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El. knyga: Multi-sensor System Applications in the Everglades Ecosystem

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This book explores the applicability of multiple remote sensors to acquire information relevant to restoration and conservation efforts in wetlands using data collected from airborne and space multispectral/hyperspectral sensors, light detection and ranging (LiDAR), Unmanned Aircraft Systems (UAS), and a hand-held spectroradiometer. This book also examines digital data processing techniques such as object-based image analysis, machine learning, texture analysis, and data fusion. After an introduction to the Everglades and to remote sensing, the book is divided into four parts based on the sensor systems used. There are chapters on vegetation mapping, biomass and water quality modeling, applications of hyperspectral data for plant stress analysis and coral reef mapping, studies of airborne LiDAR data for coastal vulnerability analysis and DEM improvement, as well as chapters that explore a fusion of multiple sensors for different datasets.

Features











Introduces concepts, theories, and advanced processing techniques





A complete introduction of machine learning, object-based image analysis, data fusion, and ensemble analysis techniques in processing data from multiple remote sensors





Explains how multiple remote sensing systems are applied in the wetland ecosystems of Florida





The author had been teaching and using both systems and her research is widely recognized

Multi-sensor System Applications in the Everglades Ecosystems provides a comprehensive application of remote sensing techniques in the Florida Everglades and its coastal ecosystems. It will prove an invaluable resource for the restoration and conservation of the Florida Everglades and beyond, for global wetlands in general. Any professional, scientist, engineer, or student working with remote sensing and wetland ecosystems will reap enormous benefits from this book.
Series Foreword xiii
Preface xv
Author xvii
Contributors xix
Abbreviations xxi
Part I Florida Everglades and Remote Sensing
1 Florida Everglades and Restoration
3(28)
Caiyun Zhang
1.1 Florida Everglades
3(1)
1.2 Geology and Land forms of the Everglades
4(6)
1.3 Everglades Ecosystem
10(16)
1.3.1 Freshwater Ecosystem
10(10)
1.3.2 Coastal Ecosystem
20(6)
1.4 Everglades Restoration
26(3)
References
29(2)
2 Introduction to Remote Sensing
31(30)
Caiyun Zhang
2.1 Overview of Remote Sensing
31(1)
2.2 Multispectral Remote Sensing
32(16)
2.2.1 Aerial Photography and Products
33(2)
2.2.2 Drone Remote Sensing and Products
35(1)
2.2.3 Spaceborne Sensors and Products
36(12)
2.3 Hyperspectral Remote Sensing
48(8)
2.3.1 Handheld Specttoradiometer to Collect Point Hyperspectral Data
50(1)
2.3.2 Airborne Sensors to Collect Hyperspectral Imagery
51(2)
2.3.3 Spaceborne Sensors to Collect Hyperspectral Imagery
53(3)
2.4 Lidar Remote Sensing
56(3)
2.4.1 Lidar Data Attributes
56(1)
2.4.2 Lidar Data Format
57(1)
2.4.3 Lidar Data Resources in the Everglades
58(1)
References
59(2)
3 Vegetation Classification Systems in the Everglades
61(10)
Caiyun Zhang
3.1 Vegetation Classification for South Florida Natural Areas
61(2)
3.2 FLUCCS
63(1)
3.3 Everglades Vegetation Classification System
64(4)
3.4 KRREP Baseline Vegetation Classification
68(1)
References
68(3)
Part II Multispectral Remote Sensing Applications
4 Applying Aerial Photography to Map Marsh Species in the Wetland of Lake Okeechobee
71(18)
Caiyun Zhang
4.1 Introduction
71(2)
4.2 Study Area and Data
73(2)
4.3 Methodology
75(7)
4.3.1 Image Segmentation to Create Image Objects
76(3)
4.3.2 Classification: SVM, RF, and ANN
79(2)
4.3.3 Accuracy Assessment
81(1)
4.4 Results and Discussion
82(4)
4.4.1 Experimental Analyses to Examine the Contribution of Texture Measures
82(2)
4.4.2 Object-Based Marsh Species and Spatial Uncertainty Mapping
84(2)
4.5 Summary and Conclusions
86(1)
References
86(3)
5 Unmanned Aircraft System (UAS) for Wetland Species Mapping
89(20)
Sara Denka Durgan
Caiyun Zhang
5.1 Introduction
89(4)
5.1.1 Background of UAS
89(1)
5.1.2 Structure from Motion Photogrammetry
90(1)
5.1.3 UAS Data Collection
91(1)
5.1.4 UAS for Vegetation Mapping
92(1)
5.2 Study Site and Data Collection
93(4)
5.2.1 Study Site
93(1)
5.2.2 UAS Data Collection
94(1)
5.2.3 In-situ Data Collection
95(2)
5.3 Methodology for Species Mapping
97(3)
5.3.1 UAS Image Pre-processing
97(1)
5.3.2 UAS Orthoimage Radiometric Correction and Segmentation
98(1)
5.3.3 Data Matching and Manual Interpretation
99(1)
5.3.4 Species Classification
99(1)
5.3.5 Accuracy Assessment
100(1)
5.4 Experimental Analysis and Results
100(2)
5.5 Discussion
102(2)
5.6 Conclusion
104(1)
References
105(4)
6 Spaceborne Multispectral Sensors for Vegetation Mapping and Change Analysis
109(14)
Caiyun Zhang
6.1 Introduction
109(1)
6.2 Data
109(1)
6.3 Methodology
110(8)
6.3.1 Time Series Image Normalization
111(1)
6.3.2 Image Segmentation
112(1)
6.3.3 Training/Testing Sample Selection in the Classification
112(4)
6.3.4 Image Classification and Accuracy Assessment
116(1)
6.3.5 Object-based Post-classification Change Analysis
117(1)
6.4 Results and Discussion
118(4)
6.4.1 Time Series Vegetation Maps in WCA-2A
118(1)
6.4.2 Vegetation Change Analysis Results
119(3)
References
122(1)
7 Water Quality Modeling and Mapping using Landsat Data
123(18)
Caiyun Zhang
7.1 Introduction
123(1)
7.2 Study Area
124(1)
7.3 Developing a Linear Model to Map Water Salinity in Northeast Florida Bay
125(8)
7.3.1 Data
125(2)
7.3.2 Methodology and Results
127(6)
7.4 Applying Geographically Weighted Regression (GWR) to Map Water Salinity in Florida Bay
133(2)
7.4.1 Data
133(1)
7.4.2 Methodology and Results
133(2)
7.5 Exploring an Object-Based Machine-Learning Approach to Assessing Water Salinity
135(3)
7.5.1 Data
136(1)
7.5.2 Methodology and Results
136(2)
References
138(3)
8 Mapping Sawgrass Aboveground Biomass using Landsat Data
141(14)
Caiyun Zhang
8.1 Introduction
141(1)
8.2 Study Area and Data
142(3)
8.3 Methodology
145(5)
8.3.1 Image Normalization
146(1)
8.3.2 Image Segmentation
147(1)
8.3.3 Matching Field Data with Landsat Data
148(1)
8.3.4 Object-based Biomass Model Development
149(1)
8.3.5 Model Evaluation
149(1)
8.4 Results
150(3)
References
153(2)
9 Applying Landsat Products to Assess the Damage and Resilience of Mangroves from Hurricanes
155(20)
David Brodylo
Caiyun Zhang
9.1 Introduction
155(1)
9.2 Study Area and Data
156(2)
9.3 Methodology
158(2)
9.3.1 Identifying Mangroves for the Selected Study Domain using GEE
159(1)
9.3.2 Damage and Recovery Analysis in GEE
159(1)
9.4 Results and Discussion
160(10)
9.4.1 Mangrove Damage Analysis
160(4)
9.4.2 Mangrove Recovery Analysis
164(2)
9.4.3 Discussion
166(4)
9.5 Summary and Conclusions
170(1)
References
170(5)
Part III Hyperspectral Remote Sensing Applications
10 Applying Point Spectroscopy Data to Assess the Effects of Salinity and Sea Level Rise on Canopy Water Content of Juncus roemerianus
175(20)
Donna Selch
Cara J. Abbott
Caiyun Zhang
10.1 Introduction
175(3)
10.2 Study Site and Data Collection
178(3)
10.3 Methodology
181(2)
10.4 Results and Discussion
183(7)
10.4.1 Effects of Salinity and Water Level on the Shoot Height
183(1)
10.4.2 Effects of Salinity and Water Level on Aboveground Biomass and CWC
184(1)
10.4.3 Spectral Response to Plant Stress Caused by Changes of Salinity and Water Levels
185(2)
10.4.4 Identifying the Optimal Spectral Indices for CWC Estimation of J. Roemerianus
187(1)
10.4.5 Derivative Analysis for CWC Estimation
188(2)
10.5 Summary and Conclusions
190(1)
References
191(4)
11 Applying Point Spectroscopy Data to Characterize Sand Properties
195(16)
Molly E. Smith
Donna Selch
Caiyun Zhang
11.1 Introduction
195(1)
11.2 Data Collection, Processing, and Analysis
196(2)
11.3 Results and Discussion
198(10)
11.3.1 Geological/Microscopic Analysis Results
198(2)
11.3.2 Qualitative Analysis of Spectroscopy Data
200(2)
11.3.3 Classification of Sand Color and Grain Size
202(3)
11.3.4 Prediction of Sand Composition using Spectroscopy Data
205(3)
11.4 Summary and Conclusions
208(1)
References
208(3)
12 Land Cover-level Vegetation Mapping using AVIRIS
211(14)
Caiyun Zhang
12.1 Introduction
211(1)
12.2 Study Area and Data
211(3)
12.3 Methodology
214(4)
12.4 Results and Discussion
218(4)
12.5 Summary and Conclusions
222(1)
References
223(2)
13 Species-level Vegetation Mapping in the Kissimmee River Floodplain using HyMap Data
225(18)
Caiyun Zhang
13.1 Introduction
225(1)
13.2 Study Area and Data
225(6)
13.3 Methodology
231(2)
13.4 Results and Discussion
233(5)
13.5 Summary and Conclusions
238(2)
References
240(3)
14 Benthic Habitat Mapping in the Florida Keys using E0-1/Hyperion
243(16)
Caiyun Zhang
14.1 Introduction
243(2)
14.2 Study Area and Data
245(1)
14.3 Methodology
246(3)
14.4 Results and Discussion
249(5)
14.5 Summary and Conclusions
254(1)
References
254(5)
Part IV Lidar Remote Sensing Applications
15 Vulnerability Analysis of Coastal Everglades to Sea Level Rise using SLAMM
259(14)
Hannah Cooper
Caiyun Zhang
15.1 Introduction
259(1)
15.2 Study Area and Data
260(1)
15.3 Methodology
261(5)
15.3.1 Lidar-DEM Transformation, Correction, and Derivation of Slope
261(2)
15.3.2 Land Cover Preparation
263(1)
15.3.3 Accretion and Elevation Change Rates
263(1)
15.3.4 Erosion Rates
263(1)
15.3.5 Sea Level Rise Projections
263(1)
15.3.6 SLAMM Setup and Calibration
264(2)
15.4 Results and Discussion
266(4)
15.4.1 Lidar-DEM Correction and Vertical Accuracy in SLR Applications
266(2)
15.4.2 SLAMM Results
268(2)
15.5 Summary and Conclusions
270(1)
References
270(3)
16 Enhancing Lidar Data Integrity in the Coastal Everglades
273(16)
Hannah Cooper
Caiyun Zhang
16.1 Introduction
273(2)
16.2 Study Area and Data
275(2)
16.3 Methodology
277(6)
16.3.1 Image Segmentation
277(1)
16.3.2 Data Matching
278(1)
16.3.3 Random Forest-based Lidar Data Correction
278(1)
16.3.4 Object-based Lidar-DEM Generation
279(1)
16.3.5 Interpolated Lidar-DEMs
280(2)
16.3.6 Lidar-DEM Accuracy Assessment
282(1)
16.3.7 Lidar Bare-Earth Points Accuracy Assessment
282(1)
16.3.8 Minimum Object-Based Bin (MOBB) and Bias Correction Lidar-DEMs
283(1)
16.4 Results and Discussion
283(4)
16.4.1 Lidar Bare-Earth Errors
283(1)
16.4.2 EBK vs. EBK Bias-Correction of Lidar-DEMs
283(1)
16.4.3 EBK Bias-Correction vs. MOBB Lidar-DEMs
284(1)
16.4.4 Object-based Lidar-DEMs from Machine Learning Models
285(2)
16.5 Summary and Conclusions
287(1)
References
287(2)
17 Assessing the Effects of Hurricane Irma on Mangrove Structures in the Coastal Everglades using Airborne Lidar Data
289(16)
Caiyun Zhang
17.1 Introduction
289(1)
17.2 Study Area and Data
290(2)
17.3 Methodology
292(1)
17.4 Results and Discussion
293(8)
17.4.1 Impacts on Mangrove Canopy Height
293(1)
17.4.2 Increased Canopy Gaps from Hurricane Irma
294(3)
17.4.3 Impacts on Terrains
297(4)
17.5 Summary and Conclusions
301(1)
References
301(4)
Part V Fusing Multiple Sensors for Everglades Applications
18 Integrating Aerial Photography, ED-1/Hyperion, and Lidar Data to Map Vegetation in the Coastal Everglades
305(14)
Caiyun Zhang
18.1 Introduction
305(2)
18.2 Study Area and Data
307(2)
18.3 Methodology
309(5)
18.4 Results and Discussion
314(2)
18.5 Summary and Conclusions
316(1)
References
317(2)
19 Assessing a Multi-sensor Fusion Approach to Map Detailed Reef Benthic Habitats in the Florida Reef Tract
319(14)
Caiyun Zhang
19.1 Introduction
319(1)
19.2 Study Area and Data
320(3)
19.3 Methodology
323(2)
19.4 Results and Discussion
325(4)
19.5 Summary and Conclusions
329(2)
References
331(2)
Index 333
Dr. Zhang received her Ph.D. in Geospatial Information Sciences from University of Texas, Dallas. Her research at FAU focuses on vegetation characterization in the Florida Everglades using multiple sensors, biomass modeling and mapping, water quality monitoring and mapping, and analyzing coastal vulnerability to sea level rise and hurricanes. She has developed innovative methodology frameworks to monitor and map the Greater Everglades by combining multiple sensors and GIS techniques, which can assist with the restoration and conservation of the Florida Everglades ecosystem. She applies modern machine learning and advanced remote sensing image processing techniques in the coastal environments to understand the effects of human activities and natural disasters on the modification of coastal landscapes. She is teaching five major remote sensing courses at FAU including Remote Sensing of Environment, Digital Image Analysis, Hyperspectral Remote Sensing, Lidar Remote Sensing and Applications, and Photogrammetry and Aerial Photo Interpretation.
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