Environmental remote sensing and systems analysis

CHANG

ENVIRONMENTAL REMOTE SENSING
and SYSTEMS ANALYSIS

ENVIRONMENTAL
ENVIRONMENT
REMOTEREMOTE
SENSINGSENSIN
and and
SYSTEMSSYSTEMS
ANALYSISANALY

EDITED BY NI-BIN
EDITED
CHANG
BY NI-BIN CHAN
Tai Lieu Chat Luong


ENVIRONMENTAL
REMOTE SENSING
and
SYSTEMS ANALYSIS
EDITED BY NI-BIN CHANG

Boca Raton London New York

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Contents
Preface......................................................................................................................vii
About the Editor.........................................................................................................ix
Contributors...............................................................................................................xi
Chapter 1 Linkages between Environmental Remote Sensing
and Systems Analysis............................................................................1
Ni-Bin Chang

Part I Water Quality Monitoring, Watershed
Development, and Coastal Management
Ni-Bin Chang and Zhemin Xuan

Chapter 3 Mapping Potential Annual Pollutant Loads in River Basins
Using Remotely Sensed Imagery........................................................ 35
Kazuo Oki, Bin He, and Taikan Oki
Fahad A. M. Alawadi
Chapter 5 Remote Sensing to Predict Estuarine Water Salinity.......................... 85
Fugui Wang and Y. Jun Xu
Chapter 6 Multitemporal Remote Sensing of Coastal Sediment Dynamics...... 109
Paul Elsner, Tom Spencer, Iris Möller, and Geoff Smith
Chapter 7 Estimating Total Phosphorus Impacts in a Coastal Bay
with Remote Sensing Images and in Situ Measurements................. 123
Ni-Bin Chang and Kunal Nayee
Chapter 8 Monitoring and Mapping of Flood Plumes in the Great Barrier
Reef Based on in Situ and Remote Sensing Observations................ 147
Michelle Devlin, Thomas Schroeder, Lachlan McKinna,

Jon Brodie, Vittorio Brando, and Arnold Dekker

iii


iv

Contents

Part II Sensing and Monitoring for Land Use
Patterns, Reclamation, and Degradation
Chapter 9 Satellite Remote Sensing for Landslide Prediction........................... 191
Yang Hong, Zonghu Liao, Robert F. Adler, and Chun Liu
Chapter 10 Analysis of Impervious Surface and Suburban Form Using
High Spatial Resolution Satellite Imagery........................................209
D. Barry Hester, Stacy A. C. Nelson, Siamak Khorram,
Halil I. Cakir, Heather M. Cheshire, and Ernst F. Hain
Chapter 11 Use of InSAR for Monitoring Land Subsidence in Mashhad
Subbasin, Iran.................................................................................... 231
Maryam Dehghani, Mohammad Javad Valadan Zoej,
Mohammad Sharifikia, Iman Entezam, and Sassan Saatchi
Chapter 12 Remote Sensing Assessment of Coastal Land Reclamation
Impact in Dalian, China, Using High-Resolution SPOT Images
and Support Vector Machine............................................................. 249
Ni-Bin Chang, Min Han, Wei Yao, and Liang-Chien Chen
Chapter 13 Mapping Impervious Surface Distribution with the Integration
of Landsat TM and QuickBird Images in a Complex
Urban– Rural Frontier in Brazil......................................................... 277
Dengsheng Lu, Emilio Moran, Scott Hetrick, and Guiying Li

Part III Air Quality Monitoring, Land Use/Land
Cover Changes, and Environmental
Health Concern
Chapter 14 Using Lidar to Characterize Particles from Point and Diffuse
Sources in an Agricultural Field....................................................... 299
Michael D. Wojcik, Randal S. Martin, and Jerry L. Hatfield
Chapter 15 Measurement of Aerosol Properties over Urban Environments
from Satellite Remote Sensing.......................................................... 333
Min M. Oo, Lakshimi Madhavan Bomidi, and Barry M. Gross


v

Contents

Chapter 16 DOAS Technique: Emission Measurements in Urban
and Industrial Regions...................................................................... 377
Pasquale Avino and Maurizio Manigrasso
Chapter 17 Interactions between Ultraviolet-B and Total Ozone
Concentrations in the Continental United States.............................. 395
Zhiqiang Gao, Wei Gao, and Ni-Bin Chang
Chapter 18 Remote Sensing of Asian Dust Storms............................................. 423
Tang-Huang Lin, Gin-Rong Liu, Si-Chee Tsay,
N. Christina Hsu, and Shih-Jen Huang
Chapter 19 Forest Fire and Air Quality Monitoring from Space........................ 457
John J. Qu and Xianjun Hao
Chapter 20 Satellite Remote Sensing of Global Air Quality............................... 479
Sundar A. Christopher

Preface
In the last few decades, rapid urbanization and industrialization have altered the priority of environmental protection and restoration of air, soil, and water quality many
times. Yet it is recognized that the sustainable management of human society is
necessary at all phases of impact from the interactions among energy, environment,
ecology, public health, and socioeconomic paradigms. The multidisciplinary nature
of this concern for sustainability is truly a challenging task that requires employing a
systems analysis approach. Such a systems analysis approach links several disciplinary areas with each other to promote the concept of sustainable management. Just as
a sophisticated piece of music involves many different instruments played in unison,
systems analysis requires a holistic viewpoint and a plethora of tools in sensing,
monitoring, and modeling that have to be woven together to explore the state and
function of air, water, and land resources at all levels.
With the aid of systems analysis, this comprehensive collection includes a variety
of research work that results from years of experience and that reflects the contemporary advances of remote sensing technologies. This unique publication presents
and applies the most recent synergy of remote sensing technologies that will advance
the overall understanding of the sensitivity of key environmental quality issues in
relation to human perturbations. These perturbations can be caused by collective or
individual impacts of economic development and globalization, population growth
and migration, and climate change on atmospheric, terrestrial, and aquatic environmental systems.
Specifically, this book aims to address the following intertwined research topics
in the nexus of the environmental remote sensing and systems analysis:
• What are the potential impacts on water quality when the management of
the nitrogen cycle in a watershed changes, affecting ecosystem health in
marine and fresh waters?
• What are the regional impacts of an oil spill in coastal environments?
• How will water quality in coastal bay and estuary regions be affected by
changing salinity concentrations, turbidity levels, and sediment transport
processes?
• How will landslide and land subsidence in association with the changing
hydrologic cycle influence human society?

• How will the effects of urbanization affect the rate of water infiltration at
urban–rural interfaces?
• How can the impact of air pollution on meteorology, climatology, and public health be evaluated in association with the changing land use and land
cover patterns from urban to global scales?
The presentations in this book uniquely elaborate on the intrinsic links of the
above questions that capture important interactions among three thematic areas.
vii


viii

Preface

They include (1) water quality monitoring, watershed development, and coastal management; (2) sensing and monitoring for land use patterns, reclamation, and degradation; and (3) air quality monitoring, land use/land cover changes, and environmental
health concerns.
On this foundation, many new techniques and methods developed for spaceborne,
airborne, and ground-based measurements, mathematical modeling, and remote
sensing image-processing tools may be realized across these three distinctive thematic areas. This book will be a useful source of reference for undergraduate and
graduate students and working professionals who are involved in the study of environmental science, environmental management, sustainability science, environmental informatics, and agricultural and forest sciences. It will also benefit scientists in
related research fields, as well as professors, policy makers, and the general public.
As the editor of this book, I wish to express my great appreciation for the contributions of many individuals who helped write, proofread, and review these book
chapters. I am indebted to the 58 authors and coauthors within the scientific community who have shared their expertise and contributed much time and effort in the
preparation of this book. I also wish to give credit to the numerous funding agencies
promoting scientific research in environmental remote sensing that have led to the
generation of invaluable findings presented here. I acknowledge the management and
editorial assistance of Irma Shagla and Kari Budyk.
Dr. Ni-Bin Chang
Director, Stormwater Management Academy
University of Central Florida
Orlando, Florida

MATLAB® is a registered trademark of The MathWorks, Inc. For product information, please contact:
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About the Editor
Dr. Ni-Bin Chang is currently a professor with the Civil,
Environmental, and Construction Engineering Depart­
ment, University of Central Florida (UCF). He is also a
senior member of the Institute of Electrical and Electronics
Engineers (IEEE) affiliated with the IEEE Geoscience and
Remote Sensing Society and the IEEE Computational Intel­
ligence Society. He has earned the selectively awarded titles
of Certificate of Leadership in Energy and Environment
Design (LEED) in 2004, Board Certified Environmental
Engineer (BCEE) in 2006, Diplomat of Water Resources
Engineer (DWRE) in 2007, elected member (Academician)
of the European Academy of Sciences (MEAS) in 2008, and elected Fellow of
American Society of Civil Engineers (ASCE) in 2009. He was one of the founders
of the International Society of Environmental Information Management and the
former editor-in-chief of the Journal of Environmental Informatics. He is currently an editor, associated editor, or editorial board member of 20+ international
journals.

ix

Contributors
Robert F. Adler
NASA Goddard Space Flight Center
Greenbelt, Maryland

Liang-Chien Chen
National Central University
Jhongli, Taiwan

and

Heather M. Cheshire
North Carolina State University
Raleigh, North Carolina

University of Maryland
College Park, Maryland
Fahad A. M. Alawadi
Regional Organization for the
Protection of the Marine
Environment (ROPME)
Safat, Kuwait
Pasquale Avino
Chemical Laboratory DIPIA, INAIL
(ex-ISPESL)
Rome, Italy
Lakshimi Madhavan Bomidi
City College

City University of New York
New York, New York
Vittorio Brando
CSIRO Land & Water
Canberra, Australia
Jon Brodie
James Cook University
Townsville, Australia
Halil I. Cakir
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina
Ni-Bin Chang
University of Central Florida
Orlando, Florida

Sundar A. Christopher
The University of Alabama
in Huntsville
Huntsville, Alabama
Maryam Dehghani
Shiraz University
Shiraz, Iran
and
K. N. Toosi University of Technology
Tehran, Iran
Arnold Dekker
CSIRO Land & Water
Canberra, Australia
Michelle Devlin
James Cook University

Townsville, Australia
Paul Elsner
Birkbeck College
University of London
London, United Kingdom
Iman Entezam
Engineering Geology Group
Geological Survey of Iran (GSI)
Tehran, Iran

xi


xii

Contributors

Wei Gao
Colorado State University
Fort Collins, Colorado

Yang Hong
University of Oklahoma
Norman, Oklahoma

Zhiqiang Gao
Colorado State University
Fort Collins, Colorado

N. Christina Hsu

NASA Goddard Space Flight Center
Greenbelt, Maryland

Barry M. Gross
City College
City University of New York
New York, New York

Shih-Jen Huang
National Taiwan Ocean University
Keelung, Taiwan

Ernst F. Hain
North Carolina State University
Raleigh, North Carolina
Min Han
Dalian University of Technology
Dalian, China
Xianjun Hao
College of Science
George Mason University
Fairfax, Virginia
Jerry L. Hatfield
National Laboratory for Agriculture
and the Environment
USDA-ARS
Ames, Iowa
Bin He
Kyoto University
Kyoto, Japan

D. Barry Hester
Bryan Cave LLP
Atlanta, Georgia
Scott Hetrick
Indiana University
Bloomington, Indiana

Siamak Khorram
North Carolina State University
Raleigh, North Carolina
Guiying Li
Anthropological Center for
Training and Research on Global
Environmental Change
Indiana University
Bloomington, Indiana
Zonghu Liao
University of Oklahoma
Norman, Oklahoma
Tang-Huang Lin
National Central University
Jhongli, Taiwan
Chun Liu
Tongji University
Shanghai, China
Gin-Rong Liu
National Central University
Jhongli, Taiwan
Dengsheng Lu
Indiana University

Bloomington, IN


xiii

Contributors

Maurizio Manigrasso
Chemical Laboratory DIPIA, INAIL
(ex-ISPESL)
Rome, Italy

John J. Qu
College of Science
George Mason University
Fairfax, Virginia

Randal S. Martin
Utah State University
Logan, Utah

Sassan Saatchi
UCLA Center for Tropical Research
Los Angeles, California

Lachlan McKinna
Curtin University
Perth, Australia

Thomas Schroeder

CSIRO Land & Water
Brisbane, Australia

Iris Möller
University of Cambridge
Cambridge, United Kingdom

Mohammad Sharifikia
Tarbiat Moddaress University
Tehran, Iran

Emilio Moran
Indiana University
Bloomington, Indiana

Geoff Smith
Specto Natura Ltd.
Cambridge, United Kingdom

Kunal Nayee
University of Central Florida
Orlando, Florida

Tom Spencer
University of Cambridge
Cambridge, United Kingdom

Stacy A. C. Nelson
North Carolina State University
Raleigh, North Carolina

Si-Chee Tsay
NASA Goddard Space Flight Center
Greenbelt, Maryland

Kazuo Oki
Institute of Industrial Science
University of Tokyo
Tokyo, Japan

Fugui Wang
LSU Agricultural Center
Louisiana State University
Baton Rouge, Louisiana

Taikan Oki
Institute of Industrial Science
University of Tokyo
Tokyo, Japan

and

Min M. Oo
University of Wisconsin–Madison
Madison, Wisconsin

Michael D. Wojcik
Utah State University Research
Foundation
Logan, Utah

University of Wisconsin–Madison
Madison, Wisconsin


xiv

Y. Jun Xu
LSU Agricultural Center
Louisiana State University
Baton Rouge, Louisiana
Zhemin Xuan
University of Central Florida
Orlando, Florida

Contributors

Wei Yao
Dalian University of Technology
Dalian, China
Mohammad Javad Valadan Zoej
K. N. Toosi University of Technology
Tehran, Iran


1

Linkages between
Environmental
Remote Sensing and

Systems Analysis
Ni-Bin Chang

CONTENTS
1.1 Introduction....................................................................................................... 1
1.2 Current Challenges............................................................................................2
1.3 Featured Areas................................................................................................... 4
1.4 Distinctive Aspects............................................................................................5
References................................................................................................................... 6

1.1  INTRODUCTION
The interactions of physical, chemical, and biological processes in coupled natural systems and the built environment have given rise to the intertwined complexity, diversity, and persistence of various types of environmental problems.
Environmental protection and restoration of air, soil, and water quality in relation
to land use and regional planning are deeply rooted in spatiotemporal evolution at
different scales. To achieve sound environmental resources management, there is
often a need to investigate pollutant storage, transport, and transformation in both
natural systems and the built environment. ���������������������������������������
However, it is recognized that the sustainable management of human society is necessary at all phases of impact from
the interactions among energy, environment, ecology, public health, and socioeconomic paradigms. Such a multidisciplinary nature of sustainability concern
is truly a challenging task that requires employing a systems analysis approach.
Environmental sensing and monitoring networks are deemed an integral
part of environmental cyberinfrastructures and may produce comprehensive
and accurate spatial information over time, providing the basis for sustainable
development. To properly respond to natural and human-induced stresses to the
environment, however, environmental resource managers often consider the
functions and values of systems analysis that may be geared toward synergistic integration among remote sensing technologies, data/image processing tools,

1

2

Environmental Remote Sensing and Systems Analysis
Data (in situ monitoring, remote sensing)
Information (database management, information retrieval)
Knowledge (data mining, systems analysis)
Action (management alternatives)

FIGURE 1.1  System complexity to be tackled by large-scale systems analysis. (From
Chang, N. B., Systems Analysis for Sustainable Engineering, McGraw Hill, New York, 2010.)

and supportive environmental cyberinfrastructures. Systems analysis can provide
a coordinated, multidisciplinary effort to identify and understand these needs.
As a consequence, systems analysis has become an important task for essential
environmental resources management throughout the world. Major momentum
to improve systems analysis emerged as a pressing priority during the late 1990s
when the Internet became a norm in information exchange. Rapid advances in
the integration of remote sensing (RS), global positioning system (GPS), and geographical information system (GIS) technologies motivate more integrative sensing, monitoring, and modeling with system thinking for sound decision making.
Such understanding leads to the proper integration of sensing, monitoring, and
modeling technologies in order to aid in the decision making involved in the preservation or remediation of the environment.
For sound decision making, a holistic approach is thus required that encapsulates
the technical, institutional, social, economic, and environmental dimensions with
systems thinking and provides an environmental basis for addressing cultural needs,
social evolution, economic reality, and national policies. This movement requires
expertise in acquisition, storage and warehousing, quality assurance, and presentation of environmental data from which the information can be retrieved and knowledge can be developed for decision making (Figure 1.1). To fulfill such a synergistic
integration, it requires the following: collecting and maintaining environmental
data; analyzing environmental data; using data for environmental protection actions;
engaging the community to promote policies and to improve the sustainable management with environmental information; evaluating the effectiveness of environmental
management processes, programs, and efforts with environmental knowledge; and
implementing total quality management through integrated environmental sensing,

monitoring, modeling, and decision making.

1.2  CURRENT CHALLENGES
Due to global climate change, economic development and globalization, increased
frequency of natural hazards, rapid urbanization, and population growth and migration, an integrated, quantitative, systems-level method of remote sensing is essential to


Linkages between Environmental Remote Sensing and Systems Analysis

3

track the dynamics of coupled natural systems and the built environment. However,
existing environmental systems that have been degraded and even contaminated
face a reduced solution space spatially and temporally, due to competing and
conflicting stakeholders’ interests over demand����������������������������������
for water supply�����������������
, industrial production, recreation, ���������������������������������������������������������
land development�����������������������������������������
, air
���������������������������������������
quality management, and
���������������
environmental flow requirements. This reduced solution space also magnifies hydroclimatic
variability, leading toward more vulnerability to seemingly unbalanced economic
development and ecosystem conservation. The increasing hydroclimatic variability
could further translate the pollution impact into aggravation of resources scarcity,
land degradation, environmental health and safety, and insufficient agricultural
production at different scales. As a consequence, rapid change detection using
remote sensing becomes an indispensable tool for future sustainable management.
This entails an acute need to integrate environmental remote sensing and systems

analysis in complex sociotechnical systems (Laracy 2007). Catastrophic failures
are associated with ignoring social, political, economic, and institutional elements
when determining the system boundaries in concert with the temporal scales of the
environmental issues that need to be sensed, monitored, and investigated (Laracy
2007).
Remote sensing, one of the core technologies in environmental informatics,
is not a panacea or an anecdote but may become powerful when fundamental
physical, chemical, and biological processes in environmental systems can be
sensed, monitored, and ���������������������������������������������������
analyzed by
������������������������������������������
a systematic approach. Yet how to optimize the synergistic effects of sensors, platforms, and sensor networks to provide
decision makers and stakeholders timely decision support tools with respect to
species diversity, spectral heterogeneity, spatial variability, and temporal scaling
issues is deemed a critical challenge (NCAR 2002; NSF 2003; Chang et al. 2009,
2010, 2011).
Further, the identification of comparative advantages across data mining,
image processing, machine learning, and information retrieval techniques applied
to exhibit information and knowledge in support of synergistic integration of
sensing, monitoring, and modeling creates an ever-growing challenge for a sound
systems analysis (Back et al. 1997; Zilioli and Brivio 1997; Volpe et al. 2007;
Chang et al. 2009, 2010, 2011). In order to apply the systems thinking archetypes to the environmental problem being observed, the problem comes down to
methodology with regard to how the techniques of systems analysis can be connected with a modern understanding of environmental remote sensing. It leads
to the creation of case-based remote sensing practices by developing operable
systems that meet requirements within imposed constraints (Pyschkewitsch et
al. 2009). This may be illuminated by some ways through assessing four dimensions of novelty, complexity, technology, and scale simultaneously (i.e., the NCTS
framework) when a new environmental remote sensing project has to be launched
(Figure 1.2). The demonstrated selection across the four dimensions in Figure
1.2 entails how the different sensors, images, and spectral analysis skills can be
integrated for scale-dependent sensing, monitoring, and modeling in case-based

remote sensing practices.


4

Environmental Remote Sensing and Systems Analysis
Complexity
Data array
Data fusion

Constellations Platforms Sensors
Novelty

Local

Image processing

Technology

Radar Multispectral Hyperspectral Mixed
technology technology technology technology

Regional
Continental
Global
Scale

FIGURE 1.2  The NCTS framework.

1.3  FEATURED AREAS

This book is designed to address the grand challenges in the nexus of environmental
remote sensing and systems analysis under global changes. Recent advances in environmental remote sensing with various data-mining, machine-learning, and imageprocessing techniques provide us with a reliable and lucid means to explore the
changing environmental quality via a temporally and spatially sensitive approach.
It leads to the improvement of our understanding of the sensitivity of key factors in
environmental resources management. Due to space limitations, the main focus of
current research in the context of environmental remote sensing and systems analysis
may be classified into three topical areas as follows:
• Topical area I: Water quality monitoring, watershed development, and
coastal management. The interactions among aquatic environments, such
as lakes, bays, and estuaries, and associated watersheds are emphasized
to monitor the human-induced changes in the regional nutrient cycle.
Addressing these interactions is as critical as coping with the impacts of
land degradation through sea–land interactions, energy and transportation,
and natural hazards on water quality management. A few applications and
case studies at different scales worldwide in Chapters 2 through 8 demonstrate a contemporary coverage of these issues in association with both
point and nonpoint sources.
• Topical area II: Sensing and monitoring for land use patterns, reclamation, and degradation. The environmental consequences of urbanization
effect in association with land use and land cover change include, but are
not limited to, changing pervious areas and altering the hydrological cycle,


Linkages between Environmental Remote Sensing and Systems Analysis

5

land subsidence, landslide and mud flows, reclamation of land from aquatic
environments, and associated complexity of land management policies. A
few applications and case studies at different scales worldwide in Chapters
9 through 13 demonstrate a contemporary coverage of these issues.
• Topical area III: Air quality monitoring, land use/land cover changes, and

environmental health concerns. From local, to regional, to continental scale,
urbanization effect and desertification seriously contribute to a number of environmental problems in air quality management. As an example, municipal
and agricultural activities require intensive long-term air quality monitoring.
Human-induced dust storms reacting with desertification result in rising global
particulate matter, with unintended social and health impacts. Global changes
such as ozone depletion and the resulting ultraviolet impact on human society and ecosystems trigger a holistic view of environmental management. A
few applications and case studies at different scales worldwide in Chapters 14
through 20 demonstrate a contemporary coverage of these issues in association
with both point and nonpoint sources.

1.4  DISTINCTIVE ASPECTS
Macroenvironment (e.g., social, political, economic, technological, and legal) and
market demand will certainly shape the most appropriate synergistic efforts between
environmental remote sensing and systems analysis techniques. Complementing this
emerging focus with respect to the actual need of coordination and exchange of data
for improved understanding of environmental informatics, this book brings together
forward-looking scholars with the requisite experience for showing coordinated
interdisciplinary approaches between environmental remote sensing and system
analysis���������������������������������������������������������������������������
.��������������������������������������������������������������������������
Compared�����������������������������������������������������������������
to previous publications, this book�����������������������������
uniquely emphasize����������
s���������
the following distinctive aspects:
• Comparative approach for information retrieval. Throughout the book,
comparisons between data-mining, machine-learning, and imageprocessing methods are presented to help managers and researchers make
the optimal selection when dealing with satellite images.
• Integration with ground-based monitoring network. To incorporate
the strength of environmental cyberinfrastructures, a few case studies

emphasize the inclusion of ground-based monitoring networks in dealing
with air quality and water quality management issues.
• Emphasis on environmental information management. The book also
focuses on environmental information management with proper integration
of Global Positioning System (GPS), Geographical Information System
(GIS) and existing environmental databases of remote sensing images and
in situ measurements in several case studies.
• Modeling for decision making. Implications in environmental resources
management and policy using integrated simulation and optimization processes are demonstrated throughout some case studies.


6

Environmental Remote Sensing and Systems Analysis

• Remote sensing and environmental health. Emphasis has been placed on the
linkages between remote sensing and environmental health implications.
• Remote sensing and environmental management. Emphasis has also been
placed on the linkages between remote sensing and environmental management policy.
• Policy analysis for decision making. Scenario- or index-based systems
analysis is�������������������������������������������������������������
������������������������������������������������������������
demonstrated������������������������������������������������
throughout some case studies ������������������
to aid in environmental policy analysis and decision making.
• Enhancement of environmental education. Multidisciplinary education and
research are demonstrated explicitly to indicate opportunities for integrated
field and laboratory studies in environmental remote sensing education.

REFERENCES

Back, T., Hammel, U., and Schwefel, H. P. (1997). Evolutionary computation: Comments on
the history and current state. IEEE Transactions on Evolutionary Computation, 1(1),
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Chang, N. B., Daranpob, A., Yang, J., and Jin, K. R. (2009). A comparative data mining analysis for information retrieval of MODIS images: Monitoring lake turbidity changes at
Lake Okeechobee, Florida. Journal of Applied Remote Sensing, 3: 033549.
Chang, N. B. (2010). Systems Analysis for Sustainable Engineering, McGraw Hill, New York.
Chang, N. B., Han, M., Yao, W., Xu, S. G., and Chen, L. C. (2010). Change detection of
land use and land cover in a fast growing urban region with SPOT-5 images and partial
Lanczos extreme learning machine. Journal of Applied Remote Sensing, 4, 043551.
Chang, N. B., Yang, J., Daranpob, A., Jin, K. R., and James, T. (2011). Spatiotemporal pattern
validation of chlorophyll a concentrations in Lake Okeechobee, Florida using a comparative MODIS image mining approach. International Journal of Remote Sensing, doi:
10.1080/01431161.2011.608089.
Laracy, J. R. (2007). Addressing system boundary issues in complex socio-technical systems,
Proceedings of Systems Engineering Research Forum, 2(1), 19–26, Hoboken, NJ.
National Science Foundation (NSF) (2003). Complex Environmental Systems: Synthesis for
Earth, Life, and Society in the 21st Century. NSF Environmental Cyberinfrastructure
Report, Washington, DC.
National Center for Atmospheric Research (NCAR) (2002). Cyberinfrastructure for
Environmental Research and Education, Boulder, CO.
Pyschkewitsch, M., Schaible, D., and Larson, W. (2009). The art and science of systems
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Part I
Water Quality Monitoring,
Watershed Development,
and Coastal Management



2

Using Remote Sensing–
Based Carlson Index
Mapping to Assess
Hurricane and
Drought Effects on
Lake Trophic State
Ni-Bin Chang and Zhemin Xuan

CONTENTS
2.1 Introduction..................................................................................................... 10
2.2 Materials and Methods.................................................................................... 12
2.2.1 Field Measurements, Data Collection, and Analysis........................... 12
2.2.2 Eutrophication Assessment.................................................................. 14
2.2.2.1 Definition of Trophic State Index (TSI)................................ 14
2.2.2.2 Classification Methods.......................................................... 15
2.2.2.3 The Role of Remote Sensing................................................. 15
2.2.3 Remote Sensing for the Estimation of Chl-a Concentrations.............. 17
2.2.3.1 Remote Sensing Data Collection.......................................... 17
2.2.3.2 Machine Learning for Remote Sensing: The GP Model...... 18
2.3 Results and Discussion.................................................................................... 21
2.3.1 Lake Okeechobee Water Quality Analysis.......................................... 21

2.3.2 Lake Okeechobee Eutrophication Assessment.................................... 23
2.3.2.1 Remote Sensing–Based Carlson Index Mapping.................. 23
2.3.2.2 Remote Sensing–Based Eutrophication Assessment............ 23
2.3.2.3 Eutrophication Assessment Based on in Situ
Measurements....................................................................... 23
2.3.2.4 Comparative Eutrophication Assessment............................. 27
2.3.3 Final Remarks: Complexity in the Estimation of Chl-a
Concentrations in Lake Okeechobee................................................... 30
2.4 Conclusions...................................................................................................... 31
References................................................................................................................. 32
9


10

Environmental Remote Sensing and Systems Analysis

2.1  INTRODUCTION
Lake Okeechobee, the second largest freshwater lake in the United States, is the
source of fresh water to the Everglades. To the north, in the Kissimmee River Basin,
major land uses are ranching and dairy farms, and as a result, excessive nutrient
loads of phosphorus have entered the lake for more than three decades, resulting
in cultural eutrophication. About 40% of the entire lake bed is covered with black,
carbonate, organic phosphorus-enriched mud (Mehta et al. 1989). This phosphorusladen sediment can be resuspended into the water column by wind and wave action
(Maceina and Soballe 1990) and can be a primary source of phosphorus to the water
column (Evans 1994) through the diffusion and desorption processes, which is
highly related to the shear stress of the sediment bed.
Excessive phosphorus loads from the Lake Okeechobee watershed over the last
few decades have led to increased eutrophication and water quality deterioration in
the lake. According to long-term monitoring records, average annual surface water

chlorophyll-a (Chl-a) concentrations from 1974 to 2010 were 14 to 28 mg/m3, and
average annual loading of total nitrogen and TP were 59 to 206 and 58 to 155 mg/m3,
respectively. Hence, the lake has long been regarded as a shallow (average depth 2.7 m),
large (1990 km2), frequently turbid eutrophic lake in south Florida. Mud sediment
resuspension and transportation can extensively impact the water quality and environment of Lake Okeechobee (Jin and Ji 2004). Higher concentrations of suspended
sediments also change light attenuation and affect the cycling of nutrients, organic
micropollutants, and heavy metals in the water column and sediment bed (Blom et
al. 1992; Van Duin et al. 1992; Jin et al. 2002). Overall, nutrient management for
improving the water quality of this lake through nonpoint source pollution control in
the lake watershed is a long-term issue in the relevant Total Maximum Daily Load
(TMDL) programs. Yet the contribution of phosphorus to the lake’s water column
from internal loading was about equal to the contribution from external loading in
late 1990s (Moore and Reddy 1994). Steinman et al. (1999) indicated that the lake
may not respond to reductions in external phosphorus inputs in the TMDL efforts
due to this high internal loading.
Lake Okeechobee has been threatened in recent decades by excessive phosphorus
loading, harmful extreme high and low water levels, intermittent hurricanes, and
rapid expansion of exotic plants. Four major hurricanes in the past decade, including
Irene in 1999, Frances and Jeanne in 2004, and Wilma in 2005, made landfalls in
this area and impacted the lake’s hydrodynamic pathways and ecosystem (James et
al. 2008). Some hurricanes affected the water quality condition in Lake Okeechobee
through persistent, sustained wind speed (Table 2.1). Resuspension of sediments due
to hurricanes and local wind gusts during the drought period later on also contributed
to nutrient release, regardless of the changing nutrient loads in the Kissimmee River
Basin. Erosion induced by wind promotes the sediment resuspension and diffusion
process. In contrast, a long-term drought from 2000 to 2001 followed by another
from 2007 to 2008 brought about salient ecosystem impacts due to the lower water
depth. Coupling effects of these continuous natural hazards resulted in the resuspension of a large quantity of sediment, lower light transparency, and the release of a
large amount of nutrients into the water column. Nevertheless, a long-term drought