; WRc Publications: Medmenham, Marlow, UK, 1996. Coveney, M. F.; Stites, D. L.; Lowe, E. F.; Battoe, L. E.; Conrow, R. Ecol. Eng. 2002, 19, 141-159. Cowardin, L. M.; Golet, F. C. Vegetatio. 1995, 118, 139-152. Craft, C. B.; Richardson, C. J. Biogeochemistry. 1993, 22, 133-156. Crane, M.; Watts, C.; Boucard, T. Sci. Total Environ. 2006, 367, 23-41. Cronk, J. K. Agric. Ecosyst. Environ. 1996, 58, 97-114. D'Angelo, E. M.; Reddy, K. R. J. Environ. Qual. 1994, 23, 937-943. Daughton, C. G.; Ternes, T. A. Environ. Health Perspect. 1999, 107, 907-938. Davies, L. C.; Carias, C. C.; Novais, J. M.; Martins-Dias, S. Ecol. Eng. 2005, 25, 594-605. Debusk, T. A.; Laughlin, R. B.; Schwartz, L. N. Water Res. 1996, 30, 2707-2716. Dietz, A. C.; Schnoor, J. L. Environ. Health Perspect. 2001, 109, 163-168.


67

Dordio, A. V.; Candeias, A. J. E.; Pinto, A. P.; Costa, C. T.; Carvalho, A. J. P. Ecol. Eng. 2008a, In Press, Corrected Proof. Dordio, A. V.; Carvalho, A. J. P.; Pinto, A. P.; Costa, C. T. In Pesticide Research Trends; Tennefy, A. B.; Ed.; Nova Science Publishers: Hauppauge, NY, USA, 2008b. Dordio, A. V.; Teimão, J.; Ramalho, I.; Carvalho, A. J. P.; Candeias, A. J. E. Sci. Total Environ. 2007, 380, 237-246. Dua, M.; Singh, A.; Sethunathan, N.; Johri, A. Appl. Microbiol. Biotechnol. 2002, 59, 143152. Dunbabin, J. S.; Bowmer, K. H. Sci. Total Environ. 1992, 111, 151-168. Eke, P. E.; Scholz, M. J. Chem. Technol. Biotechnol. 2008, 83, 55-63. Environmental Laboratory. Corps of Engineers Wetland Delineation Manual; Technical Report Y-87-1; US Army Engineer Waterways Experiment Station: Vicksburg, MS, USA, 1987. Esplugas, S.; Bila, D. M.; Krause, L. G.; Dezotti, M. J. Hazard. Mater. 2007, 149, 631-642. Evans, G. M.; Furlong, J. C. Environmental Biotechnology: Theory and Application; Wiley: Chichester, UK, 2003. Fent, K.; Weston, A. A.; Caminada, D. Aquat. Toxicol. 2006, 76, 122-159. Ferrari, B.; Paxéus, N.; Lo Giudice, R.; Pollio, A.; Garric, J. Ecotox. Environ. Safe. 2003, 55, 359-370. Fude, L.; Harris, B.; Urrutia, M. M.; Beveridge, T. J. Appl. Environ. Microbiol. 1994, 60, 1525-1531. García, M.; Soto, F.; González, J. M.; Bécares, E. Ecol. Eng. 2008, 32, 238-243. Garrison, A. W.; Pope, J. D.; Allen, F. R. In Identification and Analysis of Organic Pollutants in Water; Keith, L. H.; Ed.; Ann Arbor Science Publishers: Ann Arbor, MI, USA, 1976, pp 517-556. Gessner, T. P.; Kadlec, R. H.; Reaves, R. P. Ecol. Eng. 2005, 25, 457-469. Giraud, F.; Guiraud, P.; Kadri, M.; Blake, G.; Steiman, R. Water Res. 2001, 35, 4126-4136. Gómez Cerezo, R.; Suárez, M. L.; Vidal-Abarca, M. R. Ecol. Eng. 2001, 16, 501-517. Gray, S.; Kinross, J.; Read, P.; Marland, A. Water Res. 2000, 34, 2183-2190. Gross, B.; Montgomery-Brown, J.; Naumann, A.; Reinhard, M. Environ. Toxicol. Chem. 2004, 23, 2074-2083. Groudev, S. N.; Bratcova, S. G.; Komnitsas, K. Miner. Eng. 1999, 12, 261-270. Grove, J. K.; Stein, O. R. Water Res. 2005, 39, 4040-4050. Haberl, R.; Grego, S.; Langergraber, G.; Kadlec, R. H.; Cicalini, A. R.; Martins-Dias, S.; Novais, J. M.; Aubert, S.; Gerth, A.; Thomas, H.; Hebner, A. J. Soil Sediment. 2003, 3, 109-124. Hadad, H. R.; Maine, M. A.; Bonetto, C. A. Chemosphere. 2006, 63, 1744-1753. Hadad, H. R.; Maine, M. A.; Natale, G. S.; Bonetto, C. Ecol. Eng. 2007, 31, 122-131. Hallberg, K. B.; Johnson, D. B. Sci. Total Environ. 2005, 338, 115-124. Halling-Sørensen, B.; Nielsen, S. N.; Lanzky, P. F.; Ingerslev, F.; Lützhøft, H. C. H.; Jørgensen, S. E. Chemosphere. 1998, 36, 357-394. Halverson, N. V. Review of Constructed Subsurface Flow vs. Surface Flow Wetlands; WSRC-TR-2004-00509; Westinghouse Savannah River Company: Aiken, SC, USA, 2004. Hammer, D. A. Creating Freshwater Wetlands; CRC Press: Boca Raton, FL,USA, 1997.

68


Hammer, D. A.; Bastian, R. K. In Constructed Wetlands for Wastewater Treatment; Hammer, D. A.; Ed.; Lewis Publishers: Chelsea, MI, USA, 1989, pp 5-19. Hammer, D. A.; Pullin, B. P.; McCaskey, T. A.; Eason, J.; Payne, V. W. E. In Constructed Wetlands for Wastewater Treatment; Hammer, D. A.; Ed.; Lewis Publishers: Chelsea, MI, USA, 1989. Hannink, N. K.; Rosser, S. J.; Bruce, N. C. Crit. Rev. Plant Sci. 2002, 21, 511-538. Heberer, T. Toxicol. Lett. 2002, 131, 5-17. HELCOM. Baltic Sea Environment Proceedings No. 64B. Third Periodic Assessment on the State of the Marine Environment of the Baltic Sea, 1989-1993; Background Document.; Helsinki Commission, Baltic Marine Environment Protection Commission: Helsinki, Finland, 1996. Hench, K. R.; Bissonnette, G. K.; Sexstone, A. J.; Coleman, J. G.; Garbutt, K.; Skousen, J. G. Water Res. 2003, 37, 921-927. Hernando, M. D.; Mezcua, M.; Fernandez-Alba, A. R.; Barceló, D. Talanta. 2006, 69, 334342. Hignite, C.; Azarnoff, D. L. Life Sci. 1977, 20, 337-341. IETC-UNEP. Planning and Management of Lakes and Reservoirs: An Integrated Approach to Eutrophication; 1999. ITRC. Technical and regulatory guidance document for constructed treatment wetlands; The Interstate Technology and Regulatory Council Wetlands Team: New Jersey, NJ, USA, 2003. Johansson, L.; Gustafsson, J. P. Water Res. 2000, 34, 259-265. Kadlec, H. R.; Knight, R. L. Treatment Wetlands; Lewis Publishers: Boca Raton, FL, USA, 1996. Karathanasis, A. D.; Potter, C. L.; Coyne, M. S. Ecol. Eng. 2003, 20, 157-169. Kemper, N. Ecol. Indic. 2008, 8, 1-13. Kim, S. D.; Cho, J.; Kim, I. S.; Vanderford, B. J.; Snyder, S. A. Water Res. 2007, 41, 10131021. Kivaisi, A. K. Ecol. Eng. 2001, 16, 545-560. Kleeberg, A.; Schlungbaum, G. Hydrobiologia. 1993, 253, 263-274. Knight, R. L.; Payne, V. W. E.; Borer, R. E.; Clarke, R. A.; Pries, J. H. Ecol. Eng. 2000, 15, 41-55. Kohler, E. A.; Poole, V. L.; Reicher, Z. J.; Turco, R. F. Ecol. Eng. 2004, 23, 285-298. Kuehn, E.; Moore, J. A. Water Sci. Technol. 1995, 32, 241-250. Langergraber, G.; Haberl, R. Minerva Biotechnol. 2001, 13, 123-134. Ledin, M.; Pedersen, K. Earth-Sci. Rev. 1996, 41, 67-108. Lee, B. h.; Scholz, M. Ecol. Eng. 2007, 29, 87-95. Lesage, E.; Rousseau, D. P. L.; Meers, E.; Tack, F. M. G.; De Pauw, N. Sci. Total Environ. 2007, 380, 102-115. Lin, L. Y. In Encyclopedia of environmental biology; Nierenberg, W. A.; Ed.; Academic Press: San Diego, CA, USA, 1995; Vol. 3, pp 479-484. Liu, J.; Dong, Y.; Xu, H.; Wang, D.; Xu, J. J. Hazard. Mater. 2007, 147, 947-953. Livingston, E. H. In Constructed Wetlands for Wastewater Treatment; Hammer, D. A.; Ed.; Lewis Publishers: Chelsea, MI, USA, 1989, pp 253-264. Low, D.; Tan, K.; Anderson, T.; Cobb, G. P.; Liu, J.; Jackson, W. A. Ecol. Eng. 2008, 32, 7280.


69

Machate, T.; Noll, H.; Behrens, H.; Kettrup, A. Water Res. 1997, 31, 554-560. Machemer, S. D.; Wildeman, T. R. J. Contam. Hydrol. 1992, 9, 115-131. Maine, M. A.; Suñe, N.; Hadad, H.; Sánchez, G.; Bonetto, C. Ecol. Eng. 2006, 26, 341-347. Maine, M. A.; Suñe, N.; Hadad, H.; Sánchez, G.; Bonetto, C. Chemosphere. 2007, 68, 11051113. Mantovi, P.; Marmiroli, M.; Maestri, E.; Tagliavini, S.; Piccinini, S.; Marmiroli, N. Bioresour. Technol. 2003, 88, 85-94. Markert, B. Plants as biomonitors: indicators for heavy metals in the terrestrial environment; VCH: Weinheim, Germany, 1993. Mashauri, D. A.; Mulungu, D. M. M.; Abdulhussein, B. S. Water Res. 2000, 34, 1135-1144. Mason, C. Biology of freshwater pollution; Pearson Education: Harlow, UK, 2002. Matamoros, V.; Bayona, J. M. Environ. Sci. Technol. 2006, 40, 5811-5816. Matamoros, V.; García, J.; Bayona, J. M. Environ. Sci. Technol. 2005, 39, 5449-5454. Matamoros, V.; Puigagut, J.; Garcia, J.; Bayona, J. M. Chemosphere. 2007, 69, 1374-1380. Matamoros, V.; Caselles-Osorio, A.; Garcia, J.; Bayona, J. M. Sci. Total Environ. 2008a, 394, 171-176. Matamoros, V.; García, J.; Bayona, J. M. Water Res. 2008b, 42, 653-660. McKinlay, R. G.; Kasperek, K. Water Res. 1999, 33, 505-511. Merlin, G.; Pajean, J. L.; Lissolo, T. Hydrobiologia. 2002, 469, 87-98. Metcalf; Eddy Wastewater engineering: treatment, disposal and reuse; McGraw Hill: New York, NY, USA, 1991. Meuleman, A. F. M.; van Logtestijn, R.; Rijs, G. B. J.; Verhoeven, J. T. A. Ecol. Eng. 2003, 20, 31-44. Mitra, S. Mercury in the ecosystem; Trans Tech Publications: Aedermannsdorf, Switzerland, 1986. Mitsch, W. J.; Gosselink J.G. Wetlands; John Wiley and Sons: New York, NY, USA, 2000. Moore, M. T.; Bennett, E. R.; Cooper, C. M.; Smith, S.; Farris, J. L.; Drouillard, K. G.; Schulz, R. Environ. Pollut. 2006, 142, 288-294. Moore, M. T.; Rodgers, J. H.; Cooper, C. M.; Smith, S. Environ. Pollut. 2000, 110, 393-399. Muller, K.; Magesan, G. N.; Bolan, N. S. Agric. Ecosyst. Environ. 2007, 120, 93-116. National Research Council Wetlands: Characteristics and Boundaries; National Academy Press: Washington, DC, USA, 1995. Neralla, S.; Weaver, R. W.; Lesikar, B. J.; Persyn, R. A. Bioresour. Technol. 2000, 75, 19-25. Nivala, J.; Hoos, M. B.; Cross, C.; Wallace, S.; Parkin, G. Sci. Total Environ. 2007, 380, 1927. Oaks, J. L.; Gilbert, M.; Virani, M. Z.; Watson, R. T.; Meteyer, C. U.; Rideout, B. A.; Shivaprasad, H. L.; Ahmed, S.; Chaudhry, M. J. I.; Arshad, M.; Mahmood, S.; Ali, A.; Khan, A. A. Nature. 2004, 427, 630-633. Oovel, M.; Tooming, A.; Mauring, T.; Mander, U. Ecol. Eng. 2007, 29, 17-26. Otte, M. L.; Kearns, C. C.; Doyle, M. O. Bull. Environ. Contam. Toxicol. 1995, 55, 154-161. Ottova, V.; Balcarova, J.; Vymazal, J. Water Sci. Technol. 1997, 35, 117-123. Perkins, J.; Hunter, C. Water Res. 2000, 34, 1941-1947. Petrovic, M.; Gonzalez, S.; Barceló, D. Trac-Trends Anal. Chem. 2003, 22, 685-696. Pilon-Smits, E. Annu. Rev. Plant Biol. 2005, 56, 15-39. Reddy, K. R.; D'Angelo, E. M. Water Sci. Technol. 1997, 35, 1-10. Richardson, C. J.; Marshall, P. E. Ecol. Monogr. 1986, 56, 279-302.

70


Rivera, F.; Warren, A.; Ramirez, E.; Decamp, O.; Bonilla, P.; Gallegos, E.; Calderon, A.; Sanchez, J. T. Water Sci. Technol. 1995, 32, 211-218. Runes, H. B.; Jenkins, J. J.; Moore, J. A.; Bottomley, P. J.; Wilson, B. D. Water Res. 2003, 37, 539-550. Russell, R. A.; Holden, P. J.; Wilde, K. L.; Neilan, B. A. Hydrometallurgy. 2003, 71, 227234. Safe, S. H. Crit. Rev. Toxicol. 1994, 24, 87-149. Scholes, L.; Shutes, R. B. E.; Revitt, D. M.; Forshaw, M.; Purchase, D. Sci. Total Environ. 1998, 214, 211-219. Schulz, R. J. Environ. Qual. 2004, 33, 419-448. Schulz, R.; Hahn, C.; Bennett, E. R.; Dabrowski, J. M.; Thiere, G.; Peall, S. K. C. Environ. Sci. Technol. 2003, 37, 2139-2144. Schulz, R.; Peall, S. K. C. Environ. Sci. Technol. 2001, 35, 422-426. Seffernick, J. L.; Wackett, L. P. Biochemistry. 2001, 40, 12747-12753. Seidel, K. In Ökologie, Morphologie und Entwicklung, ihre Stellung bei den Volkern und ihre wirtschaftliche Bedeutung; Sweizerbart'sche Verlgsbuchhandlung: Stuttgart, Germany, 1955, pp 37-52. Sheoran, A. S.; Sheoran, V. Miner. Eng. 2006, 19, 105-116. Skakkebaeck, N. E.; Leffers, H.; Rajpert-De Meyts, E.; Carlsen, E.; Grigor, K. M. Trends Endocrinol. Metab. 2000, 11, 291-293. Sleytr, K.; Tietz, A.; Langergraber, G.; Haberl, R. Sci. Total Environ. 2007, 380, 173-180. Snyder, S. A.; Adham, S.; Redding, A. M.; Cannon, F. S.; DeCarolis, J.; Oppenheimer, J.; Wert, E. C.; Yoon, Y. Desalination. 2007, 202, 156-181. Sobolewski, A. Int. J. Phytoremediat. 1999, 1, 19-51. Solano, M. L.; Soriano, P.; Ciria, M. P. Biosyst. Eng. 2004, 87, 109-118. Soto, F.; Garcia, M.; de Luis, E.; Bécares, E. Water Sci. Technol. 1999, 40, 241-247. Sriyaraj, K.; Shutes, R. B. E. Environ. Int. 2001, 26, 433-439. Stales, C. A.; Peterson, D. R.; Parkerton, T. F.; Adams, W. J. Chemosphere. 1997, 35, 667749. Stearman, G. K.; George, D. B.; Carlson, K.; Lansford, S. J. Environ. Qual. 2003, 32, 15481556. Steer, D.; Fraser, L.; Boddy, J.; Seibert, B. Ecol. Eng. 2002, 18, 429-440. Stottmeister, U.; Wiessner, A.; Kuschk, P.; Kappelmeyer, U.; Kastner, M.; Bederski, O.; Muller, R. A.; Moormann, H. Biotechnol. Adv. 2003, 22, 93-117. Stowell, R.; Ludwig, R.; Colt, J.; Tchobanoglous, G. J. Environ. Eng. -ASCE. 1981, 107, 919940. Stumm, W.; Morgan, J. J. Aquatic Chemistry: An Introduction Emphasizing Chemical Equilibra In Natural Waters; John Wiley and Sons: New York, NY, USA, 1981. Sun, H.; Xu, J.; Yang, S.; Liu, G.; Dai, S. Chemosphere. 2004, 54, 569-574. Sundaravadivel, M.; Vigneswaran, S. Crit. Rev. Environ. Sci. Technol. 2001, 31, 351-409. Tanner, C. C.; Clayton, J. S.; Upsdell, M. P. Water Res. 1995, 29, 17-26. Tanner, C. C.; Kadlec, R. H.; Gibbs, M. M.; Sukias, J. P. S.; Nguyen, M. L. Ecol. Eng. 2002, 18, 499-520. Tyler, C. R.; Jobling, S.; Sumpter, J. P. Crit. Rev. Toxicol. 1998, 28, 319-361.


71

UNESCO. Convention on Wetlands of International Importance especially as Waterfowl Habitat; Ramsar, Iran, 1994. Available online at: http://www.ramsar.org/key_ conv_e.htm. USEPA. Constructed wetlands for wastewater treatment and wild life habitat: 17 Case Studies; EPA 832-R-93-005; Office of Research and Development: Cincinnati, OH, USA, 1993. USEPA. Constructed Wetlands Treatment of Municipal Wastewaters; EPA/625/R-99/010; Office of Research and Development: Cincinnati, OH, USA, 2000. USEPA; USDA-NRCS. A Handbook of Constructed Wetlands. Volume 1: General Considerations; USEPA Region III with USDA-NRCS: Washington, DC, USA, 1995. Vandecasteele, B.; Meers, E.; Vervaeke, P.; De Vos, B.; Quataert, P.; Tack, F. M. G. Chemosphere. 2005, 58, 995-1002. Vohla, C.; Poldvere, E.; Noorvee, A.; Kuusemets, V.; Mander, U. J. Environ. Sci. Health Part A-Toxic/Hazard. Subst. Environ. Eng. 2005, 40, 1251-1264. Vymazal, J. Water Sci. Technol. 1999, 40, 133-138. Vymazal, J. Ecol. Eng. 2005a, 25, 475-477. Vymazal, J. J. Environ. Sci. Health Part A-Toxic/Hazard. Subst. Environ. Eng. 2005b, 40, 1355-1367. Vymazal, J. Sci. Total Environ. 2007, 380, 48-65. Vymazal, J. In Constructed wetlands for wastewater treatment in Europe; Vymazal, J.; Brix, H.; Cooper, P. F.; Green, M. B.; Haberl, R.; Eds.; Backhuys Publishers: Leiden, The Netherlands, 1998, pp 1-15. Vymazal, J. Ecol. Eng. 2005c, 25, 478-490. Vymazal, J.; Brix, H.; Cooper, P. F.; Green, M. B.; Haberl, R. Constructed wetlands for wastewater treatment in Europe; Backhuys Publishers: Leiden, The Netherlands, 1998a. Vymazal, J.; Brix, H.; Cooper, P. F.; Haberl, R.; Perfler, R.; Laber, J. In Constructed wetlands for wastewater treatment in Europe; Vymazal, J.; Brix, H.; Cooper, P. F.; Green, M. B.; Haberl, R.; Eds.; Backhuys Publishers: Leiden, The Netherlands, 1998b, pp 17-66. Walker, D. J.; Hurl, S. Ecol. Eng. 2002, 18, 407-414. Wiebner, A.; Kappelmeyer, U.; Kuschk, P.; Kastner, M. Water Res. 2005, 39, 248-256. Williams, J. B. Crit. Rev. Plant Sci. 2002, 21, 607-635. Wood, A. In Constructed wetlands in water pollution control; Cooper, P. F.; Findlater, B. C.; Eds.; Pergamon Press: Oxford, UK, 1990. Woulds, C.; Ngwenya, B. T. Appl. Geochem. 2004, 19, 1773-1783. Wu, R. S. S. Mar. Pollut. Bull. 1999, 39, 11-22. Yu, Y. L.; Chen, Y. X.; Luo, Y. M.; Pan, X. D.; He, Y. F.; Wong, M. H. Chemosphere. 2003, 50, 771-774. Zachritz, W. H.; Lundie, L. L.; Wang, H. Ecol. Eng. 1996, 7, 105-116.


Chapter 2

REMOTE SENSING DATA FOR REGIONAL WETLAND MAPPING IN THE UNITED STATES: TRENDS AND FUTURE PROSPECTS Megan W. Lang and Greg W. McCarty USDA-ARS, Remote Sensing and Hydrology Laboratory, Beltsville Agricultural Research Center, Bldg 007, Rm 104, 10300 Baltimore Ave., Beltsville, MD 20705

ABSTRACT Historically, the biologic, aesthetic, and economic values of wetlands were largely unappreciated. Wetlands within the United States have been and are continuing to disappear at a rapid rate. Efforts are being made to conserve remaining wetlands and many regulatory policies have been adopted in support of this goal. To regulate the loss, preservation, and/or restoration of wetlands and to judge the effectiveness of these regulatory efforts in preserving associated ecosystem services, wetlands must be routinely monitored. Wetland mapping is an essential part of this monitoring program and much effort has been made by the US state and federal governments, as well as other organizations, to provide quality map products. Wetland maps can serve a variety of purposes including regulation, natural resource management, and input for models that quantify water quality and quantity as well as the provision of wetland ecosystem services at the watershed scale. Wetland hydrology is the most important abiotic factor controlling ecosystem function and extent, and it should therefore be a vital part of any wetland mapping or monitoring program. New approaches are needed to not only map wetlands, but also to monitor wetland hydrology as it varies in response to weather, vegetation phenology, surrounding landuse change, and other anthropogenic forces including climate change. Recently developed remote sensing technologies and techniques have the potential to improve the detail and reliability of wetland maps and the ability to monitor important parameters such as hydrology. Various types of remotely sensed data (e.g., aerial photographs, multispectral, hyperspectral, passive microwave, radar, and lidar) have different capabilities with specific advantages and disadvantages for wetland mapping at the regional scale. Although aerial photographs were traditionally used to map wetlands and infer hydrology, fine-resolution optical images are now

74

Megan W. Lang and Greg W. McCarty available more frequently as commercial agencies increase satellite coverage (e.g., Quickbird and IKONOS). However, optical data, such as aerial photographs and multispectral satellite images have limitations, including their inability to detect hydrology below dense vegetative canopies and their limited ability to detect variations in hydrology (i.e., inundation and soil moisture). The restrictions of optical data are increasingly being compensated for with the use of new technologies, including synthetic aperture radar, lidar, and geospatial modeling. The availability of these new data sources is increasing rapidly. For example, many states in the US are now collecting synoptic state-wide coverages of lidar data. The sources, strengths, and limitations of different types of remotely sensed data are reviewed in this paper, as well as the importance of temporal and spatial resolution necessary for regional scale wetland mapping efforts. The potential of multi-temporal, multi-sensor approaches that capitalize on geospatial modeling are emphasized for meeting current wetland mapping challenges.

1. INTRODUCTION Until recently, the biologic, aesthetic, and economic values of wetlands were largely unappreciated. United States federal legislation, such as the Swamp Lands Act of 1850 and farming innovations, encouraged the draining of wetlands. In the 1930’s and 1940’s, the US government provided free engineering services to farmers who wanted to drain their lands (Dahl and Johnson 1991). Much ecosystem damage has been done and the conterminous United States has lost over half of its wetlands (Dahl and Johnson 1991). Some states have lost more wetlands than others. For example, California has lost over 91% of its wetlands whereas Maryland has lost over 64% (Dahl and Johnson 1991; Tiner 2005). In the last half of the 20th century, public recognition of the value of wetlands along with concern over accelerating rates of wetland loss led to a change in society’s perception of wetlands. Interest in wetland conservation has increased and new governmental policies (e.g., the Highly Erodible Land Conservation and Wetland Conservation Compliance (Swampbuster) provisions that were introduced in the 1985 US Farm Bill) have removed most of the wetland drainage incentives for agricultural lands. Other US legislative acts, such as the Clean Water Act (which grew out of the Federal Water Pollution Control Act of 1948 as amended in 1972 and 1977), are now used to protect wetlands. Wetland protection is further supported by the US federal “no net wetland loss” policy (Federal Geographic Data Committee 1994). Efforts are being made to conserve remaining wetlands and many policies have been adopted in support of this goal. Accurate and up to date wetland maps assist in the proper implementation of wetland regulations. For the United States to effectively manage its remaining wetlands, their abundance, distribution, boundaries, and quality must be better understood. The way in which society interacts with wetlands is largely dependent on what value they assign to these natural resources. This value is subjective and is often measured by the function that the resource serves. Therefore, a better understanding of wetland ecosystem functioning within the landscape is necessary in order for society to place a value on wetlands (National Research Council 1995). As natural resource management becomes more holistic and moves towards ecosystem management, the synoptic view that remotely sensed data provides will become increasingly important (Finlayson and van der Valk 1995; Klemas 2001). Remote observation of wetlands is particularly necessary because they are often

Remote Sensing Data for Regional Wetland Mapping in the United States

75

difficult to access on the ground, and on-site mapping at the landscape scale is usually cost prohibitive (Harvey and Hill 2001; Rundquist et al. 2001; Baker et al. 2006; Silva et al. 2008). Remotely sensed imagery has the potential to add to our understanding of wetlands within the wider landscape setting and to better ensure their preservation via an increased understanding, more informed management practices, and a heightened appreciation of this unique resource. Remotely sensed data can be used to create not only maps of wetland presence, but also estimates of wetland condition and function (Rosenqvist et al. 2007). Significant effort has been made by scientists and managers to provide quality map products and recently developed remote sensing technologies have the potential to further improve their detail and reliability. The diversity of remotely sensed data and the techniques available to process these data have increased rapidly since the 1970’s, when the US first began to systemically map national wetland resources. Still, the dynamic nature of these ecosystems (e.g., ephemeral hydrology), their diversity (e.g., variations in plant structure and phenology), and the often small proportion of the landscape that they occupy challenge existing sensors. Recent advances in the quality and availability of radar, lidar, and other types of data, as well as the introduction of new processing methods hold great potential for the furthering of regional wetland mapping and monitoring efforts. These different geospatial datasets provide complementary information about wetland presence and function. This paper reviews the advantages and disadvantages of applying different types of remotely sensed data, including aerial photographs, multi-spectral, hyperspectral, passive microwave, radar, and lidar data, to the mapping and monitoring of wetlands.

2. CURRENT US REGIONAL WETLAND MAPPING PROGRAMS Multiple state and federal agencies have produced regional wetland maps over the last thirty-five years and these maps have developed rapidly. One of the most common wetland mapping methods uses both remotely sensed and field data. Natural resource managers have been using a combination of remotely sensed imagery and field data since the 1970’s and the techniques and the quality of data used have been rapidly evolving. In the past, most maps were produced by government agencies, such as the United States Fish and Wildlife Service (FWS) and National Oceanic and Atmospheric Administration (NOAA), but today private organizations are also contributing to the effort. Two commonly relied upon US federal wetland mapping programs are discussed below.

2.1. US Fish and Wildlife Service National Wetland Inventory One of the earliest and most commonly relied upon mapping efforts was the US FWS National Wetland Inventory (NWI). This Congressionally mandated wetland mapping and monitoring effort was initiated in 1974. NWI maps are primarily produced using mid- to high altitude aerial photographs, photointerpretation techniques, field verification, and some collateral data sources (Federal Geographic Data Committee 1994). The oldest maps are based on 1:80,000 black and white aerial photographs and more recent maps use 1:40,000 scale color infrared aerial photographs. Regardless of the type of aerial photograph used,

76

Megan W. Lang and Greg W. McCarty

maps are based on aerial photographs collected at one point in time, usually during the early spring. The NWI maps are created to provide natural resource managers with the information necessary to make well-informed decisions regarding the future of wetlands (Tiner 1999). Maps can be downloaded online at the NWI website (www.nwi.fws.gov). A new classification scheme was developed so that NWI could provide a nationally consistent product (Cowardin and Golet 1995). This new classification, often termed the Cowardin et al. classification, is a hierarchical system based on ecosystem properties. The “system” is the broadest level of the classification and it includes the estuarine, lacustrine, marine, palustrine, and riverine wetland groups. These systems are divided further based on vegetation, hydrologic regime, salinity, ecology, and the shape and location of wetlands (Cowardin et al. 1979). Although great care has been taken in the production of these maps and they are relied upon by numerous scientists and managers (Kudray and Gale 2000; Pantaleoni 2007), challenges to the cartographic process and product delivery remain. Mapping is most accurate when there is a rapid change between vegetation, hydrology, or soil at the wetland boundary (National Research Council 1995). Other wetland types, such as forested, especially evergreen forested wetlands, and temporarily flooded wetlands, are mapped more conservatively (Tiner 1997b; Tiner 2003; Kudray and Gale 2000). NWI maps usually err less by commission and more by omission. Therefore, if a wetland is indicated on an NWI map there is a high probability that one exists or that it did at the time that the aerial photograph was acquired (Nichols 1994; Stolt and Baker 1995; Tiner 1997b). Estimates of the extent of NWI’s forested wetland omission errors vary widely but omission errors can be substantial (Swartwout et al. 1981; Stolt and Baker 1995; Rolband 1995; Kudray and Gale 2000; Wright and Gallant 2007). Due to improvements in technology and expertise as well as possible land cover changes, the newer NWI maps are likely to be more reliable. However, even the newer maps are based on aerial photographs collected at one point in time and the reliability of maps can be affected by weather conditions (e.g., drought or flood) prior to and during aerial photograph collection. In addition to cartographic challenges, it has also been a challenge for NWI to deliver wetland maps, particularly digital maps, and status reports in a timely manner and for those maps and reports to be updated fast enough to keep up with landscape changes (Pantaleoni 2007; Wright and Gallant 2007). According to the NWI website (www.nwi.fws.gov), significant portions of the US have not even been mapped once and large areas are only available in hardcopy, not digital. It is uncertain whether areas of the US currently without NWI maps will be mapped in the future (Wright and Gallant 2007). Meanwhile many NWI maps are out of date or are rapidly becoming so. The US FWS has recently begun to estimate wetland function, primarily based on landscape position, landform, water body types, and hydrologic flow paths as illustrated in NWI maps, topographic maps, and aerial photographs. Estimated functions include: 1) nutrient and sediment retention, 2) preservation of biodiversity, 3) provision of habitat, 4) surface water retention, 5) maintenance of stream flow, 6) shoreline stabilization, and 7) coastal storm-surge protection. This additional information could help to better characterize ecosystem service studies such as nutrient cycling, water storage, contaminant removal, carbon storage and export, and more (Tiner 1997c; Tiner 2003; Tiner 2005).


77

2.2. NOAA Coastal Change Analysis Program The NOAA Coastal Change Analysis Program (C-CAP) is a nationally standardized database of land cover and habitat change produced for the coastal areas of the United States (NOAA 2008). This regularly updated database was established in the 1990’s to assist scientists, mangers and regulators in better understanding the coastal environment, the interactions between major land cover types (e.g., wetlands and uplands), and the impact of those interactions on marine organisms (Dobson et al. 1995). In doing so, C-CAP promotes an ecosystem-based approach to natural resource management. Unlike NWI, C-CAP maps are created in a digital environment using image processing software. It primarily relies on digital satellite imagery, field data, and other ancillary data in a geographic information system (GIS) format (Dobson et al 1995). C-CAP maps have been produced for multiple dates, at 30 m spatial resolution. Current efforts are focused on adding additional dates and production of finer resolution products in specific regions (Nate Herald 2008 personal communication). Data summaries, reports, and digital maps are available online (www.csc.noaa.gov /landcover). C-CAP is produced in coordination with several other federal agencies as part of the National Land Cover Database (NLCD), facilitated by the Multi-Resolution Land Characteristics Consortium, and serves as the coastal portion of NLCD. Map production methods are nearly identical to those used to produce NLCD data (www.mrlc.gov) for the interior portion of the US, with the largest difference being that C-CAP includes additional wetland classes which are not mapped in the interior of the US. Similar to NLCD, C-CAP primarily relies on Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper Plus (ETM+) data, although additional satellite imagery is used in some regions. These additional imagery sources include Landsat Multispectral Scanner (MSS), Systeme Pour l’Observation de la Terre (SPOT) High Resolution Visible (HRV) data, IKONOS, and Quickbird (Dobson et al. 1995; NOAA 2008). The finer resolution imagery, such as IKONOS and Quickbird, are used to gather more detailed information for training or in areas of enhanced interest (e.g., areas of rapid change; Nate Herold 2008 personal communication). In addition to the standard NLCD land cover classes, C-CAP also maps palustrine forested, palustrine scrub/shrub, palustrine emergent, estuarine forested, estuarine scrub/shrub, and estuarine emergent wetlands, as well as palustrine aquatic beds, and estuarine aquatic beds. In this way, C-CAP contains more detailed wetland classes than NLCD created for the interior of the country but it is less detailed than NWI, providing less information about hydrologic regime and other parameters. C-CAP products from different years are compared to determine the amount, rate and types of changes to land cover which have occurred between map dates. By doing so, C-CAP can be used to assess cumulative impacts of land cover change on ecosystem health (e.g., water quality and habitat quality). Although its coarser spatial resolution makes C-CAP more suitable for regional rather than finer scale applications, it has been found to be relatively accurate. In addition its standardized, digital production aids in timely distribution of consistent, up to date maps. In 1991, field data were used to investigate the reliability of NWI and C-CAP maps. Random locations were examined and NWI was found to be 88-100% accurate while C-CAP was found to be 63-97% accurate (Burgess 1995). Henderson et al. (1998) found that C-CAP data were useful for parameterizing non-point source pollution water quality models. The integration of wetland and upland land cover categories makes C-CAP particularly well

78


suited for parameterizing water quality models since many models have been designed to import land cover data but few ingest wetland specific maps. Although the models may not distinguish between all wetland categories present in C-CAP, the fact that they are available, readily ingested and easily collapsible into more generalized NLCD categories makes their use more likely in the future. Both C-CAP and NWI are exploring different methods for rapidly updating their maps. One of these methods is cross-correlation analysis (CCA), a technique developed to detect changes in land cover using multispectral satellite data (Koeln and Bissonnette 1999). Houhoulis and Michener (2000) have developed a similar process that can also be used to update wetland maps although this particular method is not currently used by C-CAP. An important advantage to these methods is that coarser resolution digital data can be used to rapidly screen for changes in areas which were originally mapped with more costly, finer resolution imagery.

2.3. Other Wetland Mapping Efforts NWI and C-CAP are two examples of regional wetland maps being used today. Other federal efforts to map or monitor wetlands include the US Environmental Protection Agency’s Environmental Monitoring and Assessment Program and the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) National Resource Inventory. NRCS also produces the Soil Survey Geographic Database (SSURGO). Although SSURGO is not a map of current wetlands, it does contain information on hydric or wetland soils which can be used to infer either current or historic wetland presence. If a hydric soil is present, this usually indicates that a wetland either is or was once present at the location. In addition, some states are also producing their own wetland maps. For example, the Maryland Department of Natural Resources produces detailed wetland maps using a methodology similar to the US FWS NWI program (Burgess 1995). The Wisconsin Department of Natural Resources and the New York State Department of Environmental Conservation also produces wetland inventory maps based on photointerpretation of aerial photographs (Johnston and Meysembourg 2002; New York State Department of Environmental Conservation 2008). As an increasing number of federal, state, and local agencies within the US have produced their own wetland maps, it has become increasingly important to develop a wetland mapping standard so that these maps can be compared across jurisdictional lines and dates. Since 1993, the Wetlands Subcommittee of the Federal Geographic Data Committee has been working to coordinate and integrate the collection of wetlands data by various US government agencies. This integration is divided into four increments: 1) standardize terminology, definitions, and classification schemes used by government agencies to gather information concerning wetlands; 2) coordinate the collection of wetlands data by government agencies; 3) evaluate the consistency of wetlands data and statistical results collected by government agencies; and 4) analyze the public policy implications and feasibility of the coordination of wetlands information. This program has made improvements on the coordination of government agencies involved in wetlands data collection (Federal Geographic Data Committee 1996; Shapiro 1995; Federal Geographic Data Committee 1994) and a draft wetland mapping standard was available online (www.fgdc.gov) at the time this chapter was written. The purpose of this standard is to enhance current and future, but not


79

necessarily historic, wetland mapping efforts. The standard specifies minimum data quality standards for inclusion in the US National Spatial Data Infrastructure when these activities are conducted or funded by the US federal government (Federal Geographic Committee 2007).

3. WETLAND HYDROPERIOD: A KEY WETLAND MAPPING PARAMETER By basic definition, wetlands are simply wet-lands (Reed 1993). For that reason, one of the main parameters that should be considered when mapping wetlands is wetland hydrology. Fluctuations in the areal extent, duration, and frequency of wetland flooding and soil saturation are called wetland hydropattern or hydroperiod (National Research Council 1995; Mitsch and Gosselink 2007). Wetland hydroperiod results from all transfers of water into and out of a wetland (National Research Council 1995; Mitsch and Gosselink 2007). Although numerous factors interact to influence wetlands, hydroperiod is the single most important force controlling not only the formation, and therefore the location, but also the functioning of a wetland (Reed 1993; Nestler and Long 1997; Mitsch and Gosselink 2000; Baghdadi et al. 2001; Töyrä et al. 2002; Bartsch et al. 2007; Beeri and Phillips 2007; Martinez and Le Toan 2007). Since wetlands are often situated at a hydrologic edge (Chopra et al. 2001; Chiu and Couloigner 2006), small changes in hydrologic regime can cause substantial changes in ecosystem characteristics and function (Mitsch and Gosselink 2000). Hydrologic conditions control important abiotic factors, which in turn influence wetland biogeochemistry, soils, and vegetation. Wetland chemistry, soils, and vegetation are therefore unique and they interact to serve a variety of functions valued by society, such as pollution reduction and flood control (Whitehead and Thompson 1993; Richardson 1994; Hruby et al. 1995; Whigham 1996; Poor 1999; Woodward and Wui 2001; Baker et al. 2006). Approximately half of all animal and plant species included on the US threatened or endangered species list depend on wetlands during a portion of their lives (US Fish and Wildlife Service 2002). Information on hydroperiod can be used to infer the types of functions that may be served by a wetland (Nestler and Long 1997; Cole and Brooks 2000; Mitsch and Gosselink 2007). Wetland soils (hydric soils) and vegetation (hydrophilic vegetation) can often be distinguished from upland soils and vegetation on remotely sensed images. Additionally, remotely sensed data can discern subtle topographic features that greatly influence the function of wetlands. Certain conditions (e.g., topographic depressions) can encourage the concentration of water and therefore the origination of wetland hydrology. Moreover, coarser landforms that favor the formation of wetlands, such as the margins of stream, can also provide important clues to the location of wetlands. For the above stated reasons, remote sensors (i.e., people who analyze remotely sensed images) monitor wetland hydrology, hydric soils, hydrophytic vegetation, and/or conditions which encourage the concentration of water to map wetlands.

80


4. DIFFICULTIES INHERENT IN WETLAND MAPPING Regardless of the methodology used to map wetlands, there are numerous difficulties inherent in the mapping process. These limitations may be related to a variety of factors including the character of the wetland being mapped, the map scale, the natural conditions present at the time of data collection, the spectral and spatial resolution of the imagery used, the software and hardware used to process the data, and the skill of the image processor (Tiner 1999). It should be noted that the specifications necessary to produce a “good” wetland map depend heavily on what that map is being used for. For example, a relatively fine scale wetland map like NWI may not be necessary when assessing wetland abundance at a global scale but it would be helpful when attempting to determine exactly where to locate a wetland mitigation site. Similarly, it may be less important to use a wetland map that accurately delineates forested wetlands if a researcher or natural resource manager is trying to locate potential habitat for an endangered bird which only lives in marshes, wetlands with nonwoody vegetation.

4.1. Temporal Considerations The timing of image collection is vital to the accurate mapping of wetlands (Klemas 2001). Ideal times can vary by hour, day, month, or year and by wetland type. Generally, the more water present in an area, either in the form of soil saturation or flooding, the more unique an area will appear as compared to surrounding uplands, and the easier it will be to identify the area using remotely sensed data. However, hydroperiod is a highly dynamic parameter in many wetlands and these wetlands must be imaged at specific times to capture water levels at their peak. For example, moisture levels can vary due to tides, rainfall events, the melting of snow and ice, the release of upstream dams, or changing levels of evapotranspiration (a combination of evaporation and transpiration primarily controlled by temperature and plant physiology). It is also important to consider the fact that precipitation and wetness conditions change from year to year (e.g., drought or flood years) as well as between seasons. For that reason, images captured during years of average weather conditions will best represent the location of wetlands. However, in some circumstances the collection of imagery during weather extremes may aid the mapping of wetlands. For example, Munyati (2000) found that some wetlands in Africa were readily apparent during droughts when wetland vegetation was the only green vegetation present in the landscape. Ideal timing of imagery collection for wetland map creation depends on the type of wetland being mapped. Deciduous forested wetlands are best mapped during the spring before leaf-out. At this time, the tree canopy interferes less with the detection of the ground and water tables tend to be high due to snowmelt and/or an extended period of low evapotranspiration rates. Because evergreen vegetation retains its leaves year round, evergreen forested wetlands do not have an optimal time for imagery collection based on vegetative state. Submerged aquatic vegetation (SAV), on the other hand, is best mapped during maximum biomass, low turbidity, low wave/wind action, and during low tide. When collecting remotely sensed data over tidal areas, it is difficult to obtain images that are recorded at the same tidal level. This is not only due to the difficulty of obtaining adjacent


81

scenes at the same tidal level. It is also due to tidal flooding that proceeds gradually upstream so that wetlands within a large estuarine system are in different stages of flooding at one point in time (Tiner 1999).

4.2. Spatial Considerations The spatial resolution or scale of imagery affects what can and cannot be interpreted in a wetland map. All maps have a minimum mapping unit (MMU) and a target mapping unit (TMU). The MMU represents the smallest wetland that appears on the map, while the TMU estimates the smallest wetland that is consistently mapped. The TMU will differ not only based on the spatial resolution of the imagery used to create the map but also based on the type of wetland being mapped (Tiner 1999). For example, easily identified wetlands, such as coastal marshes and flooded basins, will have a smaller TMU than wetlands that are more difficult to identify, like drier or evergreen wetlands. To map wetlands at a certain MMU, requires an appropriate scale of imagery. For example, when mapping wetlands in Maryland using color infrared aerial photography Tiner and Smith (1992) recommended imagery with a spatial scale of 1:58,000 to produce maps with a MMU of 0.4 ha and 1:24,000 imagery to produce maps with a MMU of 0.1 to 0.2 ha. The use of finer resolution imagery, however, requires a greater mapping effort. A spatial resolution of 1:24,000 or better is recommended for local wetland mapping when boundaries need to be precise and smaller wetlands need to be identified. Whereas, broader scale imagery, such as 1:58,000, are more appropriate for national mapping efforts (Tiner 1999). Generally, as the resolution of imagery used to map wetlands becomes finer, wetland boundaries are more distinct and refined, smaller wetlands are mapped, stereoscopic details become obvious, and difficult to identify wetlands become more easily identified. However, finer scale imagery only increases the quality of wetland maps to a point. For example, wetlands are easier to delineate at 1:12,000 than 1:5,760. This is due to extremely fine details, like tree branches, that make it difficult to delineate wetlands at this scale (Tiner and Smith 1992). It could be said at this point that the “forest (or forested wetland) cannot be seen through the trees” at this finer scale.

4.3. Wetland Type Considerations The most difficult types of wetlands to map are palustrine forested wetlands (Jacobson et al. 1987; Tiner 1990; Sader et al. 1995; Kudray and Gale 2000; Klemas 2001; Wright and Gallant 2007), evergreen wetlands, and wetlands with ephemeral hydrology (Augusteijn and Warrender 1998; Kudray and Gale 2000). Palustrine forested wetlands are difficult to map because their canopies often prevent viewing of the ground’s surface and the trees found in this type of wetland are identical or spectrally similar to those found in upland forests (Sader et al. 1995; Augusteijn and Warrender 1998; Klemas 2001). Moreover, relatively small variations in topography are capable of forming these types of wetlands. As a result, all forested wetlands, but especially small, isolated forested wetlands (e.g, vernal pools) are one of the most difficult types of wetlands to detect due to a combination of the presence of the forest canopy, often ephemerally hydrology, and in the case of vernal pools their size. Although they are difficult to detect using remotely sensed images, the mapping of palustrine

82


forested wetlands is vital because they represent approximately half of all wetlands found in the US (Sader et al. 1995) and they are the most likely type of wetland to be lost in the future (US Fish and Wildlife Service 2002). New techniques need to be developed to remotely sense forested wetlands. Although forested wetlands are often more readily identified with finer resolution imagery, this is only true to a degree. Wilen and Smith (1996) argue that the challenge to identifying forested wetlands lies not in spatial resolution but in spectral resolution. Promising technologies for meeting the forested wetland mapping challenge include radar and lidar. In addition, better quantification of the influence of hydrology on the spectral signature of forested wetlands using hyperspectral data would be helpful. Rundquist et al. (2001) called for the increased characterization of the composite vegetation-water spectral signature of emergent vegetation. For forested wetlands, the impact of hydrology on the spectral signature of the leaf layer after it has been exposed to flooding (leaves on the ground) may also be helpful if it is the leaf layer and not soils that are being remotely sensed. Since evergreen wetlands retain their leaves year-round, it is difficult to determine hydroperiod or soil type beneath them. Sometimes evergreen wetlands provide hints regarding their status. Evergreens growing in wetlands may be shorter than those growing elsewhere or they may show signs of chlorosis due to water stress (Tiner 1999). With deciduous or evergreen forested wetlands, it may be possible to view saturated soils or understory wetland characteristics through canopy openings. Wetlands exist along a continuum of soil moisture conditions, between permanently inundated, deepwater habitats and drier areas that do not sustain the soil moisture levels needed to support anaerobic soils. Many of the drier wetlands are difficult to identify on the ground yet alone with remotely sensed data. The more difficult it is to map a wetland on the ground, the more conservatively it will be mapped with remotely sensed data (Tiner 1999).

5. THE POTENTIAL OF DIFFERENT SENSORS FOR WETLAND MAPPING Aerial photographs have traditionally been used to map wetlands and some current wetland mapping programs use multispectral images (Baghdadi et al. 2001), but recent research suggests that other types of remotely sensed imagery have promise for wetland delineation. The National Research Council (1995) concluded that certain types of imagery may provide enhanced wetland mapping capabilities over traditional photointerpretation methods. This section investigates the advantages and disadvantages of using aerial photography and multispectral, hyperspectral, passive microwave, radar, and lidar imagery for wetland mapping. This is not an exhaustive review of all types of remotely sensed imagery. Instead it represents the types of imagery that are most common and that researchers have suggested have the greatest potential to improve wetlands mapping. This section discusses both spaceborne and airborne imaging systems. It is important to not only differentiate between different types of imagery but also between the different platforms used to collect those images since there are advantages and disadvantages inherent to these vantage points. The advantages of using satellite imagery for wetland mapping include coverage, timeliness, lower costs, and the ease with which it can be integrated with other types of data in a GIS (Dobson et al. 1995; Federal Geographic Data Committee 1992;


83

Papa et al. 2006). But the use of satellite data also implies certain limitations. These limitations include the often greater interference of weather with satellite data collection. In the past, the use of satellite data has meant broader spatial resolutions but this in no longer the case with new generation sensors (e.g., IKONOS and Quickbird) that collect finer spatial resolution data.

5.1. Aerial Photography The combination of aerial photographs and manual photointerpretation has been and continues to be the most commonly relied upon wetland mapping method (Lyon and McCarthy 1995; National Research Council 1995; Phinn et al. 1999; Harvey and Hill 2001). This is primarily due to the use of well-established methods and protocols, the availability of current and historic data over broad areas (Baker et al. 2006), the comparatively fine spatial resolution of aerial photographs (Klemas 2001), and of course the relatively reliable results obtained from such efforts (Rutchey and Vilchek 1999; Harvey and Hill 2001; Wright and Gallant 2007). In addition, the aerial platform provides flexibility with the timing of data collection that the satellite platform lacks. For example, airplanes can collect images at certain times of the day that correspond with optimal tidal levels whereas the overpass time of satellites is usually predetermined. Historic aerial photographs are available for much if not all of the US providing a valuable baseline for change detection analysis (Baker et al. 2006). One of the most common sources of aerial photography used for wetland mapping in the US is the US National Aerial Photography Program (NAPP) and the National High Altitude Aerial Photography (NHAP) program, both directed by the US Geological Survey (USGS). One of the major differences between aerial photography and satellite data lies not in the character of the data but with the methods that have traditionally been used to process those data. Aerial photography is often classified by a photointerpreter without computer automation of the mapping process. Photointerpretation relies on the human eye to make qualitative decisions based on the image’s tone, color, spatial patterns, texture, height/topography, associations, and other characteristics. The accuracy of these decisions can benefit from the experience and judgment of the photointerpreter. The interpretation of aerial photography also benefits from the use of stereoscopic viewing. Stereoscopic viewing helps the photointerpreter determine relative variations in elevation and vegetation height. Topographic variations provide clues as to where wetlands would likely form and information on vegetation height aids in the determination of wetland type (e.g., forested versus shrubscrub). Changes in plant height may also indicate soil moisture and other abiotic conditions (National Research Council 1995). Studies have compared the use of aerial photographs and aerial photointerpretation to the use of multispectral data for wetland mapping in different environments (Rutchey and Vilchek 1999; Harvey and Hill 2001). One of the major disadvantages of aerial photographs as compared to multispectral data is the relatively lower spectral resolution of the aerial photographs but the superior spatial resolution of aerial photographs can compensate for this disadvantage. A study conducted in the Florida Everglades found that SPOT data overestimated the amount of cattail present in an impounded wetland due to confounding factors, such as, fire, hydrology, periphyton species composition, and macrophyte morphology. Aerial photography was better able to overcome these difficulties and map

84


cattail in these areas due to the experience and reasoning of the photointerpreter (Rutchey and Vilchek 1999). Harvey and Hill (2001) compared the ability of multispectral data (SPOT and Landsat TM) to that of aerial photographs and manual interpretation techniques for mapping an Australian freshwater swamp. Although fourteen land cover types could be delineated from 1:15,000 aerial photographs with 89% accuracy, only three broad land cover types could be distinguished using the multispectral data while meeting minimum accuracy requirements (≤80%). The difference in accuracy between the aerial photographs and multispectral data was, in part, attributed to the contextual and textural information used by the photointerpreter (Harvey and Hill 2001). Although the manual interpretation of aerial photography is the most commonly used method for detailed wetland mapping, this method is subjective (Finlayson and van der Valk 1995; Augusteijn and Warrender 1998; Baker et al. 2006), time consuming and therefore cannot be quickly updated (Phinn et al. 1999; Harvey and Hill 2001; Wright and Gallant 2007), is relatively expensive (Dobson et al. 1995; Mumby et al. 1999; Lunetta and Balogh 1999; Phinn et al. 1999; Harvey and Hill 2001; Klemas 2001; Hirano et al. 2003), and has difficulty mapping certain wetland types (see section 2.1). There are inherent limitations to the use of human vision to classify a map (Rutchey and Vilchek 1999) and these decisions are inherently subjective so resultant maps may vary according to the person who creates them (Finlayson and van der Valk 1995; Augusteijn and Warrender 1998; Baker et al. 2006). Mumby et al. (1999) estimate that the creation of maps for a relatively small area (~150 km2) may take six times as long using aerial photointerpretation rather than automated interpretation of digital airborne imagery and that the cost of manual interpretation of aerial photographs will increase at a faster rate than the cost of digital interpretation. The inability of the US FWS to use aerial photointerpretation methods to map wetlands for the entire US (starting in the 1970s) illustrates the cumbersome nature of aerial photointerpretation for broad scale mapping. The type of aerial photograph used to create a wetland map has the potential to affect map accuracy. Although photointerpretation has traditionally been a manual process, which utilized analog photographs rather than digital images, analog aerial photographs can be converted to digital. Still the challenge of scanning in large numbers of photographs and normalizing photographs for variations in illumination, angle of acquisition, and sometimes even phenology and atmospheric conditions remains a challenge (Phinn et al. 1999). Digital cameras are an improvement over analog aerial photographs and can be processed using methods similar to those used for processing multispectral images (Phinn et al. 1999), although the lack of spectral information is still a limitation (Becker et al. 2007; Silva et al. 2008). If possible, it is usually advantageous to use color or color infrared aerial photographs to map wetlands, rather than black and white images. Color infrared film is usually preferred for wetlands mapping because it includes more spectral information than true color film and allows for greater contrast between different plant communities (Tiner 1999; Federal Geographic Data Committee 1992). Dale et al. (1996) found that color infrared aerial photography was a valuable tool in the identification of wetlands. A major exception to this statement occurs when mapping submerged aquatic vegetation (SAV). The relatively low spectral resolution of aerial photographs significantly limits their use for the detection of SAV in general (Silva et al. 2008). However, the use of true color film for SAV detection is preferable to the use of color infrared film due to the superior ability of shorter wavelengths


85

energy (the visible bands which compose true color imagery) to penetrate the water column (Tiner 1999).

5.2. Multispectral Multispectral sensors (Table 1) provide information on solar reflectance from materials on the Earth’s surface in multiple portions of the electromagnetic spectrum (e.g., visible, near infrared, mid-infrared, and thermal), at relatively fine (1 – 5 m) to medium spatial resolutions (10 – 30 m) and some also provide thermal data. Sensors with coarser spatial resolutions, such as the AVHRR (Advanced Very High Resolution Radiometer) with approximate 1 km resolution, are usually not considered viable options for wetland mapping at the local to regional scale (Munyati 2000; Ringrose et al. 2003), so they are not discussed below. Relative to aerial photographs, satellite borne multispectral sensors provide enhanced spectral resolution (multiple bands) and are capable of imaging a larger area at once (Rundquist et al. 2001; Li and Chen 2005), which simplifies image interpretation. Studies have found that the increased spectral resolution of multispectral data enhances wetland mapping (Federal Geographic Data Committee 1992; Phinn et al. 1999; Harvey and Hill 2001; Töyrä et al. 2002) and, in some cases, helps compensate for reduced spatial resolution (Harvey and Hill 2001). In addition, if fine detail is not required these images are usually less expensive than aerial photographs (Mumby et al. 1999) and they can be collected regularly, as dictated by satellite orbits (Rundquist et al. 2001; Li and Chen 2005). Therefore, these sensors provide not only increased spectral but also increased temporal resolution over aerial photographs, both of which are important for distinguishing different wetland classes. The comparatively fine temporal resolution of these multispectral sensors is complemented by a relatively long historic record (e.g., Landsat MSS first launched in 1972). Finally, their digital format and standardized imagery allow for automated, repeatable classification of wetlands and other land cover types (Houhoulis and Mitchner 2000; Ausseil et al. 2007). The infrared portion of the electromagnetic spectrum, which is often collected by multispectral sensors (Table 1), is considered to be particularly well suited for wetland mapping applications (Federal Geographic Data Committee 1992; Phinn et al. 1999; Munyati 2000). This is due to the high spectral response of vegetation in the near-infrared coupled with the strong absorption of water in the same region, which makes for a sharp contrast between water and vegetation in the near-infrared (Lyon and McCarthy 1995) and water absorption features (portions of the electromagnetic spectrum with distinctly lower reflectance when water is present) in the mid-infrared. Both multispectral and hyperspectral data can take advantage of this contrast with individual bands over these regions. The use of color-infrared (CIR) aerial photos can also improve wetland detection via information on the near infrared portion of the electromagnetic spectrum. However, the use of color infrared photographs is not as advantageous as the isolation of these regions through the use of distinct bands by way of digital multispectral images (Lyon and McCarthy 1995). Although many of the older satellite systems, such as Landsat MSS, Landsat TM, and SPOT, offer improved spectral resolution, their spatial resolution is often insufficient for mapping smaller or more complex wetlands (Welch et al. 1999; Ramsey and Laine 1997; Ausseil et al. 2007; Wright and Gallant 2007).

86

Megan W. Lang and Greg W. McCarty Table 1. Commonly used satellite borne multispectral sensors

Sensor Resolution (m) Bands Width Repeat 80 (240 thermal) G, R, NIR, MIR 185 km 18 days Landsat MSS 30 (120 thermal) B, G, R, NIR, MIRx2, TH 185 km 16 days Landsat TM Landsat ETM+ 30 (60 thermal; 15 pan) P, B, G, R, NIR, MIRx2, TH 185 km 16/18 days 24 G, R, NIR, MIR 141 km 24 days IRS LISS-III 20 (10 pan) P, G, R, NIR 60 km var SPOT 1,2,3 20 (10 mono) M, G, R, NIR, MIR 60 km var SPOT 4 10 (20 MIR; 2.5 or 5 pan) P, G, R, NIR, MIR 60 km var SPOT 5 4 (1 pan) P, B, G, R, NIR ~12 km var IKONOS 2.4 (61 cm) P, B, G, R, NIR 16.5 km var Quickbird 4 (1 pan) P, B, G, R, NIR 8 km var OrbView-3

Life-Span Information 1972 - 1992 edc.usgs.gov 1982 - Pres. edc.usgs.gov 1999 - Pres. edc.usgs.gov 2003 - Pres. nrsa.gov.in 1986 - Pres. spot.com 1998 - Pres. spot.com 2002 - Pres. spot.com 1999 - Pres. geoeye.com 2001 - Pres. digitalglobe.com 2003 .geoeye.com

Note that many of the more recent commercial satellites are pointable and therefore the repeat frequency can vary. This list is not comprehensive. Multiple bands in one portion of the electromagnetic spectrum are denoted with an “x” and then the number of bands (e.g., x2). Sensor name (Sensor), spatial resolution in meters (Resolution), the portion of the electromagnetic spectrum sampled by each band (Bands), swath width (Width), temporal resolution or number of days between overpasses (Repeat), the years the sensor has operated (Life-Span; Present abbreviated as Pres.), and websites where more information about the sensors can be found are provided in the table.

Phinn et al. 1999 suggests that current multispectral sensors (Landsat TM, SPOT XS, and IRS-1C) are limited to mapping wetlands with a MMU greater than 9 ha, although other studies estimate that Landsat TM is capable of mapping wetlands at much finer MMU (0.8 – 1.0 ha; Federal Geographic Data Committee 1992; Lunetta and Balogh 1999; Wright and Gallant 2007). Landsat MSS data (80 m spatial resolution) have been used to map wetlands with varying levels of success (Moore and North 1974; Severs et al. 1974; Bennet 1987). Severs et al. (1974) were able to use Landsat MSS bands 5 (red) and 7 (near infrared) to classify wetland patches of ~4 ha or more into four broad categories (i.e., marsh, seasonally flooded depressions, meadow, and open water). However, in another case, researchers had difficulties using Landsat MSS to map large, homogenous coastal wetlands, which are among the easiest wetland types to map (Ramsey and Laine 1997). The use of Landsat TM for inventorying wetlands is also limited due to the lack of spatial detail (Ramsey and Laine 1997; Wilen and Tiner 1989). Newer satellite data, such as IKONOS, provide finer spatial resolution multispectral data (Table 1). For example, IKONOS collects 4 m resolution data over the blue, green, red, and near-infrared areas of the electromagnetic spectrum and Quickbird collects 2.4 m data in the blue, green, red, and near-infrared portions of the electromagnetic spectrum. It has been predicted that newly available finer resolution multispectral sensors (e.g., IKONOS, Quickbird, and Orbview) should provide an increase in wetland mapping capability (Phinn et al. 1999). As with aerial photography, multispectral images can only be collected during the day and their collection is limited by cloud cover and atmospheric conditions (e.g., haze). These limitations may preclude the regular and repetitive collection of data that is required for certain wetland studies (Baghdadi et al. 2001; Costa 2004; Costa and Telmer 2007). This is especially true if combined with infrequent temporal coverage (e.g., satellite repeat frequency). Unfortunately, the optimal period for data collection may be short. For example, in many forested regions of the US there is a short time period between ice/snow melt and the leafing-out of trees, after which features below the forest canopy are usually obscured. More than one study has cited the presence of a vegetative canopy as one of the main deterrents to


87

accurate wetland mapping using multispectral data (Moore and North 1974; Carter 1982; Alsdorf et al. 2007; Costa and Telmer 2007). Unless significant gaps in the canopy are present, the hydrologic conditions on the ground cannot be observed. In the sub-tropics and tropics optimal time for image collection often corresponds with the periods of increased cloudiness (precipitation events). Since Landsat TM can only be acquired every 16 days, the chance of collecting a relatively cloud-free image during this optimal period is small (Federal Geographic Data Committee 1992). The ability of some sensors (e.g., SPOT and Quickbird) to collect data at multiple view angles has the potential to reduce the time between satellite overpasses and therefore increase the likelihood that images can be collected during times when conditions on the ground are optimal. When using multispectral and even hyperspectral imagery, there is often a trade-off between spectral and spatial resolution (Harvey and Hill 2001; Becker et al. 2007), with finer spatial resolution sensors having fewer bands (e.g., IKONOS, Quickbird, and OrbView) and sensors with more bands having coarser spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer [MODIS]). Adequate spatial resolution is necessary since wetland patches are often small. However spectral resolution is also important because the spectral character of different land cover classes is often similar and adequate spectral resolution is necessary to distinguish different land cover types (Harvey and Hill 2001). Wetlands that are spectrally distinct from surrounding land cover types will be mapped more accurately with multispectral images than those which are not (McCarthy et al. 2005). Therefore no one existing multispectral sensor is optimal for wetland mapping (Becker et al. 2007) in all situations and the success of wetland mapping efforts often depends just as much on the type of wetland being mapped as the type of imagery being used. However, the literature does support some generalizations regarding the utility of available multispectral data. Two of the most commonly used groups of multispectral sensors include SPOT (Jensen et al. 1991; Rutchey and Vilcheck 1999; Ringrose et al. 2003; Töyrä and Pietroniro 2005) and Landsat (Sader et al. 1995; Baghdadi et al. 2001; Townsend and Walsh 1998; Töyrä et al. 2002; Baker et al. 2006; Wright and Gallant 2007). Landsat data have been found to be preferable to other multispectral satellite data for wetland detection (Hewitt 1990; Bolstad and Lillesand 1992; Harvey and Hill 2001) and the recent announcement that it will be distributed free of charge after January of 2009 makes its use even more attractive. Although Harvey and Hill (2001) found that both Landsat TM and SPOT XS were adequate for mapping broad categories of wetland types, the coarser spatial resolution seven band Landsat TM data were preferable to the finer resolution four band SPOT data for mapping wetlands in a freshwater Australian swamp. Longer wavelength bands, especially the mid-infrared Landsat TM band (band 5; 1.55 – 1.75 μm), have been found to be especially useful for the detection of water (Federal Geographic Data Committee 1992; Phinn et al. 1999; Harvey and Hill 2001; Töyrä et al. 2002). The Federal Geographic Data Committee (1992) identified Landsat TM bands 4 and 5 as most effective for wetland delineation. Landsat TM bands 2, 3, and 4 have been found to be helpful for identifying the presence of understory vegetation (Congalton et al. 1993), which is helpful for mapping some forested wetlands (Harvey and Hill 2001). Sader et al. (1995) found that Landsat TM could be used to distinguish between forested wetland, other wetlands, forested uplands and other uplands with an accuracy of approximately 80%. Temporal resolution was found to be an important variable when using Landsat TM to map a diverse group of wetlands on the border between Maryland and Delaware. When the accuracy of a wetlands map produced with single date leaf-on (June) TM

88


imagery was compared to one that was produced using multi-temporal TM data (leaf-on and leaf-off [April]), the multi-temporal map was found to be more reliable (Lunetta and Balogh 1999). The leaf-on data were used primarily to produce a land cover map and the leaf-off data were used to detect wetland hydrology. The accuracy of the multi-temporal wetlands map was superior to that of the single date map, 88% accuracy versus 69% accuracy respectively. Forested wetlands were found to be especially difficult to map without the use of multi-date imagery. On the other hand, open water areas are relatively easy to identify using Landsat data. Beeri and Phillips (2007) used Landsat TM and ETM+ to estimate hydroperiod in wetlands with open water with high accuracy (96% detection of water bodies greater than ~15 m). They suggest the use of SAR data to improve the detection of water beneath emergent vegetation and fine resolution elevation data to locate areas of likely inundation since the presence of vegetation (Moore and North 1974; Carter 1982) and sediment (Engman and Gurney 1991) reduce the effectiveness of multispectral data for the detection of inundation. SPOT data have also been useful for wetland mapping, especially when differentiating between medium to small wetland patches which are spectrally distinct from other land cover types. Töyrä et al. (2002) found that SPOT was capable of detecting flooded wetlands with between 66% and 80% accuracy in freshwater wetlands with a combination of forests and non-woody (i.e., herbaceous) vegetation. The Indian Remote Sensing Satellites (IRS) may be used more frequently for land cover mapping in the future as the availability of current high quality Landsat images becomes less certain due to the instrument malfunction which occurred in the Landsat 7 (ETM+) sensor in July of 2003 and the uncertain future of Landsat 5 (TM) due to its advanced age. However, the lack of a mid-infrared band on the IRS-Linear Imaging Self Scanning Sensor (LISS)-II sensor was found to limit wetland detection (Johnston and Barson 1993; Mahlke 1996). In complex wetland environments, more traditional statistical classifications commonly used with multispectral imagery (e.g., supervised and unsupervised classification) may have limited utility (McCarthy et al. 2005). In these cases and when greater accuracy is required in general, the use of different classification techniques can improve the accuracy of resultant maps. For example, the use of indexes, such as the normalized difference vegetation index (cite) or transformations, such as the tassel-cap transformation may improve the discrimination of different wetland types (Sader et al. 1995; McCarthy et al. 2005; Wright and Gallant 2007). Huguenin et al. (1997) found that subpixel spectral analysis was significantly better than traditional classification methods (ISODATA, maximum likelihood, and minimum distance) for mapping bald cypress trees and tupelo gum trees in Georgia and South Carolina when using Landsat TM imagery. Subpixel spectral analysis identified cypress with 89% accuracy and tupelo gum trees with 91% accuracy when they occurred in pure stands or mixed with other species (Huguenin et al. 1997). Classification trees have proved helpful for wetland mapping (Wright and Gallant 2007), partially because they can be used to incorporate data from a variety of sources (Wright and Gallant 2007). When using Landsat ETM+ images to identify wetlands, a Stochastic Gradient Boosting decision tree technique was found to be more accurate (86.0%) than a Classification Tree Analysis (73.1%; Baker et al. 2006). Chiu and Couloigner (2006) improved classification accuracy by using a Fuzzy C-Means classifier to represent the boundary between different wetland types. This method represents the transition between different wetland types as it often is on the ground – gradual.


89

In addition to the improvement of wetland maps via the use of different classification methods, acquiring multispectral imagery at multiple view angles may enhance the discrimination of different land cover types, including wetlands (Vanderbilt et al. 2002; Dupigny-Giroux 2007). This enhanced discrimination is due to the varying appearance of wetland vegetation and water signatures when viewed from different angles and is sometimes called a directional signature (Dupigny-Giroux 2007). POLDER (Polarization and Directionality of Earth’s Reflectance) data were found to reliably discriminate three cover types (open water, emergent vegetation above inundation, and non-inundated cover types) when images were collected at angles which maximize specular reflectance of the water surface (i.e., maximize the interception of sunglint by the sensor; Vanderbilt et al. 2002). Pinty et al. (2002) found that multi-directional signatures could be used to distinguish between inundation and dark soils, which often appear similar on multispectral images. Another study found that ratios of AirMISR data collected at different angles [fore and aft of nadir (26.1o, 45.6o, and 60.0o)] were capable of distinguishing not only different wetland types in a forested area of Maine but also moisture gradients in emergent wetlands, species type and vigor, the relative proportion of water and vegetation, and areas of vegetation undergoing moisture stress (Dupigny-Giroux 2007). Dupigny-Giroux (2007) postulates that a multi-angle approach may help map evergreen wetlands. Another study found that a combination data collected to maximize sun glint and spectral mixture analysis could be used to detect areas of wetland inundation, with accuracy of within pixel estimates of inundation increasing with increasing pixel size (Vanderbilt et al. 2007).

5.3. Hyperspectral Hyperspectral data are characterized by numerous, narrow spectral bands collected in the visible, near-infrared, mid-infrared, and sometimes thermal portions of the electromagnetic spectrum. For example, the US National Aeronautics and Space Administration’s (NASA) satellite borne Hyperion sensor provides imagery with 220 spectral bands at a spatial resolution of 30 m. NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has been flown on a variety of different aircraft and has 224 narrow, contiguous spectral bands from 400 to 2500 nm (10 nm wide bands) and a pixel size of 20 m (Vane et al., 1993). These numerous, finely segregated spectral allow analysts to identify different materials based on their “spectral signature” or diagnostic patterns in absorption and reflection usually associated with the molecular and/or cellular properties of the material (Kokaly et al. 2003; Schmidt and Skidmore 2003). Although the use of spectral signatures can be very helpful in imagery analysis, these signatures can vary temporally with phenology and environmental conditions (Judd et al. 2007; Silva et al. 2008) making generalizations difficult, and therefore mapping through time and space challenging (Schmidt and Skidmore 2003). In addition, the importance of texture and context should not be discounted (Hirano et al. 2003) even when spectral resolution is extremely high. Similar to other types of imagery, hyperspectral data are capable of detecting parameters that can be used to infer wetland function. However, in addition to more obvious parameters (e.g., habitat fragmentation) hyperspectral data have the potential to detect biochemical properties such as nutrient and chlorophyll content (Schmidt and Skidmore 2003; Judd et al. 2007). Although the detection of these properties is currently

90


unlikely to be part of a local to regional wetland mapping program, this may change in the future. The drawbacks of hyperspectral data include those of all optical data, such as an inability to penetrate vegetation cover, sensitivity to clouds and other weather events, and restricted use to daylight hours. Additional restrictions of hyperspectral data include large data volume (Phinn et al. 1999; Hirano 2003; Laba et al. 2005; Becker et al. 2007), less developed/more complex image processing techniques (Phinn et al. 1999; Klemas 2001; Hirano et al. 2003; Laba et al. 2005), and the relatively poor availability of hyperspectral data in general and high price of commercial data. All of these drawbacks should be lessened as technologies mature. At present, fine to moderate resolution hyperspectral data are primarily available from airborne sensors, although an experimental hyperspectral satellite (Hyperion) collects a limited amount of medium resolution (30 m) data. As of 2008, Hyperion has greatly exceeded its life expectancy and shows signs of decline. MODIS, another satellite borne hyperspectral sensor with 36 bands, is less promising for local to regional wetland mapping due to its relatively coarse spatial resolution (250 to 1000 m). Although MODIS’s broad spatial resolution is not ideal for most regional wetlands mapping, its launch does provide hope for the availability of finer resolution hyperspectral satellite data in the future. Even the spatial resolution of some commonly available finer resolution datasets (e.g., Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperion) may be too coarse for some wetland mapping applications (Hirano et al. 2003; Becker et al. 2007). Until hyperspectral data become more available, their use in operational wetland mapping programs is unlikely (Phinn et al. 1999; Govender et al. 2007). As more hyperspectral sensors begin to collect data, data will become more available and processing techniques will advance. With time the ever-increasing capabilities of new hardware will be able to better handle hyperspectral’s large datasets. When these problems are solved, hyperspectral data could be used to supplement other types of remotely sensed data in regional wetlands mapping, especially if more specific information on plant species presence or other factors is necessary. For now, it is more likely that hyperspectral images will continue to be used to identify narrowly defined wetland classes and plant species of interest (Sahagian and Melack 1996; Klemas 2001; Schmidt and Skidmore 2003; McCarthy et al. 2005), including invasive species (Laba et al. 2005). Although the availability of satellite derived hyperspectral data and even publicly available US government sponsored airborne data (AVIRIS) is currently limited and fine spatial resolution data are not available, the availability of finer spatial resolution commercial data is increasing. There are currently several hyperspectral sensors being commercially produced and flown aboard aircraft including the CASI 1500, AISA Eagle, and AISA Hawk. The CASI 1500 is capable of collecting up to 288 bands between 380 and 1050 nm at a spatial resolution as high as .25 m (www.itres.com). The AISA Hawk and Eagle are often used together, since they collect information from different portions of the electromagnetic spectrum. The Eagle is capable of collecting up to 488 bands between the wavelengths of 400 and 970 nm, while the Hawk is capable of collecting 320 bands between the wavelengths of 970 and 2450 nm (www.specim.fi). The spatial resolution of the Hawk and Eagle sensors will vary based on platform and sensor parameters. Hyperspectral sensors have primarily been used to map wetland plant species (Hirano et al. 2003; Kokaly et al. 2003; Schmidt and Skidmore 2003; McCarthy et al. 2005; Filippi and Jensen 2006; Judd et al. 2007), although they have also been used to monitor other wetland


91

parameters, such as the condition of wetland hydrology (Anderson and Perry 1996). A review of several research projects involving the use of hyperspectral data for mapping aquatic vegetation found that accuracies ranged from 70 to 96% (Silva et al. 2008). Schmidt and Skidmore (2003) found that the spectral signatures of different salt marsh vegetation types were significantly different and they propose that these differences should allow identification of individual species using hyperspecrtal imagery. Other studies have found that the mapping of various marsh vegetation species is possible (Judd et al. 2007). Hirano et al. (2003) found higher accuracies when mapping spike rush (100%) as compared to red mangroves (40%). Although hyperspectral data, like all optical data, are limited by the tree canopy during much of the year, hyperspectral data have been used to detect forested wetlands during the leaf-on period. This can be accomplished by detecting water stress in some tree species, because when some tree species are exposed to flooding they have elevated reflectance in the green (550 nm) and near-infrared (770 nm) portions of the electromagnetic spectrum. This approach has been shown to be effective in monospecific stands of facultative wetlands trees (Anderson and Perry 1996). Hyperspectral data are particularly well suited for the mapping of SAV, since the water column reduces reflectance from the vegetation itself making detailed examination of spectral characteristics necessary to identify SAV (Silva et al. 2008). Similar to the analysis of multispectral data, spectral unmixing methods to can be used with hyperspectral data to estimate the fraction of endmembers included in each pixel (Roberts et al. 1993; Lillesand and Kiefer 1994; Rosso et al. 2005; Judd et al. 2007). This type of analysis has the potential to be more precise when using the numerous bands available with hyperspectral images, although the selection of truly pure (unmixed) endmembers can be a challenge (Filippi and Jensen 2006; Judd et al. 2007). By estimating the fraction of plant species per pixel, spectral unmixing methods can be used produce a more accurate wetland vegetation map (McCarthy et al. 2005). Rosso et al. (2005) applied spectral mixture analysis and multiple endmember spectral mixture analysis to AVIRIS data in order to map marsh vegetation. Both approaches were found to be suitable for mapping marshes, although the multiple endmember spectral mixture analysis had the advantage of incorporating more than one endmember per class. Other classification approaches, such as neural networks, have also proved beneficial. Filippi and Jensen (2006) found that neural networks performed better than end member based approaches. This neural network approach has the additional advantage of not requiring the initial input of spectral endmembers.

5.4. Passive Microwave Passive microwave radiometers operate in the same spectral range as active radar sensors, such as synthetic aperture radars, but they sense microwave energy that is naturally emitted from objects. The emission of this energy is dependent on numerous factors including, surface roughness, temperature, soil and vegetation water content, bulk density, and soil texture. Passive sensors can be used to measure soil moisture, depth to shallow water table, and biomass, which are all important characteristics for locating wetlands (Shutko et al. 1997; Jackson et al. 1999). Indeed, sensitivity of passive microwave systems to soil moisture has been well established (Du et al. 2000; Shi et al.2006). Passive microwave sensors usually detect energy between 0.5 to 30 cm. Below 0.5 cm the sensor is sensitive to clouds and rain and above 30 cm radar, television, and other forms of radiation will interfere with microwave

92


reception. It is also between 0.5 and 30 cm that passive microwave sensors are most receptive to soil moisture. To best detect soil moisture, energy should be collected from 2 or 3 radiometers in different parts of the electromagnetic spectrum between 0.5 and 30 cm (Wilen and Smith, 1996). The broad spatial resolution of most passive microwave sensors, such as the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), the Electronically Scanned Thinned Array Radiometer (ESTAR), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), limits their use to large geographic regions (Martinez and Le Toan 2007). Even if synthetic aperture technology is used, spaceborne passive microwave sensors can still only achieve a spatial resolution of between 10 and 30 km, a scale that is insufficient for regional wetlands mapping (Jackson et al. 1999). This resolution can be partially remedied by the use of spectral mixture models, which can detect subpixel endmembers. Even with spectral unmixing, spaceborne microwave radiometers are still limited by their coarse spatial resolution (Smith 1997). Photo Science and Geoinformatic, two commercial companies, tested airborne passive microwave sensors for their sensitivity to wetlands and also found that spatial resolution was a problem. It was not felt that these sensors would provide improvements over present methods of regional wetland mapping (Wilen and Smith 1996).

5.5. Radar Imaging radars (radio detection and ranging) provide information that is fundamentally different from sensors that operate in the visible and infrared portions of the electromagnetic spectrum. This is primarily due to the much longer wavelengths used by SAR sensors and the fact that they send out and receive their own energy (i.e., active sensors). One of the most common types of imaging radar is synthetic aperture radar (SAR). SAR technology provides the increased spatial resolution that is necessary in regional wetland mapping and SAR data has been used extensively for this purpose. For these reasons the following discussion will focus exclusively on SAR sensors and data. SAR sensors have different operating parameters, including not only multiple wavelengths, but also polarizations and incidence angles. It is important to appreciate differences in these instrument specifications because the selection of optimal wavelength, polarization, and incidence angle is vital for successful wetland mapping (Harris and Digby-Arbus 1986; Baghdadi et al. 2001; Costa and Telmer 2007). Optimal specifications vary based on the goals of the mapping project and the environment in which it is being carried out. Microwave wavelengths commonly used for remote sensing include: X-band (2.4 – 3.8 cm), C-band (3.9 – 7.5 cm), L-band (15 – 30 cm), and P-band (30 – 100 cm; Jensen 2000). Electromagnetic energy transmitted from the SAR sensor towards the surface of the Earth is composed of an electric and a magnetic component. These two components travel, at the speed of light (≈3 x 108 m s-1), orthogonal to one another. The orientation of the electric component of electromagnetic energy (perpendicular to the direction of travel) determines the polarization of that energy. In all but the newest satellite based sensors (i.e., RADARSAT-2 and Phased Array type L-band Synthetic Aperture Radar [PALSAR]) the energy is either transmitted or received horizontally (H) or vertically (V), relative to the surface of the Earth. SAR bands are often described by their wavelength (e.g., X, C, L, or P) and polarization (e.g.,


93

HH = horizontally transmitted and received and VV = vertically transmitted and received). The energy from SAR sensors is also transmitted and received at different angles relative to the Earth’s surface. These incidence angles are measured relative to an imaginary line perpendicular to the surface of the Earth, with smaller angles being closer to perpendicular to the terrain and larger angles being closer to parallel. It is primarily the wavelength, polarization, and incidence angle of the microwave energy in combination with certain key characteristics of Earth’s surface (dielectric property, size/roughness, and structure) that determine the amount of energy reflected in the direction of the sensor and therefore received by the sensor. Water content determines the dielectric property of most natural materials. Typically, the higher the water content, the higher the dielectric constant (a measure of the aptitude of a substance to conduct electrical energy) of the material and therefore the greater the amount of incident energy returned from the material (Jensen 2000). When mapping and monitoring wetland ecosystems, imaging radars have many advantages over sensors that operate in the visible and infrared portions of the electromagnetic spectrum. Microwave energy is sensitive to variations in soil moisture and inundation, and is only partially attenuated by vegetation canopies, especially in areas of lower biomass (Kasischke and Bourgeau-Chavez 1997; Kasischke et al. 1997a; Kasischke et al. 1997b; Townsend and Walsh 1998; Baghdadi et al. 2001; Townsend 2001; Townsend 2002; Rosenqvist et al. 2007; Lang and Kasischke 2008) or when using data collected at longer wavelengths (Hess et al. 1990; Hess et al. 1995; Martinez and Le Toan 2007). The sensitivity of microwave energy to water and its ability to penetrate vegetative canopies, make SAR ideal for the detection of hydrologic features below vegetation (Hall 1996; Kasischke et al. 1997; Kasischke and Bourgeau-Chavez 1997b; Phinn et al. 1999; Rao et al. 1999; Wilson and Rashid 2005). SAR data can even be used to detect freeze/thaw events (Bartsh et al. 2007) because the dielectric of ice is much lower than that of water. The presence of standing water interacts with the radar signal differently depending on the dominant vegetation type/structure (Hess et al. 1995) as well as the biomass and condition of vegetation (Töyrä et al. 2002; Costa and Telmer 2007). When exposed to open water without vegetation, specular reflection occurs and a dark signal (weak or no return) is observed (Dwivedi et al. 1999), making the detection of open water relatively simple. The radar signal is often reduced in wetlands dominated by lower biomass herbaceous vegetation when a layer of water is present due largely to specular reflectance (Kasischke et al. 1997a). Conversely, the radar signal is often increased in forested wetlands when standing water is present due to the double-bounce effect (Harris and Digby-Arbus 1986; Dwivedi et al. 1999). This occurs in flooded forests when energy that is sent out by the sensor is reflected strongly by the water surface away from the sensor (specular reflectance) but is then redirected back towards the sensor by a second reflection from a nearby tree trunk. The use of small incidence angles (closer to nadir) is thought to enhance the ability to map hydrology beneath the forest canopy via increased penetration of the forest canopy (Hess et al. 1990; Bourgeau-Chavez et al. 2001; Toyra et al. 2001). The temporal resolution of available radar data is relatively high for a variety of reasons. Radar sensors can collect data regardless of solar illumination, cloud cover, and most rain events (shorter wavelength [e.g., X-band] radar can be sensitive to intense rainstorms). For example, the use of radar data is particularly helpful in areas of northern Alaska where daylight can be limited and cloud cover prevalent (Evans 1995; Baghdadi et al. 2001). The

94


temporal resolution of SAR data is also increased by the ability of certain satellite borne sensors (e.g., Advanced Synthetic Aperture Radar (ASAR), RADARSAT-1, and RADARSAT-2) to collect data at multiple incidence angles, thus reducing the time between satellite overpasses. The availability of multiple well-calibrated (Martinez and Le Toan 2007) satellite borne sensors further increases the temporal resolution of SAR data because those data can be used to gather information about the same area of interest. Although knowledge of radar data characteristics and processing methods have developed considerably, further research is required in order to capitalize on all of the potential benefits that SAR data can provide to the wetland mapping process (Horritt et al. 2003; Costa 2004). The interpretation of SAR data is less intuitive than that of optical imagery (Silva et al. 2008) and the methods and software used to process SAR data are less developed than those for optical data. In addition, the sensitivity of SAR data to variations in elevation often requires radiometric and geometric corrections that utilize a digital elevation map. Finally, until recently only moderate resolution (30 m) satellite borne SAR data were available, although RADARSAT-2 data are now available (2008) at a spatial resolution of 3 m. Seasat, launched in 1978, was one of the first imaging radars to be used to study wetlands and other ecosystems (Place 1985; Pope et al., 1997; Ramsey et al. 1998). Researchers found that the L-HH microwave energy transmitted by Seasat was particularly sensitive to flooding, even below forest canopies due to the increase in backscatter caused by double-bounce scattering between tree trunks and the flooded surface (Krohn et al. 1983; Place 1985; Hess et al. 1990; Pope et al. 1997). In addition to Seasat, the ability of L-HH SAR to map inundation in forested wetlands has been well documented with a variety of other sensors (Table 2) including AIRSAR (an airborne sensor), Spaceborne Imaging Radar (SIR)-C, and Japanese Earth Resources Satellite (JERS)-1 (available from 1992 – 1998; Ormsby et al. 1985; Hess et al. 1995; Wilen and Smith 1996; Townsend and Walsh 1998; Martinez and Le Toan 2007). After the successful launch of European Remote Sensing satellite (ERS-1) (1991 to 2000) and ERS-2 (1995 to present) and RADARSAT-1 (1995 to present), C-band data were increasingly available (Table 2) and wetland studies using these shorter wavelength data were initiated. Researchers found that although C-HH band radar data were not as well suited for forested wetland studies as those from L-HH SARs, they could be used to monitor inundation patterns, especially in areas of lower biomass (Townsend and Walsh 1998; Townsend 2000; Costa 2004; Lang and Kasischke 2008). C-VV data from the ERS systems (from which the longest continuous record of SAR observations exist) have primarily been used to study herbaceous vegetation (e.g., Kasischke et al. 2003; Grings et al. 2006), but have also been successful in detecting inundation under forest canopies during the leaf-off period (Kasischke et al. 1997b; Townsend 2002). Although SIR-C/X-SAR (deployed onboard NASA’s Space Shuttle) only collected data during the spring and fall of 1994, it provided new details regarding the advantages and limitations of spaceborne multi-wavelength (X, C, and L bands), polarimetric (capable of sending and receiving multiple polarizations at the same time) SAR (Hall 1996). This advancement led to the ability to detect finer details of vegetation structure and therefore improved the accuracy of wetland classifications from space (Pope et al. 1994; Hess et al. 1995; Hall 1996; Smith 1997; Bourgeau-Chavez et al. 2001).


95

Table 2. Commonly used spaceborne SAR sensors Sensor JERS ERS-1 SIR-C X-SAR RADARSAT-1 ERS-2 ASAR

Platform Satellite Satellite Shuttle Shuttle Satellite Satellite Satellite

Resolution Bands Incidence 18m L 39 o 30m C 23 o 10 - 200m X, C, L 20 - 50 o 10 - 200m X 20 - 50 8 - 100m C 10 - 59 o 30m C 23 o 30m - 1km C 15 - 45 o

Terra SAR-X Satellite 1 - 16m Satellite 10 - 100m PALSAR RADARSAT-2 Satellite 3 - 100m

X L C

15 - 60o 8 - 60 o 70 - 60o

Polarization HH VV Polarimteric VV HH VV HH, VV, VH, HV

Width 75 km 100 km 15 - 90 km 15 - 40 km 50 - 500 km 100 km 56 - 400 km

Life-Span Information 1992 -1998 jaxa.jp/index_e.html 1991 -2000 earth.esa.int 1994 jpl.nasa.gov 1994 jpl.nasa.gov 1995 - Pres. space.gc.ca 1995 - Pres. earth.esa.int 2002 - Pres. earth.esa.int

HH, VV, VH, HV 10 - 100 km 2006 - Pres. dlr.de/en Polarimeteric 70 - 250 km 2006 - Pres. jaxa.jp/index_e.html Polarimteric 10 - 500 km 2007 - Pres. radarsat2.info

Please note that specifications are approximate and not all specifications are available at the same time. Sensor name (Sensor), platform type (Platform), spatial resolution (Resolution), wavelength band (Bands), incidence angle (Incidence), polarization, swath width (Width), the years the sensor has operated (Life-Span; Present abbreviated as Pres.), and websites where more information about the sensors can be found are provided in the table.

For example, Hess et al. (1995) and Pope et al. (1994) used SIR-C data to identify various wetland vegetation types and found that the discrimination of these classes was best when using multiple wavelengths and polarizations. Recently SAR sensors have been launched which offer multiple polarizations (ASAR; launched 2002) and even polarimetric data (PALSAR; 2006 to present and RADARSAT-2; 2007 to present). The additional information offered by these sensors is predicted to further improve the utility of SAR data for wetland mapping applications (Pope et al. 1997; Baghdadi et al. 2001; Sokol et al. 2004; Touzi et al. 2007). There are now more opportunities to use and fully explore the potential of SAR data for wetland mapping due to the relatively recent launch of ALOS PALSAR and RADARSAT-2. Both PALSAR and RADARST-2 are capable of providing data with polarimetric data which has been found to improve the detection of wetlands (Horritt et al. 2003; Baghdadi et al. 2001). Additional SAR sensors are currently being planned (e.g., Radar Imaging Satellite). These extra satellites will not only increase the amount of data available for analysis, they will also increase collection frequency, and the variety of polarizations and frequencies available (Ramsey 1997). The launch of additional SAR satellites by the US and other countries will increase SAR capabilities and applications. NASA is working towards the launch of multiple radar satellites, including the Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI), the Soil Moisture Active-Passive (SMAP), the Surface Water and Ocean Topography (SWOT) sensors. The DESDynI and SMAP sensors are scheduled to be developed and launched (2010 – 2013) before SWOT (2013 - 2016). The DESDynI sensor will contain an interferometric L-band SAR (1 km) will limit its ability to map wetlands at a regional scale, its fine temporal resolution (2-3 day) would provide valuable information concerning the provision of wetland ecosystem services as they are influenced by hydroperiod. SWOT will, in part, be designed to inventory freshwater storage in water bodies, such as wetlands. It will contain a ku-band (~2 cm) near-nadir SAR interferometer, a multiple frequency microwave radiometer,

96


and a ku-band nadir-looking radar altimeter (vertical resolution of a few cm). These details and more regarding the sensors discussed above can be found in the US National Research Council (2007) publication entitled “Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond.” In addition to the new SAR satellites planned by the US, the governments of other countries are also planning to launch new SAR satellites in the future. These planned satellites include the Radar Imaging Satellite (RISAT; ≥3m spatial resolution, multiple polarization C-band; India; Misra et al. 2005) and the MultiApplication Purpose SAR (MAPSAR; ≥3m spatial resolution, multiple polarization L-band, Germany and Brazil; Schröder et al. 2005). Radar not only has potential to detect different types of wetlands; it can also be used to study the condition and function of these valuable areas. Recent advances in radar remote sensing data and processing have made the estimation of biomass and other forest parameters possible on a landscape scale (Le Toan et al. 1992; Kellndorfer et al.1998; Kasischke et al. 1997b; Mougin et al. 1999; Martinez and Le Toan 2007). The ability of radar to map flooding under forest canopies, where optical data would not be able to detect inundation, is particularly promising (Kasischke and Bourgeau-Chavez 1997). Although microwave sensors can currently only be used to estimate soil moisture within the first 10 cm of soil, new models are being developed to help extend this estimate (Li et al. 1998). Additionally interferometric techniques can be used to create extremely accurate digital elevation models using radar imagery. These models have been used to monitor wetland vegetation height and biomass (Simard et al. 2006) and the impact of management practices on wetland hydrology (Wdowinski et al. 2008). Methods that use a combination of different bands and polarizations or synergistic approaches that use imagery from multiple radar instruments as well as optical data often provide superior results (Sahagian and Melack 1996; Smith 1997; Augusteijn and Warrender 1998; Töyrä et al. 2002; Li and Chen 2005; Töyrä and Pietroniro 2005) because they bring different pieces of information to the process. Decision tree analysis is a good option when combining different types of SAR (Baghdadi et al. 2001) or SAR and optical data (Li and Chen 2005). The use of multi-temporal radar data combined via an intensity, hue, and saturation (IHS) transformation has also been found to improve wetland mapping (Kushwaha et al. 2000).

5.6. Lidar Similar to radar sensors, lidars (light detection and ranging) are also active sensors, sending and receiving energy produced by the sensor. However, lidars use energy with much shorter wavelengths (VIS and near-infrared) than radars (microwave). There are multiple types of lidar data including waveform and more readily available discrete point return lidar. Waveform lidars sample the entire laser pulse return (echo) whereas discrete point lidars only record a certain number returns (e.g., 2 or 4). This discussion will focus on discrete point return lidar data which are often available from state or local governments (Roger Barlow – personal communication; Vierling et al. 2008) or are readily available from commercial mapping companies (Rosso et al. 2006). The vast majority of lidar data used to inform wetland mapping has been discrete return lidar data. However, it should be noted that waveform lidar technology is rapidly advancing and waveform lidar data are presently being


97

collected by some companies. Therefore the use of waveform lidar for wetland mapping and monitoring purposes will likely increase in the future. Lidars emit short pulses of energy, often from the blue-green (bathymetric lidar) or the infrared portion of the electromagnetic spectrum (standard for terrestrial applications), and these pulses illuminate very small portions of the land’s surface (Goodwin et al. 2006; Vierling et al. 2008). A recent survey of airborne lidar sensors (Lemmens 2007) found that most terrestrial lidar sensors operate in the 900 to 1550 nm range. Lidar data can be used to calculate highly accurate x,y,z locations through the use of an onboard Global Positioning and Inertial Navigation System and by calculating the distance to an object by recording the amount of time it takes for an emitted pulse, or a portion of that pulse, to return to the sensor (Vierling et al. 2008). Discrete point lidars usually detect between two and four returns per laser pulse and the strength of those returns is largely determined by the types of materials that the pulse interacts with on the ground (similar to all optical data). For example, pure water strongly absorbs near-infrared energy and green leaves strongly reflect near-infrared energy. Therefore the intensity of near infrared lidar returns from water are normally weaker than those coming from a tree canopy. Lidar derived digital elevation maps (DEMs) are often used to enhance wetland mapping based on optical (Lichvar et al. 2006; Vierling et al. 2008) data, radar data, or both (Li and Chen 2005; Töyrä and Pietroniro 2005). In this way, landscape position (e.g., slope, depression, or peak) can be made part of the wetland mapping process similar to the information provided by stereoscopic viewing of aerial photographs. This additional information can aid in the detection of wetlands which are normally difficult to identify, such as vernal pools (Lichvar et al. 2006). Although topographic information is commonly available (e.g., photogrametrically derived data and USGS topographic maps) for the United States, the spatial resolution of these data is often not sufficient for wetland identification, especially in areas of subtle topographic change. In general conventional, non-lidar derived DEMs have much coarser vertical accuracies (1 – 10 m) than those derived from lidar (15 cm – 1 m; Murphy et al. 2007b). Lidar derived DEMs can be used to detect subtle variations in topography (Töyrä and Pietroniro 2005) that are often not visible when using the finest resolution optical data. Although the automated processes typically used to analyze digital data do not benefit as directly from the judgment of the analyst as they do in manual photogrammetry, digital DEMs can be used to estimate wetness indices based on slope and contributing area (Tenenbaum et al. 2006; Murphy et al. 2007b). In this way the lidar data are being used to provide some of the same information that is often ascertained by photointerpreters via stereoscopic analysis. Areas with higher wetness indices are more likely to contain wetlands (Murphy et al. 2007b). In addition, DEMs can be used to ascertain information on the functions that wetland serve by providing information on hydrologic connectivity and surface flow pathways (Sorensen et al. 2006; Murphy et al. 2007a). However, lidar provides information not only on elevation, but also on the intensity of the lidar return. The intensity of the lidar return may be helpful for estimating wetland hydrology (Brzank et al. 2004) below the surface of vegetative canopies, provided those canopies have gaps at the time when the lidar data were collected. The increasing availability of lidar sensors that use various wavelength lasers (Lemmens 2007), likely means that proper instrument selection will be increasingly important in a wetland mapping process. The relationship between laser wavelength and water absorption features in the near infrared region maybe critical. Although lidar derived DEMs and other lidar data provide

98


opportunities for improved wetland mapping, these data do have restrictions. Similar to radar, the processing methods for lidar vary significantly from those developed for more traditional optical sensors (e.g., multispectral). A large reason for this is the fact that lidar data is often presented as point clouds which must then be interpolated to create DEM grids. Software has been developed to process these point data, but often it must be purchased separately from software developed to analyze other types of remotely sensed data. In addition, lidar data should be collected to different specifications based on their application and data collected for one application may not be suitable for another. For example, higher point densities may be needed from mapping in forested areas of relatively subtle topographic change. Point density can be improved by increasing the number of pulses, flying the platform at a lower altitude, decreasing the beam divergence angle, or simply collecting data over the same area twice (Goodwin et al. 2006). Collecting data from high platform altitudes (>3000 m) may compromise the ability of the dataset to resolve the Earth’s surface below forest canopy by weakening the return signal since only signals above a certain threshold level are detected (Goodwin et al. 2006). The utility of lidar data is also limited in certain vegetation types. For example, Rossso et al. (2006) found that lidar could not be used to accurately map ground elevation under dense herbaceous vegetation. Some researchers have found that the accuracy reports provided by commercial vendors are insufficient (Rosso et al. 2006). Finally, although the price of obtaining and processing commercial lidar should decrease in the future, it is still relatively high (Vierling et al. 2008).

CONCLUSION In the past, researchers have debated as to whether spatial or spectral resolution is more important to the mapping of wetlands. It appears from available research that a combination of spatial and spectral resolutions is needed to map wetlands with remotely sensed data. The necessary spatial resolution, of course, depends upon the size of the wetland patch that is being mapped. For example, as a general rule, it takes an area three by three pixels wide to identify an object on the ground. The more similar a cover type is to adjacent cover types, the larger the area that will be needed (Federal Geographic Data Committee 1992). Although a minimum spatial resolution is needed to map wetlands, spectral information is also vital, especially when mapping wetlands with indistinct spectral signatures, such as forested wetlands. It is this optimal combination of spatial and spectral resolution that is necessary to best map wetlands. If available, the incorporation of finer resolution optical data into the mapping process can be used to effectively increase the spatial resolution of coarser resolution optical imagery (Dymond and Shepherd 2004). This process, often called pansharpening when panchromatic data are used, increases the ability to detect smaller wetlands. Different types of data are sensitive to different components of the landscape. These components (e.g., soil moisture, presence or absence of standing water, biomass, vegetation height, cellular structure, and more) can then be synthesized to compose a superior wetland map. This data fusion is aimed at reducing classification error by incorporating more spectral information. Since radar and optical data are sensitive to very different landscape characteristics, the combination of radar and optical data can significantly improve wetland mapping (Sahagian and Melack 1996; Kushwaha et al., 2000; Ramsey et al. 1998; Lyon and


99

McCarthy 1995; Töyrä et al. 2002; Silva et al. 2008) and provide a superior land cover map (Rignot 1997; Ramsey et al. 1998). Optical-radar fusion has been shown to improve the accuracy of land cover mapping by as much as 10% (Haack and Bechdol 2000). Radar data not only bring to classifications information on inundation, plant structure, and biomass; the data have also been found to improve soil moisture estimation, even in forested wetlands (Lang and Kasischke 2008). Although optical sensors may be sensitive to soil moisture, some researchers believe that they are less suitable for this application due to their relatively low signal to noise ratio (Neusch and Sties 1999). Multispectral and hyperspectral data do, however, provide superior information concerning the identity of vegetation communities derived mainly from the molecular and cellular structure of the plants. The potential of optical-radar fusion for wetland mapping is furthered by recent increases in the availability of multiple wavelength (e.g., launch of Radarsat-2, PALSAR, and Terra SAR-X) and multiple polarization radar data and the improvement of the spatial resolution of both optical (e.g., Quickbird, 2.4 m) and radar (i.e., Radarsat-2, 3 m) data. The availability of radar data and techniques to process radar data have improved to the point that the Canadian government is using SAR data as part of their national wetland mapping program (Milton et al. 2003; Li and Chen 2005). The US government (NASA) is currently working towards the launch of new radar satellites (e.g., DESDynI, SMAP, and SWOT), as are the governments of other countries including Germany and Brazil (MAPSAR) and India (RISAT). Companies currently collecting fine resolution optical data are planning to expand their capabilities which will make the acquisition of these datasets during optimal time periods easier. For example, DigitalGlobe which currently provides QuickBird data is expanding their data delivery options with the addition of WorldView-1 (0.5 m pachromatic) which was launched in fall of 2007 and WorldView-2 (0.46 m panchromatic and 1.8 m 8 band multispectral data) which is scheduled to be launched in early 2009. The combination of the three DigitalGlobe satellites will provide daily 75%) was obtained by both planted and control treatments. The PO43- concentration curves of both treatments were similar to each other and measured PO43- concentrations of both treatments were lower than those values estimated by first order exponential regression model. Also there was less variance in P removal between planted and control treatments when compared to N removal. Thus, P removal may obviously be occurred by physicochemical processes. Most P is believed to be stored in the media bed, rather than in the plant. Since the media bed used in this study was a mineral sand that contained oxides of Fe, Al, and Ca, it could enhance the effect of P retention by chemical adsorption and precipitation in CWs (Arias et al., 2001; Yang et al., 2001). Non-planted reactors were still slightly less efficient at removing PO43- than those containing plants. It implied that biological P removal was also taking place in the PRs. Plants, algal, and microorganisms all utilize P as an essential nutrient, and contain P in their tissues though the portion of tissue P is very small compared with C and N (Brix, 1994; Kadlec, 1999; Silvan et al., 2004). Although, P sorption by media bed was quantitatively important for P removal in treatment wetlands, it was a limited process because the adsorption capacity was dependent on the quantity of mineral oxide contents in the beds and as soon as all sorption sites were occupied, no additional P removal due to adsorption could occur. Then, plant uptaking P and subsequent harvesting may also play an important role for sustaining long-term P removal (Richardson and Craft, 1993; Lantzke et al., 1999; Meuleman et al., 2003). c. Nutrient Removal Rates Iamchaturapatr and Rhee (2007) used two physical plant-characteristics, area covered by plants and plant’s weight for evaluating nutrient removal rates of aquatic plants in treatment wetland. They found that the N and P removal rates differed greatly and significantly between plant species and the method chosen between area-based and weight-based calculations (Table 3). With such variation in the results, the choice of method could have important consequences for selecting the appropriate plants and evaluating the performance of various plants for treatment wetlands. However, it is not in all cases that consideration of only one metric unit is the better method for evaluating plants’ performance. The floating plants had predominant N and P removals per weight of the plant when they were compared with the emergent plants while they also had an obviously low N and P removal efficiencies when the area occupied by plants was considered. There was also evidence that some aquatic plants such as T. dealbata and C. alternifolius, they were only a few differences of nutrient removal rates when their removal rates were compared by area-based calculation, but they were large differences when their removal rates were compared by weight-based calculation. Furthermore, several plants such as Z. latifolia, C. alternifolius, and S. triqueter preserved the high ranking of nutrient

Phytoremediation Processes for Water and Air Pollution Control…

333

removal rates among the plants for both area-based and weight-based calculations. According to these data, considerations of both area coverage by plant, aboveground/belowground biomass and plant specific area-density (plant area coverage per plant weight) were necessary for an effective plant selection and design of aquatic plant treatment systems. Table 3. Rates of nutrient removal by aquatic plants

Species A. calamus A. plantago-aquatica C. alternifolius D. fragrans D. sanderiana E. crassipes J. effusus I. ensata I. pseudacorus I. setosa P. japonica P. stratiotes P. asiatica S. radicans S. triqueter S. stoloniferum S. wallisii T. dealbata T. japonica T. latifolia Z. latifolia

Area-based calculation (mg N-P m-2 d-1) Nitrogen Phosphorus 246 ± 14 185 ± 1 328 ± 129 330 ± 55 728 ± 60 743 ± 61 395 ± 135 399 ± 28 306 ± 116 257 ± 16 46 ± 6 41 ± 5 822 ± 208 621 ± 30 354 ± 175 300 ± 1 465 ± 53 275 ± 53 579 ± 57 233 ± 59 716 ± 43 420 ± 38 105 ± 9 62 ± 5 151 ± 37 72 ± 22 382 ± 65 206 ± 40 598 ± 150 468 ± 81 648 ± 236 493 ± 110 176 ± 83 164 ± 7 633 ± 102 590 ± 76 85 ± 18 75 ± 6 754 ± 194 565 ± 28 1341 ± 144 1191 ± 129

Biomass-based calculation (mg N-P kg-1 d-1) Nitrogen Phosphorus 8±0 6±0 33 ± 13 33 ± 5 35 ± 3 36 ± 3 24 ± 8 24 ± 2 6±2 5±0 31 ± 4 28 ± 4 24 ± 6 18 ± 1 11 ± 6 10 ± 0 11 ± 1 6±1 15 ± 1 6±2 21 ± 1 12 ± 1 36 ± 3 21 ± 2 54 ± 13 26 ± 8 42 ± 7 23 ± 4 39 ± 10 30 ± 5 22 ± 8 16 ± 4 17 ± 8 16 ± 1 11 ± 2 10 ± 1 68 ± 15 60 ± 5 16 ± 4 12 ± 1 55 ± 6 49 ± 5

Values represent mean ± SD of three replicates (After: Iamchaturapatr and Rhee, 2007).

The capacities of N and P removals by various wetland treatment systems from available literature data are presented in Table 4. The removals of N and P are affected by wetland designs and wastewater characteristics. It is also likely to vary from one wetland to another. Several reasons could be accounted for such variation including (1) Site location and local weather; although it can not be typically controlled when selecting a specific site for a CW or NW project but it can affect the type and size of wetland that will be used.

Table 4. Removal of nutrients in wetland treatment systems Location

Application

Canada

FWS SSF HSSF FWS VSSF HSSF HSSF VFS FWS VSSF FWS VSSF HSSF VFS FWS VSSF

Czech Republic Germany

Korea Spain The Netherlands Turkey Uganda USA

Nutrient removals mg N m-2 day-1 mg P m-2 day-1 1942-3283 791-800 626-1179 967-1146 950 130 1186-1659 296-345 360-2651 113-411 860-3230 240-1130 720-1981 280-615 46-1341 41-1191 931-1612 857-1713 214 22 75-723 (-72)*-38 3310-7178 241-585 970-2180 50-1280 267-6473 73-1989 362 128 179-527 30-53

References Prystay and Lo (2001)a Vymazal (2002)b Luederitz et al. (2001)a Schulz et al. (2003) Yoon et al. (2001)b, Ham et al. (2004)b Iamchaturapatr and Rhee (2007) Ansola et al. (1995)c Meulemen et al. (2003) Toet et al. (2005)b Ayaz and Akça (2001)d Okurut et al. (1999), Kyambadde et al. (2005) Kyambadde et al. (2004) a Newman et al. (2000)b Coleman et al. (2001)a

Note: HSSF and VSSF are the abbreviation of horizontal and vertical subsurface flow constructed wetland. a Values are calculated by influent and effluent concentrations or percent removal efficiencies, water flow rate and wetland area. b Values are calculated by subtracting between influent and effluent loading rates. c Values are calculated for nitrogen and phosphorus loading rates of 4000-8000 mg N m-2 day-1 and 400-800 mg P m-2 day-1. * Negative values mean that the system produces rather than removes the pollutants.


335

Seasonal temperature ranges are the most critical determinant because they can greatly affect the biological conditions of aquatic plants and microorganisms living in wetland systems. (2) Soils and geology; types and properties of soils or media beds used in the treatment wetlands may different among wetland treatment systems, thus they could affect sorption capacity, chemical and biological reactions involving nutrient removal processes and (3) Characterization of wastewater to be treated; since one of the reasons that resulted in the narrower range of N removal rate by Iamchaturapatr and Rhee (2007) in comparison with other studies might be due to the different wastewater sources applying to the wetland. Wastewater quality varies widely among municipal, industrial, and agricultural water categories. Different wastewater sources have unique mixtures of potential pollutants so that even a single wastewater source category, such as municipal wastewater may vary depending on local and in some cases, site-specific environments (Kadlec and Knight, 1996). The authors believe that the importance of this variable is due to the quantity and quality of organic carbon (Org-C) supply, which influences the activity of denitrifying organisms to remove N by treatment wetlands and the wetland can be Org-C limited when receiving high NO3- loads (Gale et al., 1993; Ingersoll and Baker, 1998; Lin et al., 2002). In their experiment, N source consisted totally of only NO3- form and Org-C in raw water was absent. Therefore, the N removal might be apparently limited by the Org-C availability for denitrification.

3.3.2. Formation of Nitrogenous Chemical Species (Ammonia and Nitrite as Nitrogen) Figure 4a illustrates the formations of NH4+ produced by planted and control treatments. The concentration of NH4+ distinctly increased and linearly functioned with retention time during Days 1-3 for all the treatments. The occurrence of NH4+ was highest in the first week of an experiment and was pronounced in the treatment with aquatic plant application. The formation of NH4+ may derive from ammonification (mineralization) of organic matters in the soils (Narteh and Sahrawat, 2000; Kwabiah et al., 2001), biotic and abiotic processes of NO3reduction by microorganisms and soil chemical interactions (Hansen et al., 1996; Ma and Aelion, 2005; Revsbech, 2005), and atmospheric dinitrogen (N2) fixation (Hurd et al., 2001). The distinction of NH4+ quantities produced by planted treatment and control treatment might be due to the different amounts of residual organic nitrogen (Org-N) in the media beds. The high NH4+ concentrations were found by planted treatment to be due to the decomposition of planted materials (i.e., leaves, stems, and roots) and residual organic matters of soil origin, while NH4+ production by control treatment might result from the decomposition of residual organic matters of only soil origins. The reduction of NH4+ concentrations may mainly occur by plant assimilation and microbial nitrification process (Cronk and Fennessy, 2001; Kyambadde et al., 2005). The NH4+ removal through volatilization of unionized ammonia (NH3) may be ignored because a maximum NH3/NH4+ ratio is only accounted for about 0.013 (or 1.3%) by planted treatment and 0.025 (or 2.5%) by control treatment. These values are calculated by applying pH of 7.4 and 7.7 for planted and control treatments (see Figure 5b, the temperature equals to 25 oC and equilibrium constant (K) equals to 10-9.3 (Stumm and Morgan, 1996). Figure 4b illustrates the formations of NO2- produced by planted and control treatments. The measured bulk liquid DO concentration in PRs (see Figure 5a) may not represent the actual DO concentration within the soil layer. The deeper layers of media bed are the lower DO concentration.

336

Jae Seong Rhee, Yong-hui Song, Fasheng Li et al.

Figure 4. Occurrences of nitrogenous chemical species concentrations by 21 aquatic plants growing in planted reactors and controls (without plantation): (a) Ammonia as nitrogen (NH4-N) and (b) Nitrite as nitrogen (NO2-N). The points represent means ± SD of three replicates.

Existing DO gradient concentration in wetland soils has been measured by O2 microsensors and reported by several studies. Davidsson et al. (1997) found that DO levels (about 9.6 mg/L) of sandy and peaty soils were completely reduced to zero when soil depth was > 7.5 cm. Arth and Frenzel (2000) found that an active nitrifying zone occurred in the surface water layer of planted reactor growing Oryza sativa and in a depth of 2-3 cm in the soil, while a denitrifying zone occurred at a distance 0.2-0.5 cm from rooted surface. There is also accumulating evidence that some organisms also denitrify aerobically (Kornaros and Lyberatos, 1997; Joong et al., 2005). Thus, denitrification can occur in the relatively low O2 layer and bottom of the media bed, while nitrification can occur in aerobic regions of the water suspension, top bed layer between soil and water interfaces, and the surface around individual plant roots, creating simultaneous nitrification-denitrification processes in treatment wetlands (Mitsch and Gosselink, 2000; Bastviken et al., 2003; Revsbech et al.,


337

2005; Kremen, et al., 2005). Additionally, nitrification is pH-sensitive and rates decline significantly at pH < 6.8. A pH value of 7.0-8.0 is recommended to maintain reasonable nitrification and denitrification rates (Metcalf and Eddy, 2003), since pH values (see Figure 5b) were within a range that could well support both nitrification and denitrification processes. The rise of NO2-N concentrations which is commonly used to indicate potential of nitrification-denitrification processes (Metcalf and Eddy, 2003; Kyambadde et al., 2005) shows the obvious distinction between planted and control treatments. The PRs contained plants had more NO2- production than a control treatment.

Figure 5. Daily dissolved oxygen (DO) concentrations and water pH by 21 aquatic plants growing in planted reactors (PRs) and controls (without plantation): (a) DO values and (b) pH values. The points represent the means ± SD of three replicates. (After: Iamchaturapatr and Rhee, 2007).

338


Aquatic plant has several functions to facilitate microbial activities in treatment wetlands by plant roots serving large surface area for biofilms and depositing C contents, CO2 and O2 into the rhizosphere (Gopal, 1999; Cronk and Fennessy, 2001; Sundaravadivel and Vigneswaran, 2001; Stottmeister, et al. 2003). Thus, planted treatment could accelerate both the nitrification and denitrification rates resulting in high NO2- production, while NO2production in the control treatment was limited due to the fact that autotrophic nitrifying organisms could use only dissolved CO2 diffused from the atmosphere as C supply for nitrification and this reaction could only take place in water surface suspension and oxidized bed layer where DO was still available for nitrifying organisms (Arth and Frenzel, 2000). Although both nitrification and denitrification occur at reduced rates as indicated by the DO effects described for both processes, if sufficient retention time exists, the overall N removal can be significant (see Figure 3a).

3.3.3. Oxygen and pH The physical and chemical environment of a wetland affects all biological processes. In turn, many wetland biological processes modify this physical/chemical environment. Two of the most important factors are dissolved oxygen (DO) and pH. Oxygen (O2), although abundant in the atmosphere (approximate 21% of atmospheric gases by volume), has a limited solubility in water. It is frequently a limiting factor for the growth of plants in microorganisms in wetlands. Water entering the treatment wetland may or may not contain carbonaceous and nitrogenous oxygen demand (CBOD and NOD). After entering the wetland, several competing processes affect the concentrations of O2, biological oxygen demand (BOD), and N species (Figure 4). Iamchaturapatr and Rhee (2007) shown that planted treatment had a negative effect to DO concentrations of treated water. The time-dependent DO concentrations in planted and control treatments are illustrated in Figure 5a. Both the planted and control treatment showed rapid depletion of available DO concentrations at an early stage of the experiment. The reason was that the biodegradation of residual organic and planted matters in media beds of control and planted reactors caused high oxygen demand (OD), and the system could not supply the OD of soil aerobic organisms under most circumstances. When the demand was higher than supply, the DO concentration was depleted. Then, DO concentrations gradually improved. The increasing in DO concentrations might result from complete/partial oxidation of residual organic matters by microorganisms in media beds decreasing biological oxygen demand (BOD), DO concentrations were refilled by molecular diffusive reaction of O2 from the air and O2 transfer by plants (Brix, 1994, Mitsch and Gosselink, 2000; Bezbaruah and Zhang, 2004a). Planted treatments clearly showed the lower levels of DO concentrations when compared with unplanted treatment along the retention time. Several reasons could be used to explain this phenomenon (Gopal, 1999; Kyambadde et al., 2004; Thullen et al., 2005): (1) the aerobic degradation of planted materials inside planted reactors might consume a large amount of O2 in comparison with control reactors. (2) Plant shading in the planted reactors inhibits the growth of suspended photosynthesis organisms resulting in almost nil or reduced O2 production of photosynthesis organisms in the water. (3) The plant above ground bodies limited a contact between atmospheric phase and water phase, resulting in low O2 transfer from air to the water. Contrary to the control reactors, they were freely open-surface reactors, a contact between atmospheric phase and water phase was larger than the planted reactors,


339

resulting on high O2 transfer from air to water. (4) NOD for nitrification process in planted reactors might probably be higher than in control reactors (see also Figure 4, planted treatments appeared more NH4+ and NO2- formations than control treatments). It was estimated that about 3.22 g of O2 were consumed per gram of NH4-N oxidized and 1.11 g oxidation of O2 were consumed per gram of NO2-N oxidized (Kadlec and Knight, 1996). However, it is not in all cases that planted treatment has negative effect on the available DO level of treated water. It has also been reported by many studies that planted treatments not only improved the DO concentration of treated water, but also displayed positive results over unplanted treatments (Urbanc-Bercic, 1994; Coleman et al., 2001; Ham et al., 2004). Wetland plants are reported to have different abilities for O2 transfer to water-soil mediums (Brix, 1993; 1994). An ability of the plants to release O2 through the rhizosphere of wetlands is well documented and reported in the range of 0.001-12 g O2 m-2 d-1 (Brix and Schierup, 1990; Armstrong et al., 1990; Sikora et al., 1995; Bezbaruah and Zhang, 2004a). Bezbaruah and Zhang (2004a) suggested that the wide range of planted O2 release intensities and this may occur due to the difference of experimental conditions such as methods for measurement and calculation of O2 release rate, plant specific parameters (i.e., species, planted mass and age), locations (i.e., temperature and weather conditions), and water-soil properties (i.e., OD, redox state, pH and availability of nutrients). Hydrogen ion concentration, measured as pH, influences many biochemical transformations. It influences the partitioning of ionized and un-ionized forms of acids and bases and controls of solubility of many gases and solids. Figure 5b illustrates the changes of pH in planted and control treatments by considering retention time. The water pH of 21 aquatic plants growing in planted reactors was found within the range of 6.7-7.7 while control reactors showed the pH values in the range of 7.3-8.1. Unlike the change of DO concentration, the change of water pH was of insignificant magnitude, but the observation suggested that the planted treatment gave a slightly negative effect to pH of treated water. The control treatment showed slightly higher pH values than the planted treatment along retention time, a result similar to other wetland studies observed by EPA (1999), Coleman et al., (2001) and Lin et al. (2002). The reason for rapid reduction of pH in planted treatment at an early stage of the experiment might be the formation of dissolved CO2 and carbonic acid (H2CO3) in water by the degradation of organic compounds (residual organic matters in media beds and planted materials) of aerobic organisms resulting in pH reduction (Kadlec and Knight, 1996; Coleman et al., 2001, Kyambadde et al., 2004). Additionally, the abilities of plants to induce the decrease of soil pH are reported from hydrogen ion (H+) excretion by the root uptake of cations (i.e. NH4+, metals) coupled with root exudation of organic acids and release of CO2 from the root respiration (Hinsinger, 1998; Rao et al., 2002; Bezbaruah and Zhang, 2004b). However, it is not only the biological functions of plants that have been reported to alter the soil pH but functions of soil-microorganisms, types of soils and its compositions are also reported to affect soil pH. Other reviews describe the effect of several chemical reactions on the contribution of acidic and basic soil reactions. Inorganic and organic acids produced by the decomposition of soil matters are common soil constituents that may affect soil pH; Nitrification is the additional biochemical source for H+ while occurrence of denitrification produces alkalinity; the loss of H+ ions from cation exchange reactions occurs when the H+ ions of an organic acid are exchanged with other cations in the wetland soil such as calcium ion (Ca2+), ferrous ion (Fe2+), potassium ion (K+), magnesium ion (Mg2+) and manganese ion

340


(Mn2+), and Sulfate (SO42-) reduction in anaerobic condition increases alkalinity by producing OH- ions, while the oxidation of sulfur compounds increases acidity by producing H+ ions (Haynes, 1990; Vile and Wieder, 1993; Risgaard-Petersen and Jensen, 1997; Kim, 1998; Gilliam et al., 1999).

4. CASE STUDY: CONSTRUCTED WETLANDS IN THE LAKE OF HONGHU PARK, CITY OF SHENZHEN Shenzhen city, at the central coastal area in southern Guangdong Province, is the passageway from mainland China to Hong Kong. It has a total land area of about 2000 km2. Since the mid-1980s, Shenzhen has been growing rapidly from rural land to an important industrial city, and becoming one of the special economic zones in China. Intensive human activities and land-use change have dramatically affected the regional water environment. Water shortage, flooding and water pollution became more serious in the process of urbanization.Lake of Honghu Park is located in the downtown area of Shenzhen city (Figure 6).

Figure 6. Map of Honghu Park in Shenzhen city. The points indicate the location of water sampling for water quality analysis. S1 is the effluent water from constructed wetlands. S2, S3 and S4 are the entrance of 1st, 2nd and 3rd lake, respectively. S5 and S6 are the middle and end of 3rd lake.


341

The lake received the water from heavily polluted Buji River as supplement. The lake itself has almost no other water resources. Direct water irrigation from Buji River increased nutrient loads of the lake resulting on severe lake-eutrophication. Since 1997, the aquatic plant treatment system was built and aimed to treat the water from Buji River, and provide the following lake system with treated water (Figure 7). The system initially contained three cells of artificial wetland on land area of about 2200 m2 with designed capacity about 1000 t d-1. In 2004, the system was upgraded and expanded for treatment capacity by installing further six cells of wetland in land area about 7900 m2. The overall total treatment capacity was 5000 t d1.

Figure 7. Constructed wetlands in Honghu Park in Shenzhen city, China: (a) General view, (b) Various aquatic plants included Cannas sp. and Phragmites sp. and (c) Effluent from treatment wetland flowed to the Lake of Honghu Park.

342


Figure 8. Water quality in Lake of Honghu Park in 2006: (a) Water pH, (b) Dissolved oxygen (DO) concentrations, (c) Chlorophyll a (Chl a) concentrations, (d) Ammonium as nitrogen (NH4-N) concentrations and (e) Phosphate as phosphorus (PO4-P) concentrations. (S1 is the effluent water from constructed wetlands. S2, S3 and S4 are the entrance of 1st, 2nd and 3rd lake, respectively. S5 and S6 are the middle and end of 3rd lake.).

There are three lakes in Honghu Park. The first and second lakes have water depth about 0.5 to 1 m, and water retention time of about 2 and 6 d, respectively. The third lake has water depth about 1-1.5 m and water retention time of about 45 d. Water flows from the first lake to the second and the third one, following the same direction as the water-flow of Buji River. Figure 8 shows the water quality in the Lake of Honghu Park in 2006. It has shown that installation of wetland treatment system could reduce the nutrient loads and eutrophic state in


343

the Lake of Honghu Park. In addition, the lake itself was served as natural treatment. The concentrations of NH4-N and PO4-P were dramatically reduced after the water was passed through 3rd lake.The water quality of the lake was improved and met standard of Grade 2-3 of surface water quality in China.

5. CARBON DIOXIDE SEQUESTRATION BY PHYTOREMEDIATION PROCESS One of the major environmental concerns of today is the excessive pollution of greenhouse gases, especially CO2 gas. It is now generally accepted that limits will have to be placed on the atmospheric concentration of CO2 in the atmosphere (IPCC, 2007). World total CO2 emissions and atmospheric CO2 concentrations is demonstrated in Figure 9. The anthropogenic emissions of CO2 have sharply increased from the mid-20th century to the beginning of the 21st century by approximately 400%. Consequently, atmospheric levels of CO2 have increased over the same period from an estimated about 310 ppm to present level of approximately 386 ppm with remarkably annual growth rate of about 2 ppm yr-1 (NOAA, 2008; WRI, 2008). There are several approaches to managing the levels of CO2 emitted into the atmosphere. The first is to increase the efficiency of energy conversion. A second approach is to use energy sources that are lower in carbon (C) or C-free. One of the most understudied approaches is CO2 capture and sequestration (CCS). The CCS technologies can be used to manage emissions from both point and non-point sources and can be used in conjunction with other C management methods.

Figure 9. World total carbon dioxide (CO2) emissions and its atmospheric concentrations: (1) Total yearly CO2 emission (WRI, 2008). (2) Atmospheric CO2 concentration (all data before 2004 are from WRI (2008), while the data from 2004 to present were from NOAA (2008)). (3) Annual CO2 growth rate (NOAA, 2008).

344


To be successful, the techniques and practices to sequester C must meet the following requirements: (1) be effective and cost-competitive; (2) provide stable, long-term storage; and (3) be environmentally benign. This section provides an assessment of phytoremediation process for sequestrating CO2, which is at the same time readily accessible and provides the reader with sufficient information on phyto-research for CO2 removal and their possible future development.

5.1. Plants as Biological Tool for Carbon Dioxide Sequestration Recently, there are several proposed types of CCS technology included ocean sequestration such as deep ocean injection or increasing the amount of CO2 dissolved in the ocean. Although this technology can store a large quantity of CO2 into the ocean, the primary concern with this approach is that highly CO2 concentration released into the ocean will cause acidification and potential ecological damage, therefore further research is required to obtain the necessary information and, eventually, a practical demonstration of the safety and feasibility of the method (Auerbach et al., 1997; Tamburri et al., 2000; Huesemann et al., 2002). Another proposed technology is storage of CO2 in geological formations such as use depleted oil and gas formations to store CO2, formation of sandstones by injecting CO2 into deep saline aquifers and methane (CH4) recovery by replacing CO2 into coal-beds. In this approach, the storage of CO2 may differ according to the specific geological information in each site. Thus, the verification of the suitable location as a disposal site and underground monitoring of leakage CO2, assessment of local health or ecological impacts are necessary (Hanisch, 1998; Bruant et al., 2002; Anderson, 2004). Mitigation of CO2 by phytoremediation process is another option being study. Enhancing the natural processes that remove CO2 from the atmosphere is thought to be one of the costeffective means of reducing atmospheric levels of CO2. Green plants in both terrestrial and aquatic ecosystems withdraw CO2 from the atmosphere by photosynthesis process. Carbon sequestered from the atmosphere is stored in plant fiber (above and below ground) for extended periods of time. High atmospheric CO2 levels enhanced plant productivity generally and, consequently, the rate at which CO2 is removed from the atmosphere (Hollinger et al., 1995; Schulze et al., 1995 and Burton, 1997). The amount of CO2 removed from the atmosphere each year by photosynthetic organisms is massive. It was referred by Gifford et al. (2000) that the minimum gross fixation of CO2 by green plants was about 120 Gt C yr-1. Kim et al. (2004) stated that wetland ecosystem including peat-forming wetland stored a vast C of 455 Pg C. This represents 20-30% of the world’s pool of soil organic C and is comparable to the total C in the atmosphere as CO2. Since the increase of plant biomass resulted by elevated CO2 supplementation could suggest that more pollutants be taken up from the contaminated growth media. Several literature reviews proposed the benefits of elevated CO2 for phytoremediation process, for example, Tang et al. (2003) grew two plant species, sunflower (Helianthus annus L.) and Indian mustard (Brassica juncea L. Czern.) in copper (Cu) contaminated soils at different atmospheric CO2 levels between 350-1200 ppm. At the end of their experiment, the plants were harvested and measured the concentrations of Cu in their leaves, stems and roots. The bioaccumulation factor (BF) was calculated as the ratio of Cu concentration in leaves to the


345

Cu concentration in the soil. For sunflower plants, the ratio of the observed BFs at 800 ppm CO2 to those observed at 350 ppm CO2 (BF800/350) was 3.4 in natural soil, 10.9 in soil containing 100 mg Cu per kg soil, and 4.2 in soil containing 200 mg Cu per kg soil, and the ratio of observed BFs at 1200 ppm CO2 to those observed at 350 ppm CO2 (BF1200/350) was 1.2, 3.8 and 2.6, respectively. For Indian mustard plants the results were even more spectacular. The BF800/350 ratios were 14.5 in natural soil, 39.5 in soil containing 100 mg Cu per kg soil, and 2.5 in soil containing 200 mg Cu per kg soil, while the similar ratios of BF1200/350 were 5.4, 17.8 and 1.2, respectively. Hence, they concluded that use of CO2 fertilizer for triggering hyper-accumulation in plants, and increasing biomass production, could open up the way for enhanced phytoremediation and for phytomining. Although, successful performance of phytoremediation by aquatic plant treatment systems for treating contaminated water and soil is being clearly acceptable by scientists, engineers and governmental regulators. The usage of this technology for air pollution control is only at very early stage of commercial and public developments and still few applications in the field. A new concept using wastewater discharged from a CO2-emitting industry for plant cultivation was introduced (Figure 10), which CO2 in flue gas and excess nutrients in wastewater were simultaneously removed leading an economically feasible of air pollution and wastewater treatment concepts. However, many commercial technologies for migrating CO2 are recently emphasized on aquatic photosynthetic organisms such as alga and photosynthetic bacteria due to some favorable results including high capability to assimilate CO2 into carbohydrates and other useful substances (such as lipids and proteins), providing high CO2 fixation rates and tolerance to hard environments comparable to land plants and readily for incorporating a technology into industrial processes comparing with other photosynthetic system using higher plants (Murakami and Ikenouchi, 1997).

Figure 10. Combination of air-water treatment conceptual system.

346


5.2. Efficiency of Aquatic Plants for Carbon Dioxide Removal The estimation of CO2 removal rate by aquatic plants was conducted in several batch experiments in closed system under controlled environment in the laboratory, while the longrun operation was conducted by connecting several phytoreactor (PRs) in series. Figure 11 shows the schematic diagram for setting up of vertical free-water surface flow constructed wetlands (VFS-CWs) in three series and visual view in the laboratory. During the presence of light, an enriched CO2 air (ECA) of about 0.2% CO2 (v/v) was fed intoVFS-CWs. While the dark period, there was no addition of CO2 into feeding gas flowed intoVFS-CWs, thus it was supplied by only ambient air. The gas flowrate of feeding gas was kept constant during day and night operation. The wastewater was sequentially passed through the planted beds in vertical flow direction. The water flow-rate was controlled by water-flow regulator and electric timer to provide hydraulic retention time (HRT) of 1 d for each reactor.

Figure 11. Three stages in series of vertical free-water surface flow constructed wetlands (VFS-CWs): (a) Diagram of system operation and (b) Running system in laboratory.

5.2.1. Removal Rates of Carbon Dioxide by Aquatic Plants Figure 12 illustrates the reduction of CO2 concentrations by four aquatic plants and control (without plantation) at different elevated CO2 levels.


347

Figure 12. Reduction of carbon dioxide (CO2) concentrations by planted and control treatments: (a) Elevated CO2 500 ppm, (b) Elevated CO2 1000 ppm, (c) Elevated CO2 1500 ppm and (d) Elevated CO2 2500 ppm.

The experiments indicated that PRs contained aquatic plants can effectively remove CO2 in the range of 500-2000 ppm. After 5 hours of the treatment, the CO2 concentrations inside PRs contained plants were less than 150 ppm for all elevated CO2 concentrations. Only 3-8% of elevated CO2 concentrations could be removed by control treatment. Table 5 summarizes the area-based removal rates for CO2 by aquatic plants in batch operations. The CO2 removal rates differed greatly and significantly between planted and control treatments. Results showed that the mean CO2 removal rates by planted treatment ranged from 2590 to 3430 mg C m-2 day-1. While mean CO2 removal rates by control treatment was only about 154 mg C m2 day-1. In continuous operation, the CO2 concentrations of influent and effluent gas were continuously observed every hour. Change of diurnal CO2 concentrations is illustrated by Figure 13. The shaded area (gray) depicts the amount of daily CO2 uptake and release from the system. The result showed that the positive CO2 removal of the system occurred during the day time while the negative CO2 removal (production of excess CO2 concentration)

348


occurred during the night time. The net CO2 removal rate of the system can be calculated by subtracting CO2 accumulated in the system with CO2 released from the system. Table 5. Area-based rates for carbon dioxide removal by aquatic plants Aquatic plants Controlb C. alternifolius I. ensata I. setosa T. dealbata

CO2 removal rates (mg C m-2 d-1)a 94-245 (154 ± 19) 1504-4006 (3115 ± 358) 1230-3761 (2590 ± 308) 1423-5019 (3430 ± 472) 1352-3704 (2636 ± 284)

Note: Values in parentheses are mean ± SE of three replicates. a For elevated CO2 concentrations in the range between 500 and 2000 ppm. b Control is reactor without plant.

Figure 13. Concentrations of carbon dioxide (CO2) influent and effluent gases of three stages in series of vertical free-water surface flow constructed wetlands (VFS-CWs).

5.2.2. Effect of Elevated Carbon Dioxide on Plant Responses Figure 14 illustrates the N, P and CO2 removals by three stages in series of VFS-CWs. Increasing the number of reactors in series resulted on an improvement of CO2 and nutrient removals in three stages in the system. The effect of elevated CO2 condition on plant’s growth in each reactor is illustrated in Figure 14. Results showed that the VFS-CWs treating enriched CO2 gas together with nutrients had higher plant’s growth rate in comparison with control (same system but it was fed with ambient air). Numerous growth effects and physiological responses of plants under elevated CO2 levels have been studied in many reviews including those by Allen (1990) for plant species, Bazzaz (1990) for consideration of natural ecosystems, and Eamus (1996) and Saxe et al. (1998) for forest trees. Pospisilova and Catsky (1999) illustrated the several responses of elevated CO2 on over 150 individual plants.


349

Elevated CO2 increased rates of net photosynthesis and plant water-use efficiency in about 85% and 90%, while reducing stomatal conductance and rates of transpiration in approximately 75% of the cases analyzed.

Figure 14. Variations of nutrient and carbon dioxide concentrations and biomass harvested rates in three stages in series of vertical free-water surface flow constructed wetlands: a) Nitrogen (N), phosphorus (P) and CO2 concentrations and (b) Comparison of biomass harvested rates between ambient air and enriched CO2 air condition. The bars represent mean ± SD of four replicates.

Allen (1990) shown that elevated CO2 positively affected plant-photosynthesis rate, growth, biomass and yield, water-use efficiency, leaf-area per unit ground-area and root/shoot

350


ratio but negatively affected stomatal conductance, transpiration, respiration and leaf-area per unit weight. Furthermore, the effect of high elevated CO2 on photosynthesis rate was studied by Pan and Quebedeaux (1998). The plants in this experiment were exposed to CO2 concentrations of 200, 360, 700, 1,000 and 1,600 ppm. Results of the experiment showed the increasing of photosynthesis rate nearly 1.9, 2.8 and 4.5 times greater than those records at ambient CO2 for concentrations of 700, 1,000 and 1,600 ppm. Tognetti et al. (1998) studied the long term effects of elevated CO2 on trees, which developed for 15-25 years in a basin containing CO2 content of 5001,000 ppm. It was cleared that long-term effects of plants exposing in elevated CO2 raised photosynthetic rates and wood density of the trees on average 2.75-6.1 times and 3-6% higher than rates observed growing in ambient condition. Furthermore, increased rates of dark respiration under atmospheric CO2 enrichment were observed, which was coincident with an observed increase in nonstructural carbohydrates in the CO2-enriched trees. However, the amount of excess C lost via dark respiration from the trees exposed to elevated CO2 was much less than that gained from photosynthesis. An increasing of temperature under elevated CO2 is also major consideration on biomass productivity. Ojala et al. (2002) found an enhancing of root biomass in aquatic plant (Equisetum fluviatile L.) by 10, 15 and 25% under elevated air temperature, twice-ambient CO2 concentrations and the combination of these two parameters. Several studies have found that plant-aboveground and belowground growth was stimulated under elevated CO2 (Jongen et al., 1995; Fitter et al., 1997; Hebeisen et al., 1997; Paterson et al., 1997; Rogers et al.,1998; Zak et al., 2000; Nelson, 2003). Thus, an increasing of plant above-ground and below-ground biomass could result on an increasing of plant capability for nutrient uptakes. Tanner (1996) studied the growth characteristics, plant above-ground and below-ground biomass of eight wetland plants. He suggests that a greater ratio of plant biomass to wetland volume can enhance the contact between plant roots and wastewater resulting in a greater nutrient removal, also a relationship between total biomass and nutrient removal can be described by linear regressive equation. The relationship of total plant biomass and nutrient removal rates by aquatic plants was illustrated in Figure 15. The results showed that N removal by aquatic plants was linearly increased with an increasing of plant biomass. However, there was no change of P removal with an increasing of plant biomass. In addition, the effects of elevated CO2 condition on the soil nutrients and microbial communities were demonstrated by many studies. Barrett et al. (1998) and Weerakoon et al. (1999) performed the experiment to study the uptake of nutrients in rice at elevated CO2 concentrations of about 2-3 times higher than atmospheric concentration. The experiment showed an increasing of both N and P uptakes in the range of 21-60% in comparison with uptake rates at ambient CO2 level. Impact of rising atmospheric CO2 on ecosystem was studied by Jones et al. (1998) and Insam et al. (1999). The results had shown an increasing of soil microbial communities in various plants grown in enriched CO2 environment. Dakora and Drake (2000) observe an increasing of nitrogenase activity in wetland by 73 and 23% for the C3 and C4 species grown under doubled atmospheric CO2 concentration. They found that rising of CO2 level in the air could enhance soil bacterial biodiversity and resulted in greater overall soil-microorganisms processes.


351

15.0 (a)

-1

N removal rate (mg d )

12.5 10.0 7.5 5.0 y = 0.3319x + 3.8396

2.5

2

R = 0.31 0.0 0.0

2.5

5.0

7.5

10.0

12.5

15.0

-2

Plant density (kg m ) 15.0 (b)

-1

P removal rate (mg d )

12.5 10.0 7.5 5.0 2.5

y = 0.0117x + 3.844 2

R = 0.01 0.0 0.0

2.5

5.0

7.5

10.0

12.5

15.0

-2

Plant density (kg m )

Figure 15. Effect of plant’s biomass on nutrient removals: (a) Nitrogen (N) and (b) Phosphorus (P).

Arnone and Bohlen (1998) observed the seasonal variation of microorganism activity in soil under elevated CO2 environment in grassland of northwestern Switzerland They found an increasing of soil moisture contents 10-20% and soil heterotrophic activity nearly double when the CO2 level was raised from 350 to 600 ppm. Furthermore, many studies indicated that plant’s type was the one of the important factors influencing the alteration of soil microbial profile in elevated CO2 environment. In ryegrass, for example, elevated CO2 increased the dominance of Pseudomonas species, whereas the white clover increased the dominance of Rhizobium species (Marilley et al., 1999; Montealegre, 2000).

352


SUMMARY The application of aquatic plant treatment system for nutrient and carbon dioxide (CO2) removals was studied by batch and continuous operations. Specie of choice, system design and types of substrate play an important role on the performance of treatment wetland. Although, there are many well–known stories about phytoremediation processes in water and soil environments, but the use of phytoremediation for air pollution prevention is only at early stage of development. Although, these biological CO2 mitigation actions would become limiting over the next few century, as maximal forest protection and standing biomass levels are approached. Still, very large and long-term positive effects would be realized, not only in reducing the potential for climate changes but also in preserving ecosystems and biodiversity, and in increasing productivity of agricultural and forestry systems.

ACKNOWLEDGEMENTS Part of this work was supportred by the Eco-Technopia 21 project of the Ministry of Environment, Korea (2M21530), KIST basic research project (2E20350) and international cooperative research project (2Z03230) with CRAES.

REFERENCES Anderson, J. (2004). Monitoring and verification of geological and ocean carbon dioxide disposal. In: T. Findlay (Ed.), Verification Yearbook 2003, VERTIC, London, 263 pp. Ansola, G., Fernández, C. and de Luis, E. (1995). Removal of organic matter and nutrients from urban wastewater by using an experimental emergent aquatic macrophyte system. Ecological Engineering, 5, 13-19. Arias, C.A., Bubba, M.D. and Brix, H. (2001). Phosphorus removal by sands for use as media in subsurface flow constructed reed beds. Water Research, 35, 1159-1168. Armstrong, W., Armstrong, J. and Beckett, P.M.. (1990). Measurements and modeling of oxygen release from roots of Phragmites australis. In P.F. Cooper and B.C. Findlater (Eds.), Constructed Wetlands in Water Pollution Control, Oxford: Pergamon Press, pp. 41–51. Auerbach, D.I., Caulfield, J.A., Adams, E.E. and Herzog, H.J. (1997). Impacts of ocean CO2 disposal on marine life: I A toxicological assessment integrating constant-concentration laboratory assay data with variable-concentration field exposure. Environmental Modeling and Assessment, 2, 333-343. Arth, I. and Frenzel, P. (2000). Nitrification and denitrification in the rhizosphere of rice: the detection of processes by a new multi-channel electrode. Biology and Fertility of Soils, 31, 427-435. Ayaz, S.Ç. and Akça, L. (2001). Treatment of wastewater by natural systems. Environment International, 26, 189-195. Bastviken, S.K., Eriksson, P.G., Martins, I., Neto, J.M., Leonardson, L. and Tonderski, K. (2003). Wetlands and Aquatic Processes: Potential Nitrification and Denitrification on


353

Different Surfaces in a Constructed Treatment Wetland. Journal of Environmental Quality, 32, 2414-2420. Belmont, M.A., Cantellano, E., Thompson, S., Williamson, M., Sánchez, A. and Metcalfe, C.D. (2004). Treatment of domestic wastewater in a pilot-scale natural treatment system in central Mexico. Ecological Engineering, 23, 299-311. Bezbaruah, A.N. and Zhang, T.C. (2004a). Quantification of oxygen release by Bulrush (Scirpus validus) roots in a constructed treatment wetland. Biotechnology and Bioengineering, 89, 308-318. Bezbaruah, A.N. and Zhang, T.C. (2004b). pH, redox and oxygen microprfiles in rhizosphere of bulrush (Scirpus validus) in a constructed wetland treating municipal wastewater. Biotechnology and Bioengineering, 88, 60-70. Brix, H. (1993). Macrophyte-mediated oxygen transfer in wetlands: transfer mechanisms and rates. In: G.A. Moshiri (ed.), Constructed Wetlands for Water Quality Improvement, Florida: Lewis Publishers, pp. 391-398. Brix, H. (1994). Functions of macrophytes in constructed wetlands. Water Science and Technology, 29, 71–78. Brix, H. and Arias, C.A. (2005). The use of vertical flow constructed wetlands for on-site treatment of domestic wastewater: New Danish guidelines. Ecological Engineering, 25, 491-500. Brix, H. and Schierup, H.-H. (1990). Soil oxygenation in constructed reed beds. In: P.F. Cooper and B.C. Findlater (Ed.), Constructed Wetlands in Water Pollution Control, Oxford: Pergamon Press, pp. 53-66. Bruant, R.G., Guswa, A.J. Celia, M.A. and Peters, C.A. (2002). Safe storage of CO2 in deep saline Aquifers. Environmental Science and Technology, 36, 240A-245A. Burton I. (1997). Vulnerability and adaptive change in the context of climate and climate change. Climatic change, 36, pp. 185–196. Campbell, C.S. and Ogden, M. (1999). Constructed Wetlands in the Sustainable Landscape. John Wiley and Sons Inc., New York, 288 pp. Coleman, J., Hench, K., Garbutt, K., Sexstone, A., Bissonnette, G. and Skousen J. (2001). Treatment of domestic wastewater by three plant species in constructed wetlands, Water, Air, and Soil Pollution, 128, 283-295. Cook, C.D.K. (1996). Aquatic Plant Book (2nd Ed.). SPB Academic Publishing, Amsterdam/New York, 228 pp. Cronk, J.K. and Fennessy, M.S. (2001). Wetland Plants: Biology and Ecology. CRC Press, Florida, 462 pp. Davidsson, T.E., Stepanauskas, R. and Leonardson, L. (1997). Vertical patterns of nitrogen transformations during infiltration in two wetland soils. Applied and Environmental Microbiology, 63, 3648-3656. Davis, J.A. and Froend, R. (1999). Loss and degradation of wetlands in southwestern Australia: underlying causes, consequences and solutions. Wetlands Ecology and Management, 7, 13-23. Day, J.W. Jr., Arancibia, A.Y., Mitsch, W.J., Lara-Dominguez, A.L., Day, J.N., Ko, J.Y., Lane, R., Lindsey, J. and Lomeli, D.Z. (2003). Using Ecotechnology to address water quality and wetland habitat loss problems in the Mississippi basin: a hierarchical approach. Biotechnology Advances, 22, 135-159.

354


Eertman, R.H.M., Kornman, B.A., Stikvoort, Ed. and Verbeek, H. (2002). Restoration of the Sieperda Tidal Marsh in the Scheldt Estuary, The Netherlands. Restoration Ecology, 10, 438-449. EPA (1988). Design manual: Constructed wetlands and aquatic plant systems for municipal wastewater treatment. U.S. Environmental Protection Agency (EPA), EPA/625/1-88/022. EPA (1999). Free water surface wetlands for wastewater treatment: A technology assessment. U.S. Environmental Protection Agency (EPA), Retrieved November, 03, 2004, from http://www.epa.gov/owow/wetlands/pdf/FW_Surface_Wetlands.pdf EPA (2000). Manual: Constructed wetlands treatment of municalpal wastewaters. U.S. Environmental Protection Agency, EPA 832-R-93-008. Gale, P.M., Devai, I., Reddy, K.R. and Graetz, D.A. (1993). Denitrification potential of soils from constructed and natural wetlands. Ecological Engineering, 2, 119-130. Gibbs, J.P. (2000). Wetland Loss and Biodiversity Conservation. Conservation Biology, 14, 314-317. Gifford, R.M., Barrett, D.J., Lutze, J.L. and Samarakoon, A.B. (2000). The CO2 fertilizing effect: Relevance to the global carbon cycle. In. T.M.L. Wigley and Schimel, D.S. (Eds.), The Carbon Cycle, Cambridge University Press, United Kingdom, pp. 77-92. Gilliam, F.S., May, J.D., Fisher, M.A. and Evan, D.K. (1999). Short-term changes in soil nutrients during wetland creation. Wetlands Ecology and Management, 6, 203-208. Gopal, B. (1999). Natural and constructed wetlands for wastewater treatment: potentials and problems. Water Science and Technology, 40, 27-35. Ham, J.H., Yoon, C.G., Hwang, S.J. and Jung, K.W. (2004). Seasonal performance of constructed wetland and winter storage pond for sewage treatment in Korea. Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering, A39, 1329-1343. Hanisch, C. (1998). The pros and cons of carbon dioxide dumping. Environmental Science and Technology, 32, 20A-24A. Hansen, H.C.B., Koch, C.B., Nancke-Krogh, H., Borggaard, O.K. and Sørensen, J. (1996). Abiotic nitrate reduction to ammonium: Key role of green rust. Environmental Science and Technology, 30, 2053-2056. Haynes, R.J. (1990). Active ion uptake and maintenance of carbon-anion balance: a criteria examination of their role in regulating rhizosphere pH. Plant and Soil, 126, 247-264. Higgins, M.J., Rock, C.A., Bouchard, R., and Wengrezynek, B. (1993). Controlling agricultural runoff by use of constructed wetlands. In: Moshiri, G.A. (Ed.), Constructed Wetlands for Water Quality Improvement, Lewis Publishers, Boca Raton, Florida, pp. 359-367. Hinsinger, P. (1998). How do plant roots acquire mineral nutrients? Chemical processes involved in the rhizosphere. Advances in Agronomy, 64, 225-265. Holland, C.C., Honea, J., Gwin, S.E. and Kentula, M.E. (1995). Wetland degradation and loss in the rapidly urbanizing area of Portland, Oregon. Wetlands, 15, 336–345. Hollinger D.Y., Kelliher F.M., Schulze E.D., Vygodskaya N.N., Varlagin A., Milukova I., Byers J.N., Sogachov A., Hunt J.E., McSeveny T.M., Kobak K.I., Bauer G. and Arneth A. (1995). Initial assessment of multi-scale measures of CO2 and H2O flux in the Siberian taiga. Journal of Biogeography. 22, 425–431. Huesemann, M.H., Skillman, A.D. and Crecelius, E.A. (2002). The inhibition of marine nitrification by ocean disposal of carbon dioxide. Marine Pollution Bulletin, 44, 142-148.


355

Hurd, T.M., Raynal, D.J. and Schwintzer, C.R. (2001). Symbiotic N2 fixation of Alnus incana ssp. rugosa in shrub wetlands of the Adirondack Mountains. New York, USA, Oecologia, 126, 94-103. Iamchaturapatr, J., Yi, S.W. and Rhee, J.S. (2007). Nutrient removals by 21 aquatic plants for vertical free surface-flow (VFS) constructed wetland. Ecological Engineering, 29, 287293. Ingersoll, T. and Baker, L. (1998). Nitrate removal in wetland microcosms. Water Research, 32, 677-684. IPCC (2007). The Fourth Assessment Report: Climate Change 2007. Available online at the website of Intergovernmental Panel on Climate Change, http://www.ipcc.ch. IWA (2000). Constructed Wetlands for Pollution Control: Processes, Performance, Design and Operation. IWA Specialist Group on Use of Macrophytes in Water Pollution Control, IWA Publishing, London, UK, 156 pp. Joong, K.K., Kyoung, J.P., Kyoung, S.C., Nam, S.-W., Park, T.-J. and Bajpai, R. (2005). Aerobic nitrification-denitrification by heterotrophic Bacillus strains. Bioresource Technology, 96, 1897-1906. Kadlec, R.H.and Knight, R.L. (1996). Treatment Wetlands. Lewis Publishers, New York, 893 pp. Kadlec, R.H. (1999). The limits of phosphorus removal in wetlands. Wetlands Ecology and Management, 7, 165-175. Kim, H.T. (1998). Principals of Soil Chemistry (3rd Ed.). Marcel Dekker, New York, 531 pp. Kim,S.Y., Kang, H. and Freeman, C. (2004). Effects of elevated atmospheric CO2 on wetland soils. In S.K. Hong et al. (Eds.), Ecological Issues in a Changing World: Status, Response and Strategy, Kluwer Academic Publishers, Netherlands, pp. 47-54. Kornaros, M. and Lyberatos, G. (1997). Kinetics of aerobic growth of a denitrifying bacterium, Pseudomonas denitrificans, in the presence of nitrates and/or nitrites. Water Research, 31, 479-488. Kovacic, D.A., Twait, R.M., Wallace, M.P. and Bowling, J.M. (2006). Use of created wetlands to improve water quality in the Midwest—Lake Bloomington case study. Ecological Engineering, 28, 258-270. Kremen, A., Bear, J., Shavit, U. and Shaviv, A. (2005). Model demonstrating the potential for coupled nitrification denitrification in soil aggregates. Environmental Science and Technology, 39, 4180-4188. Kwabiah, A.B., Stoskopf, N.C., Voroney, R.P. and Palm, C.A. (2001). Nitrogen and phosphorus release from decomposing leaves under sub-humid tropical conditions. Biotropica, 33, 229-240. Kyambadde, J., Kansiime, F., Gumaelius, L. and Dalhammar, G. (2004). A comparative study of Cyperus papyrus and Miscanthidium violaceum-based constructed wetlands for wastewater treatment in a tropical climate. Water Research, 38, 475-485. Kyambadde, J., Kansiime, F. and Dalhammar, G. (2005). Nitrogen and phosphorus removal in substrate-free pilot constructed wetlands with horizontal surface flow in Uganda. Water, Air, and Soil Pollution, 165, 37-59. Lantzke, I.R., Heritage, A.D., Pistillo, G. and Mitchell, D.S. (1998). Phosphorus removal rates in bucket size planted wetlands with a vertical hydraulic flow. Water Research, 32, 1280-1286.

356


Lantzke, I.R., Mitchell, D.S., Heritage, A.D. and Sharma, K.P. (1999). A model of factors controlling orthophosphate removal in planted vertical flow wetlands. Ecological Engineering, 12, 93-105. Luederitz, V., Eckert, E., Lange-Weber, M., Lange, A. and Gersberg, R.M. (2001). Nutrient removal efficiency and resource economics of vertical flow and horizontal flow constructed wetlands. Ecological Engineering, 18, 157-171. Lin, Y.F, Jing, S.R., Wang, T.W. and Lee, D.Y. (2002). Effects of macrophytes and external carbon sources on nitrate removal from groundwater in constructed wetlands. Environmental Pollution, 119, 413-420. Ma, H. and Aelion, C.M. (2005). Ammonium production during microbial nitrate removal in soil microcosms from a developing marsh estuary. Soil Biology and Biochemistry, 37, 1869-1878. Meuleman, A.F.M., Van Logtestijn, R., Rijs, G.B.J. and Verhoeven, J.T.A. (2003). Water and mass budgets of a vertical-flow constructed wetland used for wastewater treatment. Ecological Engineering, 20, 31-44. Metcalf and Eddy (2003). Wastewater Engineering: Treatment and Reuse (4th Ed.). McGrawHill, New York, 1819 pp. Mitsch, W.J. and Gosselink, J.G. (2000). Wetland (3rd Eds.). John Wiley and Sons, New York, 920 pp. Mitsch, W.J. and Wang, N. (2000). Large-scale coastal wetland restoration on the Laurentian Great Lakes: Determining the potential for water quality improvement. Ecological Engineering, 15, 267-282. Murakami, M. and Ikenouchi, M. (1997). The biological CO2 fixation and utilization project by RITE(2) – Screening and breeding of microalgae with high capability in fixing CO2. Energy Conversion and Management, 38, S493-S497. Narteh, L.T. and Sahrawat, K.L. (2000). Ammonium in solution of flooded West African soils. Geoderma, 95, 205-214. Nelson, M., Alling, A., Dempster, W. F., van Thillo, M. and Allen, J. (2003). Advantages of using subsurface flow constructed wetlands for wastewater treatment in space applications: Ground-based mars base prototype. Advances in Space Research, 31, 17991804. Newman, J.M., Clausen, J.C. and Neafsey, J.A. (2000). Seasonal performance of a wetland constructed to process dairy milkhouse wastewater in Connecticut. Ecological Engineering, 14, 181-198. Nicholls, R.J. (2004). Coastal flooding and wetland loss in the 21st century: changes under the SRES climate and socio-economic scenarios. Global Environmental Change, 14, 6986. NOAA (2008). Trends in atmospheric carbon dioxide - Mauna Loa. U.S. National Oceanic and Atmospheric Administration (NOAA), Retrieved April 10, 2008 from http://www.esrl.noaa.gov/gmd/ccgg/trends/ Okurut, T.O., Rijs, G.B.J. and van Bruggen, J.J.A. (1999). Design and performance of experimental constructed wetlands in Uganda, planted with Cyperus papyrus and Phragmites mauritianus. Water Science and Technology, 40, 265-271. Prochaska, C.A. and Zouboulis, A.I. (2006). Removal of phosphates by pilot vertical-flow constructed wetlands using a mixture of sand and dolomite as substrate. Ecological Engineering, 26, 293-303.


357

Prystay, W. and Lo, K.V. (2001). Treatment of greenhouse wastewater using constructed wetlands. Journal of Environmental Science and Health - Part B Pesticides, Food Contaminants, and Agricultural Wastes, 36, 341-353. Puigagut, J., Villaseñor, J., Salas, J.J., Bécares, E. and García, J. (2007). Subsurface-flow constructed wetlands in Spain for the sanitation of small communities: A comparative study. Ecological Engineering, 30, 312-319. Rao, T.P., Yano, K., Iijima, M., Yamauchi, A. and Tatsumi, J. (2002). Regulation of rhizosphere acidification by photosynthetic activity in cowpea (Vigna unguiculata L. Walp.) seedlings. Annals of Botany, 89, 213-220. Revsbech, N.P., Jacobsen, J.P. and Nielsen, L.P. (2005). Nitrogen transformations in microenvironments of river beds and riparian zones. Ecological Engineering, 24, 447455. Richardson, C.J. and Craft, C.B. (1993). Effective phosphorus retention in wetlands: Fact or fiction? In: G.A. Moshiri (Ed.), Constructed Wetlands for Water Quality Improvement, Florida: CRC Press, pp. 271-282. Risgaard-Petersen, N. and Jensen, K. (1997). Nitrification and denitrification in the rhizosphere of aquatic macrophyte Lobelia dortmanna L.. Limnology and Oceanography, 42, 529-537. Schulz, C., Gelbrecht, J. and Rennert, B. (2003). Treatment of rainbow trout farm effluents in constructed wetland with emergent plants and subsurface horizontal water flow, Aquaculture, 217, 207-221. Schulze E.D., Schulze W., Kelliher F.M., Vygodskaya N.N., Ziegler W., Kobak K.L., Koch H., Arneth A., Kusnetsova W.A., Sogatchev A., Issajev A., Bauer G. and Hollinger D.Y. (1995). Above-ground biomass and nitrogen nutrition in a chrono-sequence of pristine Dahurian Larix stands in eastern Siberia. Canadian Journal of Forest Research, 25, 943– 960. Sikora, F.J., Tong, Z., Behrends, L.L, Steinberg, S.L. and Coonrod, H.S. (1995). Ammonium removal in constructed wetlands with recirculating subsurface flow: removal rates and mechanisms. Water Science andTechnology, 32, 193-202. Silvan, N., Vasander, H. and Laine, J. (2004). Vegetation is the main factor in nutrient retention in a constructed wetland buffer. Plant and Soil, 258, 179-187. Streever, W.J. (1997). Trends in Australian wetland rehabilitation. Wetlands Ecology and Management, 5, 5-18. Stottmeister, U., Wießner, A., Kuschk, P., Kappelmeyer, U., Kästner, M., Bederski, O., Müller, R.A. and Moormann, H. (2003). Effect of plants and microorganisms in constructed wetlands for wastewater treatment. Biotechnology Advances, 22, 93-117. Stumm, W. and Morgan, J.J. (1996). Aquatic Wastewater: Chemical Equilibria and Rates in Natural Waters (3rd Eds.). John Willey and Sons, New York, 1040 pp. Sundaravadivel, M. and Vigneswaran, S. (2001). Constructed wetlands for wastewater treatment. Critical Reviews in Environmental Science and Technology, 31, 351-409. Tamburri, M.N., Peltzer, E.T., Friederich, G.E., Aya, I., Yamane, K. and Brewer, P.G. (2000). A field study of the effects of CO2 ocean disposal on mobile deep-sea animals. Marine Chemistry, 72, 95-101. Tang, S., Xi, L., Zheng, J. and Li, H. (2003). Response to elevated CO2 of Indian mustard and sunflower growing on copper contaminated soil. Bulletin of Environmental Contamination and Toxicology, 71, 988-997.

358


Thullen, J.S., Sartoris, J.J and Nelson, S.M. (2005). Managing vegetation in surface-flow wastewater-treatment wetlands for optimal treatment performance. Ecological Engineering, 25, 583-593. Toet, S., Van Logtestijn, R.S.P., Schreijer, M., Kampf, R. and Verhoeven, J.T.A. (2005). The functioning of a wetland system used for polishing effluent from a sewage treatment plant. Ecological Engineering, 25, 101-124. Tsuchiya, T. (1991). Leaf life span of floating-leaved plants. Vegetatio, 97, 149-160. Urbanc-Bercic, O. (1994). Investigation into the use of constructed reedbeds for municipal waste dump leachate treatment. Water Science and Technology, 29, 289-294. Vile, M.A. and Wieder, R.K. (1993). Alkalinity generation by Fe(III) reduction versus sulfate reduction in wetlands constructed for acid mine drainage treatment. Water, Air, and Soil Pollution, 69, 425-441. Vymazal, J. (2002). The use of sub-surface constructed wetlands for wastewater treatment in the Czech Republic: 10 years experience. Ecological Engineering, 18, 633-646. Vymazal, J. (2005). Horizontal sub-surface flow and hybrid constructed wetlands systems for wastewater treatment. Ecological Engineering, 25, 478-490. Weishar, L.L., Teal, J.M. and Hinkle, R. (2005). Designing large-scale wetland restoration for Delaware Bay. Ecological Engineering, 25, 231-239. WHO (2002). Eutrophication and health. World Health Organization (WHO), Regional office for Europe, Luxembourg: Office for Official Publications of the European. Retrieved 01 April, 2008 from internet website of European Commission http://ec.europa.eu/environment/water/water-nitrates/pdf/eutrophication.pdf Wilson, R.F and Mitsch, W.J. (1996). Functional assessment of five wetlands constructed to mitigate wetland loss in Ohio, USA. Wetlands, 16, 436–451. WRI (2008). Earth Trends: Searchable database of Climate and Atmosphere. World Resources Institute (WRI), Retrieved April 10, 2008 from http://earthtrends.wri.org/ Yang, L., Chang, H.T., and Huang, M.N.L. (2001). Nutrient removal in gravel- and soilbased wetland microcosms with and without vegetation. Ecological Engineering, 18, 91105. Yoon, C.G., Kwun, S.K.and Ham, J.H. (2001). Feasibility study of a constructed wetland for sewage treatment in Korean rural community. Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering, A36, 1101-1112. Zalidis, G.C., Takavakoglou, V., Panoras, A., Bilas, G. and Katsavouni S. (2004). Reestablishing a sustainable wetland at former Lake Karla, Greece, using Ramsar restoration guidelines, Environmental Management, 34, 875-886.


Chapter 11

PHYTOPLANKTON BIOMASS REGULATION IN CONTRASTING ENVIRONMENTAL STATES OF TEMPORARY POOLS Silvia Martín∗, Marta Rodríguez and David G. Angeler University of Castilla – La Mancha, Institute of Environmental Sciences (ICAM), Avda. Carlos III s/n, E-45071 Toledo, Spain

ABSTRACT Although abiotic forces play a fundamental role in community and process regulation of disturbed wetland ecosystems, biotic interaction is increasingly recognised for having important regulatory feedback effects. This chapter reports on the contextspecific role of biotic and abiotic regulation of phytoplankton biomass in temporary ponds. Contamination of artificial ponds with different application concentrations of a fire retardant resulted in alterations of the trophic status, primary producer and zooplankton communities in treatment ponds. Principal component analyses suggested that facilitation of phytoplankton biomass through cladocerans was the most important controlling factor in nutrient-limited control ponds. These biotic interaction effects disappeared in retardant treatment ponds where phytoplankton biomass was almost exclusively controlled by water depth fluctuation. This context-specific, eutrophicationmediated physical control of algal biomass in treatment ponds adds a new dimension to the traditional perspective of resource and consumer control of phytoplankton in alternative ecosystem states in lakes. The context-dependent interplay of physical and biotic processes in wetlands will likely influence applied issues and challenge wetland management and restoration.

Keywords: temporary ponds, alternative states, disturbance ecology, phytoplankton biomass.

∗

E-mail addresses: [email protected] (S. Martín)

360

Silvia Martín, Marta Rodríguez and David G. Angeler

1. INTRODUCTION Wetlands are hydrologically disturbed ecosystems and abiotic forces have traditionally been considered the most important regulators of communities and processes in these ecosystems (Mitsch and Gosselink, 2000; Euliss et al., 2004). This view is deeply rooted in wetland ecology and the potential structuring role of biotic feed back effects has long been neglected. Recent studies begin to show the importance of biotic interactions in wetland community and process regulation within the constraints set by the natural disturbance regimes (e.g., Zimmer et al., 2001; Hanson et al., 2005; Zimmer et al., 2006), and some studies suggest that these can be even used to bolster restorative interventions in degraded wetlands (Falk et al., 2006). Despite plankton being recognised as ideal study object for determining the role of biotic and abiotic regulation of wetland communities because of their rapid growth, generation turnover, and response to environmental variables (Angeler and Rodrigo, 2004), this group has been little studied in wetlands compared to lakes. Furthermore, the few studies on wetland plankton available in the literature have been carried out on different taxonomic groups, across different spatial and temporal scales and in different environmental settings and contexts, thereby complicating comparisons. Thus it is currently difficult to predict when and to what extent biotic interactions will be important in naturally disturbed wetland environments. Wetlands are susceptible to anthropogenic disturbance, and offer the opportunity to study the synergistic effects of natural disturbance and anthropogenic stress on communities. For example, using a manipulative design to simulate a scenario of fire retardant contamination in artificial temporary ponds, Angeler and Moreno (2006, 2007) have shown that wetlands profoundly change in their abiotic environment and biotic community characteristics as a result of the hypereutrophication event. The shifts observed in the ponds affected by retardant contamination (increased trophic status, reduced submerged macrophyte biomass, increased phytoplankton biomass, and altered zooplankton community composition) were consistent with predictions made by alternative state theory (e.g., Scheffer et al., 1993).While the temporal duration of the pond study does not allow for making inferences about stability of the alternative states, an issue that is currently debated in the literature (Schröder et al., 2005), it provides the opportunity to study the influence of biotic and abiotic regulation in these contrasting environmental states. Because Angeler and Moreno (2006, 2007) used a manipulative design, the context dependence of biotic or abiotic regulation in function of a hypereutrophication event can be determined, thereby contributing to broaden current paradigms related to basic (community regulation) and applied (alternative state) wetland ecology (Angeler et al. 2007). Given that temporary wetlands are highly dynamic in space and time (Williams, 2006), which complicates the determination of mechanistic processes if studied under inappropriate temporal scales (Drake, 1990; Weiher and Keddy, 1999), this study aims at revealing the broad-scale regulation of phytoplankton biomass in the contrasting community states reported in previous studies (Angeler and Moreno, 2006, 2007). The advantage of this approach is that food-web effects and the influence of natural hydrological variability can be studied in an easy, simple and temporarily integrative manner using multivariate statistics. While broadscale studies undoubtedly overlook certain trophic linkages and interactions, the critical issue

Phytoplankton Biomass Regulation in Contrasting Environmental States…

361

to be resolved is whether these complexities are mere details or, alternatively, when and if trophic linkages are truly important in driving the dynamics of the system.

2. MATERIAL AND METHODS 2.1. Artificial Ponds A detailed description of the pond design, comments on the ecological realism of the study using these artificial ponds, and the rationale for studying fire retardant impacts in temporary environments can be found in Angeler and Moreno (2006). Briefly, nine artificial ponds (diameter 4 m, Zmax 1 m) separated by 4 m and lined with black pond foil (0.2 cm thickness) were constructed in the Quintos de Mora Range Station (Toledo, Central Spain). Each pond received 100 L dry wetland soil from Las Tablas de Daimiel National Park (central Spain) that served as a donor bank of dormant invertebrate and plant propagules. Ponds were flooded with 4000 L stream water from a nearby creek, and colonization processes of macrophytes and invertebrates in the ponds after filling took place via recruitment from the propagule banks.

2.2. Monitoring Program Monitoring for this study spanned two hydroperiods that lasted from October 2003September 2004 and October 2004-May 2005, respectively (Figure 1). The designated retardant ponds were contaminated in August 2003 with the retardant, Fire Trol 934, which consists of > 90% (w/w) of ammonium polyphosphates. All ponds were dry at the time of retardant contamination. They filled with the first autumnal rains by the end of September 2003. Because rainfall was abundant in autumn and winter of 2003, the ponds did not completely dry out during the study. We therefore separated one hydroperiod from the next on the basis of the lowest water level in 2004 (Figure 1). We followed user instructions (details in Angeler and Moreno 2006) to contaminate ponds with two environmentally realistic retardant application concentrations (1 L m-2 [Treatment 1, T1] and 3 L m-2 [T2]). These application rates are recommended by fire managers for use in grasslands and scrublands, respectively. Pond selection for treatment application followed a complete randomized approach with each treatment level (control, T1 and T2) being replicated three times. Water depth, Secchi transparency, water temperature, pH, electrical conductivity, dissolved oxygen saturation and zooplankton were sampled in two-week intervals during the first hydroperiod and at monthly intervals during the second hydroperiod. Depth-integrated water samples were taken from each pond centre and composited in a 10-L bucket; ponds were accessed from a portable bridge. Samples were collected in 2 L, HCl-cleaned and distilled water-rinsed PVC bottles, preserved below 4 °C during the field trip and analyzed in the laboratory for chlorophyll a (Chl a), total nitrogen (TN), ammonium (NH4), unionized ammonia (NH3) total phosphorus (TP), soluble reactive phosphorus (SRP) and dissolved organic carbon (DOC).

362


Figure 1. Time course of selected limnological variables (means ± standard error) measured in the artificial ponds (modified from Angeler and Moreno 2006, 2007). Solid line, control ponds; dashed lines, treatment 1 ponds (1 Lm-2); dotted lines, treatment 2 ponds (3 Lm-2).

Protocols for analyses are given in Angeler and Moreno (2006). Zooplankton sampling and evaluations followed Angeler et al. (2006). For this study, density data were transformed to biomass following published length-weight regressions and formulas for calculating


363

volume/weight relationships (Bottrell et al., 1976). The abundance of submerged aquatic vegetation (SAV) was estimated on each sampling date; it is expressed as the percentage volume of vegetation infesting the water column (percent volume infested; PVI) (Jeppesen et al., 1998).

2.3. Statistical Analyses Principal component analysis (PCA) with varimax factor rotation was used to explore which biotic and abiotic variables best represented gradients in the pond environments of each treatment level over both hydroperiods. The biweekly data from the first hydrological cycle were averaged to a monthly treatment mean. The treatment means from both hydrological cycles were used in the PCA. All limnological variables (Tab. 1) were entered in the analyses, except ostracods and SAV which had zero variance in retardant treatments. Because some of our limnological variables were highly correlated between each other, their use in subsequent linear multiple regression analyses would have artificially inflated the power of the model. The use of the PCA axes as independent variables in the regression analyses helped to predict log-transformed Chl a without risking bias due to multicollinearity of variables (Harrell et al., 1984; Graham, 2003). The SPSS v. 12. software package (SPSS, Inc., Chicago, Illinois) was used for all statistical analyses.

3. RESULTS 3.1. Pond Seasonality and Effects of Retardant Contamination The impact-recovery patterns of water quality variables have been described from a purely temporal perspective by Angeler and Moreno (2006). Briefly, the ponds showed a marked seasonal variability reflecting the fluctuating character of Mediterranean temporary pond ecosystems. For comprehension sake, the time trends of selected variables are reproduced in Figure 1. Retardant application comprised a “sledgehammer contamination event” and resulted in a significant increase of nutrients (total P, total N, NH4 and soluble reactive P), and chl a, and a loss of submerged macrophytes (a diverse assemblage of Chara aspera Deth. ex Willd., Chara hispida L., Chara vulgaris L., Ranunculus trichophyllus Chaix, and Zannichelia pedunculata Reichenb.) due to combined effects of germination failure and high water column turbidity (Table 1). Immediately after retardant contamination mean total P concentration reached 60.3 mg L-1 in T1 and 216 mg L-1 in T2, respectively, exceeding manifold the maximum values reported for most hypereutrophic surface waters. Total N rose to 49.3 mg L-1 (T1) and 120.4 mg L-1 (T2) on the first sampling date; the control remained at 0.56 mg L-1. The concentrations of phosphorus and nitrogen decreased during the study, however none of these variables converged with concentrations in the control by the end of the study. The lack of convergence was more pronounced for P variables compared to nitrogenous compounds.

Table 1. Summary of the limnological environment of artificial ponds. Shown are the means ± standard errors of twenty sampling dates comprising two hydroperiods in control ponds (0 L m-2), treatment 1 ponds (1 L m-2), and treatment 2 ponds (3 L m-2) respectively. M: state where submerged macrophytes were abundant; P: state were phytoplankton was abundant Limnological variables Chlorophyll a (µg·L-1) Sumberged macrophytes (PVI) Water depth (cm) Secchi depth (cm) Temperature (ºC) pH Electr. conductivity (µS·cm-1) Oxygen saturation (%) Total phosphorus (mg·L-1) Soluble reactive P (mg·L-1) Total nitrogen (mg·L-1) NH4 (mg·L-1) NH3 (mg·L-1) DOC (mg·L-1) Rotifera (µg·L-1) Cladocera (µg·L-1) Ostracoda (µg·L-1) Copepoda (µg·L-1)

Control (M) 12.90 ± 3.61 51 ± 5 59.66 ± 4.67 57.23 ± 4.58 14.98 ± 1.70 8.87 ± 0.16 342 ± 42 86.86 ± 10.27 0.32 ± 0.08 0.16 ± 0.07 0.61 ± 0.13 0.47 ± 0.10 0.26 ± 0.09 6.50 ± 0.85 30.18 ± 6.97 115.83 ± 33.41 124.58 ± 74.08 72.72 ± 25.12

Treatment 1 (P) 57.99 ± 3.61 0±0 59.76 ± 4.10 55.52 ± 4.28 14.85 ± 1.70 8.50 ± 0.15 359 ± 41 78.27 ± 8.00 13.67 ± 3.47 13.29 ± 3.91 4.58 ± 2.59 3.56 ± 2.01 0.36 ± 0.13 8.70 ± 1.12 253.20 ± 48.30 47.27 ± 17.55 0±0 5.37 ± 2.42

Treatment 2 (P) 93.3 ± 34.60 0±0 60.99 ± 4.20 51.38 ± 4.22 14.96 ± 1.61 8.09 ± 0.16 568 ± 62 77.62 ± 10.40 52.47 ± 11.40 48.30 ± 8.85 22.68 ± 6.90 17.64 ± 5.37 0.80 ± 0.27 11.66 ± 1.44 338.17 ± 102.6 80.34 ± 35.60 1.3 ± 0.52 15.54 ± 9.86


365

The nutrient loading contributed to an increase of phytoplankton biomass, which resulted in Chl a peaks of 249.2 µg L-1 in T1 and 650.2 µg L-1 in T2. Secchi transparency and pH were also significantly affected by retardant treatment, while electrical conductivity, NH3, DOC, water color, and other variables not susceptible to a change in nutrient loading (i.e., related to water depth and temperature) were not significantly affected by the retardant (Table 1, Angeler and Moreno, 2006). The biomass of rotifers, cladocerans and copepods also showed pronounced seasonality over the study period (Figure 1). Ostracod biomass was marginal (< 10 µg L-1) throughout almost all of the study, but peaked in the control ponds between January and March 2005 (1360.1 µg L-1). Retardant application lowered the biomass of cladocerans, ostracods and copepods but increased rotifer biomass (Table 1; Angeler and Moreno, 2007).

3.2. Limnological Environment The limnological environment in control ponds and retardant treatment ponds was dominated by abiotic factors (Table 2). Table 2. Summary of the varimax rotated PCA solutions showing principal factors and limnological variables with significant loadings. The axes are named according to the collective attributes of the limnological variables they are composed of.

Control ponds PC 1 (Seasonality 1)

% Variance explained (Eigenvalue) 78.48 total 24.57 (3.93)

PC 2 (Resource control)

21.53 (3.45)

PC 3 (Summer drawdown)

15.93 (2.55)

PC 4 (Consumer control) PC 5 (Seasonality 2)

8.87 (1.42) 7.58 (1.21)

Treatment 1 ponds PC 1 (Resource control)

79.14 total 30.53 (5.33)

PC 2 (Seasonality 1)

21.54 (3.23)

Limnological variables

Correlation coefficient (r)

SRP Secchi Water depth Oxygen TP NH4 TN NH3 pH Cladocerans Temperature

0.71 0.84 0.90 -0.74 0.70 0.90 0.92 0.96 0.89 0.79 0.86

TP SRP NH4 TN Secchi Water depth Conductivity

0.91 0.88 0.95 0.95 0.94 0.92 -0.76

366

Silvia Martín, Marta Rodríguez and David G. Angeler Table 2. (Continued)


% Variance explained (Eigenvalue) 18.63 (2.79)

PC 4 (Consumer control)

8.44 (1.27)

Treatment 2 ponds PC 1 (Resource control)

76.82 total 33.21 (5.31)


16.40 (2.62)

PC 3 (Consumer control 1) PC 4 (Seasonality 2)

11.66 (1.87) 8.77 (1.40)

PC 5 (Consumer control 2)

6.78 (1.08)

Limnological variables NH3 Temperature Oxygen Rotifers

Correlation coefficient (r) 0.87 0.77 0.58 0.89

TP SRP NH4 TN Water depth Conductivity Rotifers Temperature Oxygen Cladocerans

0.94 0.94 0.94 0.94 -0.93 0.82 -0.82 0.77 -0.80 0.90

Characteristics of the ponds natural variability were reflected in distinct PCA axes that separated between water level fluctuations and temperature variability. These showed inverse time trends during the study, i.e., temperatures were highest during summer months when water levels were lowest in the ponds and vice versa (Figure 1). Both axes are considered as seasonality axes (Table 2), and they are considered as surrogates for the natural disturbance regimes of the ponds. In the case of control ponds, a third axis related to pond seasonality was identified; it included NH3 and pH. Because NH3 forms from NH4 under conditions of high pH and temperatures, we interpret this axis as a situation of a summer drying-out phase. PCA axes that served as surrogates of the natural disturbance regime and resource axes explained together < 50% of the inertia in the models of all treatment levels (Table 2). The influence of consumers (broad taxonomic groups of zooplankton) in structuring the limnological environment was marginal whether or not ponds were treated with the retardant.

3.3. Phytoplankton Biomass Regulation Multiple regression analyses showed that phytoplankton biomass in the control ponds was explained by combinations of resource, consumer and natural disturbance predictors. Although consumers contributed little to structuring the limnological environment of ponds, they best explained phytoplankton biomass (50% of the total variance), followed by resource control (27.3%), and the combined effects of water depth and temperature seasonality (22.7%) (Table 3, Figure 2). The strong consumer effects disappeared in all retardant treatment ponds. In T1 ponds, phytoplankton was best predicted by axes related to water-level fluctuations (82.3%) and, to a minor degree, by temperature gradients. No resource control was detected (Table 3).


367

Figure 2. Results from multiple regression analyses showing the relationships of phytoplankton biomass with significant predictor axes related to resources, consumers and natural disturbance from the PCA ordination. The asterisk means that nutrients probably acted as a surrogate for other regulating forces (see Discussion).

From the ecological perspective, this suggests that in the nutrient-enriched phytoplankton-dominated state, algal biomass responds more pronouncedly to the natural fluctuations in water depth and temperature compared with the uncontaminated controls.

368


Table 3. Summary of linear multiple regression analyses relating principal components (Table 2) to phytoplankton biomass (log Chl a). Partial variances were significant at p < 0.05 (*), p < 0.01 (**) and p < 0.001 (***) Treatment Level Control

Intercept

Independent variables1

Partial r2

0.74

Treatment 1

1.26

-0.12 Seasonality 1 0.21 Resource 0.28 Consumer 0.15 Seasonality 2 -0.47 Seasonality 1 0.22 Seasonality 2

0.04* 0.12*** 0.22*** 0.06* 0.51*** 0.11***

Treatment 2

1.51

0.20 Resource 0.30 Seasonality 1 1 See PCA factor axes in Table 2.

0.09** 0.22***

Overall p level < 0.0001

Overall r2 (adjusted) 0.43

< 0.0001

0.60

< 0.001

0.30

Phytoplankton biomass regulation changed markedly in T2. Algal biomass also responded to water-level fluctuations (71%), but the relationship was inverse compared to T1 and control ponds, which suggests a biomass increase with rising water depth in T2. Despite the hypereutrophication effects caused by retardant application, T2 ponds showed a positive relationship between phytoplankton biomass and resources (Table 3, Figure 2).

4. DISCUSSION Algae biomass in shallow aquatic environments is regulated by complex food web interactions (e.g., nutrient competition with, and allelopathic suppression by, submerged macrophytes and zooplankton herbivory) and resource (nutrient) availability (e.g., Jeppesen et al., 1997; 1998). The positive relationship of phytoplankton biomass with nutrients in the control ponds, which was not found in retardant treatment ponds (except T2 ponds; discussed below) suggest that algae biomass was nutrient limited. This is supported by the positive relationship observed between phytoplankton biomass and cladocerans in the control ponds, which suggests a facilitation effect of phytoplankton through zooplankton nutrient regeneration. After the hypereutrophication event caused by fire retardant contamination in treatment ponds the abiotic and biotic pond environment changed, consistent with state shifts reported in shallow lakes. Nutrient enrichment contributed to fuel phytoplankton, submerged macrophytes virtually disappeared from the ponds, and the zooplankton community shifted to a less efficiently grazing community dominated by rotifers. The duration of this study was not long enough to determine whether retardant contamination nudged ponds permanently to an alternative ecosystem state (Schröder et al., 2005), but it allowed revealing what factors control phytoplankton biomass under these conditions. In contrast to lakes, temporary ponds show marked seasonal variability of water depth, including frequent drying out. Several studies have shown the strict influence of physical factors (e.g., water depth variability), rather than trophic status or food web effects, on wetland plankton (Angeler and Rodrigo, 2004; Angeler et al., 2005). The alternating drying


369

out and filling phases can lead to expansions and contractions of the water column, leading to concentration/dilution cycles of aquatic biota and nutrients as well (Stanley et al., 1997). This could explain the negative relationship of phytoplankton biomass with PCA axes reflecting water depth seasonality in control and T1 ponds, which suggest that algal biomass was highest when water depth was lowest. However, the opposite relationship observed in T2 ponds suggests that algal biomass increased with rising water levels. We hypothesise that this pattern is due to retardant toxicity at high application rates. Angeler and Moreno (2006) showed disparate hypereutrophication effects with nutrient levels (mean total P concentrations of 52.47 mg L-1) exceeding manifold those of most eutrophic surface waters. Concentrations of toxic NH3 also exceeded critical limits reported for aquatic biota in the literature (reviewed in Giménez et al., 2004). This occurred especially in summer months when temperature and pH values are high coinciding with low water depths (Angeler and Moreno, 2006). This suggests that phytoplankton can suffer especially during drought periods from the combined effects of naturally stressful conditions (low water availability, high temperatures) and anthropogenic stress. The positive relationship of water depth and algae biomass suggests that phytoplankton is relieved from these adverse conditions when ponds refill with winter rains. Because of the broad-scale focus of this study exact mechanisms underlying the patterns observed in treatment and control ponds can not be unambiguously determined. This limits also the interpretation of the positive relationship observed between resources and phytoplankton biomass in the nutrient-supersaturated T2 ponds. Rather than a true nutrient effect, it is likely that other processes including self-shading of the algal standing crop, bloom formation and collapse, nutrient leaching from living and dead biota, and allelopathic activity of cyanobacteria could have caused the observed relationship between resources and phytoplankton standing crop. Unfortunately, due to the initial goals of our research on fire retardant impacts in temporary ponds, no phytoplankton taxonomic analyses are currently available for corroboration. Fire retardant contamination adds a new perspective to eutrophication research, which, in turn, offers an applied ecological frame for determining the context-dependence of abiotic and biotic regulation of plankton of different types of surface waters. Biota-mediated nutrient regeneration can be important to sustain algal growth under nutrient limiting conditions (e.g., Zimmer et al., 2006; this study) and/or when competition with submerged aquatic vegetation is high. By, contrast physcial control prevails under nutrient-enriched conditions, thereby fitting traditional views that natural disturbance regime is key to the regulation of wetland communities and processes (Mitsch and Gosselink, 2000). Wetland plankton holds potential to add a new dimension to plankton research which is currently biased towards physically less disturbed lake ecosystems. While current plankton paradigms typically embrace a perspective of bottom-up and top-down control, the consideration of physical forces could help advance basic limnology and eventually translate into sound management of increasingly threatened wetland ecosystems.

370


ACKNOWLEDGEMENTS We are grateful to A. Velasco, C. Morales, P.V. Cepeda, J.M. Moreno, and our project partners from France, Portugal and Greece for contributing to this study in many ways. The Quintos de Mora staff is acknowledged for support throughout the study. The comments of Andrew Boulton and Miguel Alvarez-Cobelas improved previous manuscript drafts. Financial support was provided from the European Community research project “Extension Retardant Application System (ERAS)” (Contract number EVG1-2002- 300019).

REFERENCES Angeler, D.G., A.J. Boulton, K.M. Jenkins, B. Sánchez, M. Alvarez-Cobelas, and S. SánchezCarrillo, 2007: Alternative states and temporary wetlands: Research opportunities for understanding effects of anthropogenic stress and natural disturbance. In: P.A. Clarkson (ed.), Environmental Research Advances. Nova Science Publishers, Inc. New York, pp. 5-17. Angeler, D.G. and J.M. Moreno, 2006: Impact-recovery patterns of water quality in temporary wetlands after fire retardant pollution. - Can. J. Fish. Aquat. Sci. 63:16171626. Angeler, D.G. and J. M. Moreno, 2007: Zooplankton community resilience after press-type anthropogenic stress in temporary ponds. - Ecol. Appl. 17: 1105-1115. Angeler, D.G. and M.A. Rodrigo, 2004: Ramp disturbance – ramp response: a simple model for wetland disturbance ecology. - Mar. Freshwat. Res. 55: 33-37. Angeler, D.G., B. Sánchez, G. García and J.M. Moreno, 2006: Community ecotoxicology: invertebrate emergence from Fire Trol 934 contaminated vernal pool and salt marsh sediments under contrasting photoperiod and temperature regimes. - Aquat. Toxicol. 78: 167-175. Angeler, D.G., S. Sánchez-Carrillo, M.A. Rodrigo, O. Viedma and M. Alvarez-Cobelas, 2005: On the importance of water depth, macrophytes and fish in wetland picocyanobacteria regulation. - Hydrobiologia 549: 23-32. Bottrell, H.H., A. Duncan, Z.M.Gliwicz, E. Grygierek, A Herzig, A. Hillbricht-Ilkowska, et al. 1976: A review of some problems in zooplankton production studies. - Nor. J. Zool. 24: 419-456. Drake, J.A. 1990: Communities as assembled structures: do rules govern pattern? - Trends Ecol. Evol. 5: 159-164. Euliss, N.H., Jr., J.W. LaBaugh, L.H. Fredrickson, D.M. Mushet, M.K. Laubhan, G.A. Swanson, T.C. Winter, D.O. Rosenberry and R.D. Nelson, 2004: The wetland continuum: a conceptual framework for interpreting biological studies. - Wetlands 24: 448–458. Falk, D.A., M.A. Palmer and J.B. Zedler, 2006: Foundations of Restoration Ecology. - Island Press, Washington, D.C. Giménez, A., E. Pastor, E. Zarate, E. Planas, and J. Arnaldos, 2004: Long-term forest fire retardants: a review of quality, effectiveness, application and environmental considerations. - Int. J. Wildland Fire 13: 1–15.


371

Graham, M.H., 2003: Confronting multicollinearity in ecological multiple regression. Ecology 84: 2809-2815. Hanson, M.A., K.D. Zimmer, M.G. Butler, B.A. Tangen, B.R. Herwig and N.H. Euliss, Jr., 2005: Biotic interactions as determinants of ecosystem structure in prairie wetlands: an example using fish. - Wetlands 25: 764-775. Harrell, F.E., K.L. Lee, R.M. Califf, D.B. Pryor and R.A. Rosati, 1984: Regression modelling strategies fro improved prognostic predictions. – Stat. Med. 3: 143-152. Jeppesen, E., J.P. Jensen, M. Søndergaard, T. Lauridsen, J.L. Pedersen and L. Jensen, 1997: Top-down control in freshwater lakes: The role of nutrient state, submerged macrophytes and water depth. - Hydrobiologia 342/343:151-164. Jeppesen, E, M. Søndergaard, M. Søndergaard and K. Cristoffersen, 1998: The structuring role of submerged macrophytes in lakes. - Springer Verlag, New York. Mitsch, W. J and J.G. Gosselink, 2000: Wetlands, 3rd ed. - Wiley and Sons, New York. Scheffer, M., S.H. Hosper, M.-L. Meijer, B. Moss and E. Jeppesen, 1993: Alternative equilibria in shallow lakes. - Trends Ecol. Evol. 8: 275-279. Schröder, A., L. Persson and A.M. De Roos, 2005: Direct experimental evidence for alternative stable states: a review. - Oikos 110: 3-19. Stanley, E.H., S.G. Fisher and N.B. Grimm, 1997: Ecosystem expansion and contraction in streams. - BioScience 47: 427-436. Weiher, E. and P.A. Keddy, 1999: Ecological assembly rules: perspectives, advances, retreats. - Cambridge University Press, Cambridge. Williams, D.D., 2006: The Biology of Temporary Waters. - Oxford Univ. Press, Oxford, U.K. Zimmer, K. D., M.A. Hanson and M.G. Butler, 2001: Effects of fathead minnow colonization and removal on a prairie wetland ecosystem. - Ecosystems 4:346–357. Zimmer, K.D., B.R. Herwig, and L.M. Laurich, 2006: Nutrient excretion by fish in wetland ecosystems and its potential to support algal production.- Limnol. Oceanogr. 51: 197207.


Chapter 12

CAN TERN MIGRANTS COEXIST WITH URBAN DEVELOPMENT AND ESTUARINE RECREATIONAL ACTIVITIES? Ken Chan, Jill Dening and Marja-Leena Malinen Institute of Hydrobiology, Jinan University, Guangzhou, P.R. China

INTRODUCTION Urbanisation and recreational activities are two of the major causes of population declines of species (Czech et al. 2000), and throughout the world they continue to spread and intensify at a rapid rate (Hill et al. 1997). The two are often linked—an increase in recreational activities is often associated with nearby growth in residential development and vice versa. Developmental growth is greatest in places of high tourism value, such as in coastal areas with sandy shores. Sandy coasts are popular with beach walking and jogging, swimming, off-road vehicles, boating, ecotourism, and other outdoor activities. The most concentrated activities are in estuaries with sandbanks and intertidal flats that are protected from the open ocean. Yet the same estuaries are often sensitive ecosystems, commonly frequented by a variety of resident and migrant birds that use the areas to breed, forage, or roost (Erwin 1996; Drut and Buchanan 2000; Chan and Dening 2007). Increasing incidents of human disturbance can affect breeding behavior, feeding patterns, opportunities for rest, and decline in estuarine bird abundance (Burger 1986; Mitchell et al. 1988; Burger and Gochfield 1991; Brown et al. 2001; Verhulst et al. 2001). The direct impact on reproduction in breeding birds is obvious, but survival of migratory species is also affected through ineffective buildup of requisite fat reserves to successfully undertake their migratory journey (Goss-Custard et al. 2002). For both resident and migrant birds, disturbance could result in reduced feeding time, lowering the necessary fat reserves for survival (Brown et al. 2001). Many waterbirds (those bird species dependent on aquatic habitats to complete aspects of their life cycles) are long-distance migrants that travel considerable distances between wintering and breeding grounds. The most studied of these are the waders (the long-legged wading shorebirds such as curlews, sandpipers, stilts, oystercatchers, plovers, avocets),

374

Ken Chan, Jill Dening, and Leena Malinen

commonly referred to as shorebirds. In the space of a year, many will have used a range of habitats that include estuaries, beaches, and rocky shorelines. These habitats temporally represent "migratory bottlenecks" in which limited resource availability can become an important survival issue (Drut and Buchanan 2000). Much has been documented about the effect of urban development and recreational activities on foraging waders along coastal areas (Lafferty 2001; Thomas et al. 2003; Rogers et al. 2006). In the Asia–Pacific region where more than half of the human population lives and where over 85% of the important wetlands of the region are under threat, there have been consistent calls for urgent conservation of shorebird habitat in the East Asian–Australasian Flyway (Watkins 1993; Nebel 2007). Somewhat neglected by comparison are the terns (Laridae), a major waterbird group which commonly occupy these areas also. Like waders, many terns are migrants that congregate in coastal habitats at certain times of the year in large numbers, often seen alongside waders on sandbanks and mudflats (Erwin 1996; Cornelius et al. 2001; Bugoni et al. 2005; Chan and Dening 2007). Surprisingly, despite the common knowledge that various species of terns worldwide use coastal areas to rest, little information is available on the impact of urbanization and recreation on roosting terns. Implications of human disturbance on these seabirds do exist in the literature, but the focus is usually related to breeding behavior (Holloway 1993; Burger 1998; Yorio et al. 2001; Benoit and Bretagnolle 2002). In 2000-2003, a comprehensive ground survey of terns in a popular recreational waterway in Australia (Chan and Dening 2007) revealed one of the largest congregations of non-breeding terns in a southern hemisphere estuary. The estuary is located at the southern end of a coastal strip, off the city of Caloundra, in the state of Queensland known as the Sunshine Coast (Figure 1). This region has been subjected to rapid urban development in recent years and commercially is highly dependent on tourism. Residential growth has long led to concerns about destruction of other coastal habitats in the region for a number of vulnerable or endangered species [e.g. wallum sedgefrog Litoria olongburensis (Ingram and McDonald 1993) and ground parrot Pzoporus wallicus (Chan et al. 2008) in coastal heath]. Coastal development increases recreational water activities, and the Sunshine Coast is particularly susceptible because it has a subtropical climate and extensive sandy beaches which are already the main attractions of the tourism industry. The discovery of Caloundra sandbanks as an important site for overwintering terns has generated interest about other estuaries on the Sunshine Coast as potential roosting sites. Near the northern end of the Sunshine Coast 50 nautical km to the north is suburban Noosa Heads (26° 22' 57"S, 153° 4' 26"E), arguably the most popular tourist destination in the region. Noosa heads is situated at the mouth of the Noosa River where approximately 20 ha of sandbanks can be seen at low tide. Residents have reported an assortment of breeding and non-breeding seabirds and shorebirds occupying the sandbanks at various times of the year. Until now, no survey has been carried out to verify bird numbers and diversity, nor their relationship with the ever-increasing human activities in the area. The Caloundra sandbank survey by Chan and Dening (2007) raised questions about the long-term impact of increasing human activities on migratory terns overwintering at estuarine sandbanks. It prompted government management authorities to commit to investigating the interaction between human and birds for sustainable benefits of tourism and wildlife presence. The present work is the result of the government commissioning such an investigation. In this study, we aimed to determine (1) the types and numbers of birds in the Noosa estuary and how they compare with those in Caloundra sandbanks, (2) the types and numbers of disturbance events at peak

Can Tern Migrants Coexist with Urban Development…

375

and outside holiday periods, and (3) the behavioral response of birds to disturbance events.The objective of the research is to produce data which may be used to propose policy recommendations to minimize the impact of recreational activities on roosting terns. Such a policy is warranted in light of the projected human activities in the region because, as human coastal populations increase, the prediction is increased potential for bird disturbance. Human activities are not the only disturbance type affecting waterbirds. Seabirds and shorebirds are subject to many natural predators, ranging from birds of prey, gulls and other birds, and native mammals (Moore 2002). Indeed, it has been argued that human-induced loud noises (e.g. helicopters) and fast-approaching objects (e.g. dogs) elicit response linked to the evolution of antipredatory behavior, but which incur energetic and lost opportunity costs (Frid and Dill 2002).

Figure 1. Map of southeast Queensland showing Noosa estuary and nearby locations of interest.

376


When disturbed by natural predators or by humans, resting birds may require significant amounts of energy flying away and foraging birds suffer loss in feeding time. Burger and Gochfeld (1991) found an increased number of people caused sanderlings Calidris alba not only to forage where there is less disturbance, but also to undertake increased nocturnal foraging. Resting birds can change their spatial and temporal distribution because of human disturbance (Cornelius et al. 2001), and may even be displaced altogether (Pfister et al. 1992). Natural predators can similarly affect flocking behavior in shorebirds (Page and Whitacre 1975). The tidal restrictions on foraging and roosting of waterbirds can compound the effects of disturbance. Shorebirds typically spend most of the time roosting during high tide, but Hotker (2000) noted flocks of Dunlins Calidris alpina occasionally fly and remain over the sea and suggested that the behavior is to prevent surprise attacks by raptors. Rehfisch et al. (1996) calculated that disturbance to roosting red knot C. canutus during high tide would increase the daily energy expenditure by 5.9%, or an extra 40 min feeding time, in an enforced 10 km flight to an undisturbed roost. For these reasons, we collected data with respect to high and low tides. To successfully assess the long-term impact of human activities, we additionally collected bird-response data to disturbance and experimentally determined the “flight-initiation distance” (FID)—which refers to the exact distance at which birds begin to flee—of terns and waders to human approach. The FID is often used as a guide to quantify the perception of predation risk (Geist et al. 2005).

METHODS For surveying purposes, the sandbanks at the Noosa estuary were divided into seven sections (sites). Despite the size and movement of the sandbanks being dynamic, the seven sections are normally distinct at low tide, although Sites 2 and 3 were merged at times. Site 1 is always joined to the mainland (Figure 2). Surveys on bird types and numbers were conducted by boat access for all sites. Bird counts were made at both high and low tides on the same day over 18 occasions from October 2005 to January 2007. At least one census was conducted for each month except for December 2005, July 2006, and October 2006 when no counts were made. Simultaneous identification and counts of disturbance events were made during each bird census. A minimum of three observers was involved at each census: two people recorded birds and the third person recorded disturbance. At least two bird observers were used so as to ensure vigilance of flock movements to avoid double counting. The surveys covered both weekends and weekdays, and included the peak holiday periods: Easter, Boxing Day and Australia Day. An additional 11 counts were made from a strategic land-point at the mouth of the estuary for detection of terns entering the estuary from the ocean to roost for the night. Our previous study at Caloundra sandbanks showed that (1) terns return to their sandbank roosts in the evening after feeding in the open sea for the day, and (2) detection was especially challenging in summer when bird numbers were highest. From these observations, we found it necessary to have at least two experienced observers counting terns entering the estuary and a third observer counting birds leaving the estuary during summer evening counts. The total number of birds leaving from the estuary was then subtracted from the number entering the estuary to avoid recounting the same birds.


377

Figure 2. Noosa estuary showing sandbank sampling sites. Sandbank sizes shown are true only to the time the photo was taken; sandbank movement constantly causes size change.

The evening tern counts began at dusk and continued until last light. Birds were identified to species level when possible. Kowa TSN-822 telescopes, pairs of 8 x 30 and 10 x 40 binoculars, and hand-held counters aided bird identification and counts. All human-based activities in the estuary in and surrounding the sandbanks were recorded during each census. These were later categorized into aircraft, boat, dog, jet-ski, kite surfer, people, people and dogs, vehicle, and other (e.g. horse). Any detection of direct bird response to such disturbance events is categorized as A = excluded, B = walked/ran away or stopped feeding, C = lifted and resettled at the same site within 30 sec, and D = lifted and did not return. “Excluded” (A) refers to no birds present, such as when people are in the area and no birds can be seen. For flight-initiation distance (FID), the observers first identify an individual bird either resting or foraging that was not initially disturbed by the observers’ presence. To measure the distance a person could approach a bird before it was disturbed, one observer remained stationary while a second observer advanced at a steady walking pace of 0.5–1 m/sec toward the target bird that was either resting or foraging at some distance. The “advancing observer” stopped as soon the target bird ceased its activity, i.e. stopped foraging or, in the case of a resting bird, became noticeably vigilant or started to move away. The stationary observer then measured the distance to the “advancing observer” with the aid of a Yardage Pro Compact 600 range finder. This distance (Distance 1) was recorded and the “advancing observer” pressed forward again until the target bird took flight, at which time the “advancing observer” stopped again and a second reading of distance (Distance 2) was measured and recorded. The “advancing observer” then moved forward to the original location of the target species and a third reading (Distance 3) was measured and recorded. Thus Distance 3 minus Distance 1 equals distance from bird at initial disturbance and Distance 3 minus Distance 2 is the FID.

378


Because separate counts for high and low tides were carried out on the same day, it is possible to compare numbers of birds and of disturbance events across tide levels. As the count data did not meet the assumptions of parametric testing, a Wilcoxon sign-ranked test was used for pairwise comparisons at high and low tides for diurnal bird numbers and for disturbance events. Median and range values are presented. The FIDs, which were measured in meters, had approximate normal distributions and variances, and a two-sample t-test was used to determine mean differences in FID between waders and terns with the values presented in mean±SD.

RESULTS Species Composition and Abundance Thirty-five species of waterbirds were recorded using the estuary, plus another four species of raptor (Brahminy kite Haliastur Indus, osprey Pandion haliaetus, whistling kite Haliastur sphenurus, and white-bellied sea-eagle Haliaeetus leucogaster). There were three resident waders and 15 migratory waders, the most abundant of which were bar-tailed godwit Limosa lapponica, Pacific golden plover Pluvialis fulva, and sharp-tailed sandpiper Calidris acuminata. But by far the most abundant group of birds belonged to the terns (Table 1), three species of which are migratory to the study area (common tern Sternula hirundo, little tern Sterna albifrons, white-winged tern Chlidonias leucopterus) and four are resident (Caspian tern Sterna caspia, crested tern Sterna bergii, gull-billed tern Sterna nilotica, common noddy Anous stolidus). Up to 90% of birds observed at the sandbanks during the migratory period were terns, and as much as 90% of these terns were common terns. Tern migrants were in the Noosa estuary from late November 2005 to April 2006 and again from early December 2006 until January 2007 when census ceased. Although maximum numbers were reached at high tide for all species of terns except for the gull-billed tern (Table 1), no significant difference was found when tern numbers were compared over eight days of counting which covered both high and low tides during the migration period (z = 1.82, p = 0.07). However, migrant terns as a group were generally more common at low than at high tide (z = -1.96, p = 0.05, n = 8; Figure 3). The number of waders was similar at high and low tides (z = 0.91, p = 0.36, n = 13), but other waterbirds (e.g. silver gull Larus novaehollandiae, Australian pelican Pelecanus conspicillatus) occurred in higher numbers at low tide (z = 2.482, p = 0.01; n= 13). Migrant waders were present all year round as a proportion stay behind for the Austral winter; this allowed comparison of migrant and resident numbers at high and low tides for the entire study period. Migrant waders were found to be more numerous at both high (z = -3.72, p < 0.01, n = 18) and low tide (z = 3.29, p < 0.01, n = 18). As expected, the number of terns entering the estuary to roost for the night was much higher than that counted during daytime hours (Figure 4); whereas diurnal counts peaked at 8 500 birds, evening counts peaked at 38 340 birds in the February 2005 census. It was difficult to identify species of terns when birds were constantly flying into the estuary at fading light.


379

Table 1. Maximum diurnal counts for selected species at low and high tides Common name

Scientific name

Low

High

Bar-tailed godwit

Limosa lapponica

161

201

Pacific golden plover

Pluvialis fulva

73

80

Sharp-tailed sandpiper

Calidris acuminata

65

70

Red-necked stint

Calidris ruficollis

50

32

Red-capped plover

Charadrius ruficapillus

38

33

Whimbrel

Numenius phaeopus

23

29

Little black cormorant

Phalacrocorax sulcirostris

494

541

Pied cormorant

Phalacrocorax varius

67

60

Silver gull

Larus novaehollandiae

319

207

Australian pelican

Pelecanus conspicillatus

45

55

Common tern

Sterna hirundo

2700

6300

Little tern

Sternula albifrons

139

210

White-winged tern

Chlidonias leucopterus

230

490

Caspian tern

Sterna caspia

18

42

Crested tern

Sterna bergii

1296

1886

Gull-billed tern

Sterna nilotica

11

11

9000

8000

Number of birds

7000 6000 5000 4000 3000 2000 1000

Figure 3. Diurnal numbers of migrant and resident terns at high and low tides.

15.01.07

26.12.06

Residents (low) Migrants (low) Residents (high) Migrants (high)

16.12.06

15.12.06

21.11.06

05.11.06

10.09.06

24.09.06

11.08.06

15.04.06

25.06.06

12.03.06

08.02.06

08.03.06

14.01.05

26.01.06

22.10.05

28.11.05

0

380

Ken Chan, Jill Dening, and Leena Malinen 45000 40000

Number of birds

35000

Migrant terns Resident terns

30000 25000 20000 15000 10000 5000

07

06 15

.0

1.

2. 15

.1

1. .1 21

11

.0

8.

06

06

06 6. .0 25

12

.0

3.

3. 08

.0

2. .0 08

06

06

06

06 1. .0 14

.1 28

22

.1

0.

1.

05

05

0

Figure 4. Numbers of terns entering the Noosa estuary to roost for the night.

But experience suggests the proportion of common terns was similar to that counted during daytime hours. This would give an estimate of 30 000-35 000 common terns coming to roost at the sandbanks on that night.

Spatial Distribution During the day, terns preferred to roost on sandbanks with unhindered 360 degree visibility. Resident terns occupied all available sandbanks at low tide, but migratory terns were more selective and mostly roosted only on sandbanks completely void of vegetation (Sites 6 and 3). At high tide when sandbanks became contracted, the vast majority of migrant terns roosted on Site 1—the large above-water sandbank attached to the mainland (see Figure 2)—even though small islands of sandbanks were available. Resident terns also favored Site 1 at high tide. This contrasts with shorebirds which occupied all areas in the estuary where available. At high tides of 2.2 m and above (using Brisbane source), all sandbanks except for the vegetated sites were fully inundated, and terns left the estuary sandbanks to roost at the sand spit at Site 1. For example, on 14 January 2006 when a 2.42 m high tide was present, all migratory terns counted that day were roosting on the spit.


381

Human Activities and Raptor Disturbance A total of 230 human-related activity events was observed. The majority of disturbance occurred during high tide (71%, z = -2.95, p < 0.01, n = 15) and on sandbanks closest to the mainland. People and dogs, with or without owners, were the most numerous human-related events (Figure 5). On many occasions people were observed deliberately running at flocks of birds to force them into flight. If a dog accompanies its owner, it too will chase the birds. We were able to observe 89 disturbance events that directly induced flight (flushing). Of these, 80 were human-related (the others were caused by raptors). Disturbance usually caused short flights of

Wetlands: Ecology, Conservation and Restoration

The World's Largest Wetlands: Ecology and Conservation

Cougar: Ecology and Conservation

Architectural Tiles: Conservation and Restoration

Ecology and Ecosystem Conservation

FOrest ecology and conservation

Grasslands: Ecology, Management and Restoration

Nonequilibrium Ecology (Ecology, Biodiversity and Conservation)

Seamounts Ecology Fisheries and Conservation

Behavioral Ecology and Conservation Biology

Seagrasses: Biology, Ecology and Conservation

Seagrasses: Biology, Ecology and Conservation

Bumblebees: Behaviour, Ecology, and Conservation

Coastal Dunes: Ecology and Conservation

Conservation and Restoration of Archive Materials

Restoration Ecology: The New Frontier

Conservation and Restoration of Glass, Second Edition

Wetlands

Science and Conservation of Vernal Pools in Northeastern North America: Ecology and Conservation of Seasonal Wetlands in Northeastern North America

Large Herbivore Ecology, Ecosystem Dynamics and Conservation (Conservation Biology)

Riparia: Ecology, Conservation, and Management of Streamside Communities (Aquatic Ecology)

Remote Sensing for Ecology and Conservation: A Handbook of Techniques (Techniques in Ecology and Conservation)

The Bonobos: Behavior, Ecology, and Conservation

Allee Effects in Ecology and Conservation

Island Bats: Evolution, Ecology, and Conservation

Island Biogeography: Ecology, Evolution, and Conservation

Cats of Africa : Behavior, Ecology, and Conservation

Otters: Ecology, Behaviour and Conservation (Oxford Biology)

Bumblebees: Behaviour, Ecology, and Conservation, Second Edition

Large Herbivore Ecology, Ecosystem Dynamics and Conservation

Seasonally Dry Tropical Forests: Ecology and Conservation

Wetlands: Ecology, Conservation and Restoration

The World's Largest Wetlands: Ecology and Conservation

Cougar: Ecology and Conservation

Architectural Tiles: Conservation and Restoration

Ecology and Ecosystem Conservation

FOrest ecology and conservation

Grasslands: Ecology, Management and Restoration

Nonequilibrium Ecology (Ecology, Biodiversity and Conservation)

Seamounts Ecology Fisheries and Conservation

Behavioral Ecology and Conservation Biology

Seagrasses: Biology, Ecology and Conservation

Seagrasses: Biology, Ecology and Conservation

Bumblebees: Behaviour, Ecology, and Conservation

Coastal Dunes: Ecology and Conservation

Conservation and Restoration of Archive Materials

Restoration Ecology: The New Frontier

Conservation and Restoration of Glass, Second Edition

Wetlands

Science and Conservation of Vernal Pools in Northeastern North America: Ecology and Conservation of Seasonal Wetlands in Northeastern North America

Large Herbivore Ecology, Ecosystem Dynamics and Conservation (Conservation Biology)

Riparia: Ecology, Conservation, and Management of Streamside Communities (Aquatic Ecology)

Remote Sensing for Ecology and Conservation: A Handbook of Techniques (Techniques in Ecology and Conservation)

The Bonobos: Behavior, Ecology, and Conservation

Allee Effects in Ecology and Conservation

Island Bats: Evolution, Ecology, and Conservation

Island Biogeography: Ecology, Evolution, and Conservation

Cats of Africa : Behavior, Ecology, and Conservation

Otters: Ecology, Behaviour and Conservation (Oxford Biology)

Bumblebees: Behaviour, Ecology, and Conservation, Second Edition

Large Herbivore Ecology, Ecosystem Dynamics and Conservation

Seasonally Dry Tropical Forests: Ecology and Conservation

Recommend Documents