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E-grāmata: Hyperspectral Remote Sensing: Fundamentals and Practices

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Advanced imaging spectral technology and hyperspectral analysis techniques for multiple applications are the key features of the book. This book will present in one volume complete solutions from concepts, fundamentals, and methods of acquisition of hyperspectral data to analyses and applications of the data in a very coherent manner. It will help readers to fully understand basic theories of HRS, how to utilize various field spectrometers and bioinstruments, the importance of radiometric correction and atmospheric correction, the use of analysis, tools and software, and determine what to do with HRS technology and data.

Recenzijas

"Hyperspectral Remote Sensing: Fundamentals and Practices is an excellent resource for both research and classroom needs. Concepts and applications are presented clearly and in a user-friendly fashion. The book is an innovative tool for environmental science practitioners interested in getting up-to-date on remote sensing techniques. The hyperspectral sections, especially, provide excellent content both in theory and applications. This is and will be a wonderful resource for the current generation of remote sensing scientists and students." Arturo Sanchez-Azofeifa, University of Alberta, Canada

"This masterful book provides a comprehensive and much-needed overview of hyperspectral remote sensing and imaging spectroscopy. Covering all aspects of the field, including characteristics of field, aerial and satellite borne sensors, the pre-processing and analysis of hyperspectral data, available software packages, and a wide array of applications, this book will be the key reference for students and those working in the field. The engaging style, detailed theoretical background, and numerous practical insights offer an unparalleled treatment of the subject." Timothy Warner, West Virginia University, USA

"The quantitative retrieval of biophysical properties of the earths surface from remote sensing has become a reality over the past twenty years, largely because of the synergistic development of hyperspectral sensors and detailed physical models of the interaction of electromagnetic radiation for a range of earth surface classes. Implicit in these successes is an in-depth understanding of the corrections to be applied to the sensor data and the specific analysis approaches for each surface type, suited to the available spectral detail. Ruiliang Pus comprehensive and timely book reflects the authors intimate experience with all aspects of hyperspectral remote sensing, including applications --- an invaluable work for students and professionals alike!" John R. Miller, York University, Toronto, Ontario, Canada

"This book provides a comprehensive and timely overview on all aspects of hyperspectral remote sensing combined with various applications. As such, it is an excellent book of reference for both students and professionals active in the field of optical remote sensing. It deals with all aspects of retrieving quantitative information on biophysical properties of the Earths surface, the data corrections needed and the range of analysis approaches available." International Journal of Applied Earth Observation and Geoinformation, January 2018

"Hyperspectral Remote Sensing: Fundamentals and Practices book is likely to prove valuable to upperclassmen, graduate students, and practitioners. It covers the field in depth and draws heavily on extensive literature."

Photogrammetric Engineering & Remote Sensing, September 2018 "Hyperspectral Remote Sensing: Fundamentals and Practices is an excellent resource for both research and classroom needs. Concepts and applications are presented clearly and in a user-friendly fashion. The book is an innovative tool for environmental science practitioners interested in getting up-to-date on remote sensing techniques. The hyperspectral sections, especially, provide excellent content both in theory and applications. This is and will be a wonderful resource for the current generation of remote sensing scientists and students." Arturo Sanchez-Azofeifa, University of Alberta, Canada

"This masterful book provides a comprehensive and much-needed overview of hyperspectral remote sensing and imaging spectroscopy. Covering all aspects of the field, including characteristics of field, aerial and satellite borne sensors, the pre-processing and analysis of hyperspectral data, available software packages, and a wide array of applications, this book will be the key reference for students and those working in the field. The engaging style, detailed theoretical background, and numerous practical insights offer an unparalleled treatment of the subject." Timothy Warner, West Virginia University, USA

"The quantitative retrieval of biophysical properties of the earths surface from remote sensing has become a reality over the past twenty years, largely because of the synergistic development of hyperspectral sensors and detailed physical models of the interaction of electromagnetic radiation for a range of earth surface classes. Implicit in these successes is an in-depth understanding of the corrections to be applied to the sensor data and the specific analysis approaches for each surface type, suited to the available spectral detail. Ruiliang Pus comprehensive and timely book reflects the authors intimate experience with all aspects of hyperspectral remote sensing, including applications --- an invaluable work for students and professionals alike!" John R. Miller, York University, Toronto, Ontario, Canada

"This book provides a comprehensive and timely overview on all aspects of hyperspectral remote sensing combined with various applications. As such, it is an excellent book of reference for both students and professionals active in the field of optical remote sensing. It deals with all aspects of retrieving quantitative information on biophysical properties of the Earths surface, the data corrections needed and the range of analysis approaches available." International Journal of Applied Earth Observation and Geoinformation, January 2018

"Hyperspectral Remote Sensing: Fundamentals and Practices book is likely to prove valuable to upperclassmen, graduate students, and practitioners. It covers the field in depth and draws heavily on extensive literature."

Photogrammetric Engineering & Remote Sensing, September 2018

Foreword xv
Preface xvii
Acknowledgments xix
Author xxi
Introduction xxiii
Chapter 1 Overview of Hyperspectral Remote Sensing 1(30)
1.1 Concepts of Imaging Spectroscopy
1(7)
1.1.1 Spectroscopy
1(1)
1.1.2 Imaging Spectroscopy
2(2)
1.1.3 Hyperspectral Remote Sensing
4(1)
1.1.4 Differences between Hyperspectral and Multispectral Imaging
5(1)
1.1.5 Absorption Features and Diagnostic Spectral Features
6(2)
1.2 Development of Hyperspectral Remote Sensing
8(3)
1.3 Overview of Hyperspectral Remote Sensing Applications
11(9)
1.3.1 Geology and Soils
11(3)
1.3.2 Vegetation and Ecosystems
14(2)
1.3.3 The Atmosphere
16(1)
1.3.4 Coastal and Inland Waters
17(1)
1.3.5 Snow and Ice Hydrology
18(1)
1.3.6 Environmental Hazards
18(1)
1.3.7 Urban Environments
19(1)
1.4 Perspective of Hyperspectral Remote Sensing
20(2)
1.5 Summary
22(1)
References
23(8)
Chapter 2 Field Spectrometers and Plant Biology Instruments for HRS 31(34)
2.1 Non-Imaging Field Spectrometers
31(18)
2.1.1 Introduction
31(2)
2.1.2 Principles of Field Spectroscopy and General Guidelines on Field Techniques
33(5)
2.1.2.1 Principles of Field Spectroscopy
33(4)
2.1.2.2 General Guidelines on Field Technique
37(1)
2.1.3 Field Spectrometers
38(11)
2.1.3.1 ASD Field Spectroradiometers
38(3)
2.1.3.2 SVC (GER) Field Spectroradiometers
41(3)
2.1.3.3 Spectral Evolution Field Spectroradiometers
44(1)
2.1.3.4 SpectraScan Spectroradiometers
45(1)
2.1.3.5 Ocean Optical Spectrometers
46(3)
2.2 Plant Biology Instruments for HRS
49(13)
2.2.1 Introduction
49(1)
2.2.2 Plant Biology Instruments
49(16)
2.2.2.1 Instruments for Measuring Leaf Area and Leaf Area Index
49(5)
2.2.2.2 Instruments for Measuring Photosynthesis and fPAR
54(4)
2.2.2.3 Instruments for Measuring Chlorophyll Content
58(4)
2.3 Summary
62(1)
References
62(3)
Chapter 3 Imaging Spectrometers, Sensors, Systems, and Missions 65(36)
3.1 Working Principles of Imaging Spectrometry
65(2)
3.1.1 Whiskbroom Imaging Spectrometry
65(2)
3.1.2 Pushbroom Imaging Spectrometry
67(1)
3.2 Airborne Hyperspectral Sensors/Systems
67(18)
3.2.1 Advanced Airborne Hyperspectral Imaging Sensor (AAHIS)
67(6)
3.2.2 Airborne Imaging Spectrometer (AIS)
73(1)
3.2.3 Airborne Imaging Spectrometer for Different Applications (AISA)
74(1)
3.2.4 Advanced Solid-State Array Spectroradiometer (ASAS)
74(1)
3.2.5 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)
75(1)
3.2.6 Compact Airborne Spectrographic Imager (CASI)
76(1)
3.2.7 Compact High-Resolution Imaging Spectrograph Sensor (CHRISS)
77(1)
3.2.8 Digital Airborne Imaging Spectrometers (DAIS 7915, 16115)
77(1)
3.2.9 Fluorescence Line Imager (FLI)
77(1)
3.2.10 Hyperspectral Digital Imagery Collection Experiment (HYDICE)
78(1)
3.2.11 Hyperspectral Mapper (HyMap)
79(1)
3.2.12 HyperSpectral Cameras (HySpex)
80(1)
3.2.13 Infrared Imaging Spectrometer (ISM)
80(1)
3.2.14 Modular Airborne Imaging Spectrometer (MAIS)
80(1)
3.2.15 Modular Imaging Spectrometer Instrument (MISI)
81(1)
3.2.16 Multispectral Infrared Camera (MUSIC)
81(1)
3.2.17 Probe-1
81(1)
3.2.18 Reflective Optics System Imaging Spectrometer (ROSIS)
81(1)
3.2.19 SWIR Full Spectrographic Imager (SFSI)
82(1)
3.2.20 Spatially Modulated Imaging Fourier Transform Spectrometer (SMIFTS)
82(1)
3.2.21 TRW Imaging Spectrometers (TRWIS)
83(1)
3.2.22 Variable Interference Filter Imaging Spectrometer (VIFIS)
84(1)
3.2.23 Wedge Imaging Spectrometer (WIS)
84(1)
3.3 Spaceborne Hyperspectral Sensors/Missions
85(10)
3.3.1 Advanced Responsive Tactically Effective Military Imaging Spectrometer (ARTEMIS), TacSat-3 Satellite
86(1)
3.3.2 Compact High-Resolution Imaging Spectrometer (CHRIS) PROBA Satellite
86(3)
3.3.3 Fourier Transform Hyperspectral Imager (FTHSI), MightySat II Satellite
89(1)
3.3.4 Global Imager (GLI), NASDA ADEOS-II Satellite
89(1)
3.3.5 HJ-A/HSI (Hyperspectral Imager, HJ-1A Satellite)
89(1)
3.3.6 Hyperion (Hyperspectral Imager, EO-1 Satellite)
90(1)
3.3.7 HySI (HyperSpectral Imager, IMS-1 Satellite)
91(1)
3.3.8 Medium-Resolution Imaging Spectrometer (MERIS), ESA ENVISAT Satellite
91(1)
3.3.9 Moderate-Resolution Imaging Spectroradiometer (MODIS), Terra/Aqua Satellites
91(1)
3.3.10 Environmental Mapping and Analysis Program (EnMAP)
92(1)
3.3.11 Fluorescence Explorer (FLEX)
93(1)
3.3.12 Hyperspectral Imager Suite (HISUI)
93(1)
3.3.13 Hyperspectral Infrared Imager (HyspIRI)
94(1)
3.3.14 Multisensor Microsatellite Imager (MSMI)
94(1)
3.3.15 Hyperspectral Precursor and Application Mission (PRISMA)
94(1)
3.4 Summary
95(1)
References
96(5)
Chapter 4 Hyperspectral Image Radiometric Correction 101(62)
4.1 Introduction
101(2)
4.2 Atmospheric Effects
103
4.2.1 Atmospheric Refraction
104(1)
4.2.2 Atmospheric Scattering
104(3)
4.2.3 Atmospheric Absorption
107(1)
4.2.4 Atmospheric Transmittance
108
4.3 Correcting Radiometric Errors Induced by Sensors/Systems
101(16)
4.3.1 Introduction to Radiometric Errors Caused by Sensors/Systems
109(2)
4.3.2 De-Striping
111(3)
4.3.3 Correcting Smile- and Keystone-Induced Errors
114(3)
4.4 Atmospheric Correction Methods
117(30)
4.4.1 Introduction to Atmospheric Correction
117(2)
4.4.2 Empirical/Statistical Methods
119(5)
4.4.2.1 The Empirical Line Calibration (ELC)
119(4)
4.4.2.2 Internal Average Reflectance (IAR) and Flat Field Correction (FFC)
123(1)
4.4.3 Radiative Transfer Methods
124(21)
4.4.3.1 Atmospheric Correction Now (ACORN)
124(3)
4.4.3.2 Atmospheric Correction (ATCOR)
127(1)
4.4.3.3 Atmosphere Removal (ATREM)
128(3)
4.4.3.4 Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH)
131(3)
4.4.3.5 High-Accuracy Atmospheric Correction for Hyperspectral Data (HATCH)
134(3)
4.4.3.6 Imaging Spectrometer Data Analysis System (ISDAS)
137(1)
4.4.3.7 Comparison
138(7)
4.4.4 Relative Correction Methods
145(2)
4.5 Techniques for Estimating Atmospheric Water Vapor and Aerosols
147(9)
4.5.1 Atmospheric Water Vapor
147(4)
4.5.1.1 Narrow/Wide (N/W) Technique
147(2)
4.5.1.2 Continuum Interpolated Band Ratio (CIBR)
149(1)
4.5.1.3 Three-Band Ratioing (3BR)
150(1)
4.5.1.4 Linear Regression Ratio (LIRR)
150(1)
4.5.1.5 Atmospheric Pre-Corrected Differential Absorption (APAD)
151(1)
4.5.2 Atmospheric Aerosols
151(13)
4.5.2.1 Dark Dense Vegetation (DDV) Technique
152(1)
4.5.2.2 Aerosol Optical Thickness at 550 nm (AOT at 550 nm)
153(3)
4.6 Summary
156(1)
References
157(6)
Chapter 5 Hyperspectral Data Analysis Techniques 163(66)
5.1 Introduction
163(1)
5.2 Spectral Derivative Analysis
164(1)
5.3 Spectral Similarity Measures
164(6)
5.3.1 Cross-Correlogram Spectral Matching (CCSM)
166(2)
5.3.2 Spectral Angle Matching (SAM)
168(1)
5.3.3 Euclidian Distance (ED)
169(1)
5.3.4 Spectral Information Divergence (SID)
169(1)
5.4 Spectral Absorption Features and Wavelength Position Variables
170(8)
5.4.1 Four-Point Interpolation
171(1)
5.4.2 Polynomial Fitting
172(1)
5.4.3 Lagrangian Technique
172(2)
5.4.4 IG Modeling
174(2)
5.4.5 Linear Extrapolation
176(2)
5.5 Spectral Vegetation Indices
178(7)
5.6 Hyperspectral Transformation and Feature Extraction
185(11)
5.6.1 Principal Components Analysis (PCA)
185(1)
5.6.2 Signal-to-Noise Ratio-Based Image Transforms
186(3)
5.6.2.1 Maximum Noise Fraction (MNF) Transform
186(1)
5.6.2.2 Noise-Adjusted Principal Component Transform
187(2)
5.6.3 Independent Component Analy'sis
189(3)
5.6.4 Canonical Discriminant Analysis (CDA)
192(2)
5.6.5 Wavelet Transform
194(2)
5.7 Spectral Mixture Analysis (SMA)
196(10)
5.7.1 Traditional Spectral Unmixing Modeling Techniques
197(4)
5.7.2 Artificial Neural Networks Solution to LSM
201(1)
5.7.3 Multiple End-Member Spectral Mixture Analysis (MESMA)
201(1)
5.7.4 Mixture-Tuned Matched Filtering Technique (MTMF)
202(1)
5.7.5 Constrained Energy Minimization (CEM)
203(1)
5.7.6 End-Member Extraction
204(2)
5.7.6.1 Pixel Purity Index (PPI)
205(1)
5.7.6.2 N-Finder
205(1)
5.8 Hyperspectral Image Classifications
206(12)
5.8.1 Segment-Based Multispectral Classifiers
207(3)
5.8.2 Artificial Neural Networks (ANN)
210(3)
5.8.3 Support Vector Machines
213(17)
5.8.3.1 Linear SVM for a Separable Case
213(2)
5.8.3.2 Linear SVM for a Nonseparable Case
215(1)
5.8.3.3 Nonlinear SVM: Kernel Method
216(1)
5.8.3.4 SVMs for Multiclass Classification
217(1)
5.9 Summary
218(1)
References
219(10)
Chapter 6 Hyperspectral Data Processing Software 229(34)
6.1 Introduction
229(1)
6.2 ENVI
230(6)
6.2.1 Atmospheric Correction
230(2)
6.2.2 Building a 3D Image Cube and Plotting Spectral Curve
232(1)
6.2.3 Data Transformation
232(1)
6.2.4 End-Member Determination and Extraction
233(1)
6.2.5 Spectral Unmixing
234(1)
6.2.6 Target Detection
234(1)
6.2.7 Mapping and Discriminant Methods
235(1)
6.2.8 Vegetation Analysis and Suppression
236(1)
6.3 ERDAS IMAGINE
236(4)
6.3.1 IMAGINE Spectral Analysis Workstation
237(1)
6.3.2 Anomaly Detection
237(1)
6.3.3 Target Detection
237(1)
6.3.4 Material Mapping
237(2)
6.3.5 Material Identification
239(1)
6.3.6 Atmospheric Adjustment
239(1)
6.4 IDRISI
240(2)
6.4.1 Hyperspectral Signature Development
240(2)
6.4.2 Hyperspectral Image Classification
242(1)
6.4.3 Extraction of Absorption Features
242(1)
6.5 PCI Geomatics
242(3)
6.5.1 Data Visualization
243(1)
6.5.2 Atmospheric Correction
243(1)
6.5.3 Hyperspectral Unmixing and Mapping
243(2)
6.6 TNTmips
245(4)
6.6.1 Hyperspectral Explorer Tool
245(1)
6.6.2 Atmospheric Correction
245(1)
6.6.3 Hyperspectral Image Transformation
246(1)
6.6.4 Hyperspectral Unmixing and Mapping
247(2)
6.7 Other Minor Software Tools and Programs for Processing Hyperspectral Data
249(11)
6.7.1 DARWin
249(1)
6.7.1.1 Set Smoothing Filter Width
249(1)
6.7.1.2 EZ-ID Quick Material Identification Tool
249(1)
6.7.1.3 Vegetation Indices
250(1)
6.7.2 Hyperspectral Image Processing and Analysis System (HIPAS)
250(1)
6.7.3 Imaging Spectrometer Data Analysis Systems (ISDAS)
251(1)
6.7.4 Integrated Software for Imagers and Spectrometers (ISIS)
251(1)
6.7.5 MATLAB°
252(1)
6.7.6 MultiSpec
253(1)
6.7.7 Optical Real-Time Adaptive Spectral Identification System (ORASIS)
253(1)
6.7.8 Processing Routines in IDL for Spectroscopic Measurements (PRISM)
254(1)
6.7.9 SPECMIN
255(1)
6.7.10 Spectrum Processing Routines (SPECPR)
256(1)
6.7.11 Tetracorder
256(1)
6.7.12 The Spectral Geologist (TSG)
257(3)
6.8 Summary
260(1)
References
261(2)
Chapter 7 Hyperspectral Applications in Geology and Soil Sciences 263(62)
7.1 Introduction
263(2)
7.2 Spectral Characteristics of Minerals/Rocks
265(5)
7.2.1 Spectral Absorption Characteristics Caused by the Electronic Processes
265(3)
7.2.1.1 Due to Crystal-Field Effects
265(2)
7.2.1.2 Due to Charge Transfer
267(1)
7.2.1.3 Due to Color Centers
267(1)
7.2.1.4 Due to Conduction Band Transitions
268(1)
7.2.2 Spectral Absorption Characteristics Caused by the Vibrational Processes
268(1)
7.2.2.1 Due to Molecular Water
268(1)
7.2.2.2 Due to Hydroxyl
269(1)
7.2.2.3 Due to Carbonate, Borate, and Phosphate
269(1)
7.2.3 Spectral Absorption Characteristics of Alteration Minerals
269(1)
7.3 Analytical Techniques and Methods in Geological Applications
270(30)
7.3.1 Spectral Absorption Feature Extraction in Spectra of Minerals
271(6)
7.3.2 Identifying and Mapping Minerals Using Hyperspectral Mineral Indices
277(2)
7.3.3 Identifying and Mapping Minerals Using Spectral Matching Methods
279(2)
7.3.3.1 SAM
280(1)
7.3.3.2 CCSM
281(1)
7.3.4 Estimating and Mapping the Abundance of Minerals Using Spectral Unmixing Methods
281(5)
7.3.4.1 Linear Spectral Mixing (LSM)
282(1)
7.3.4.2 MESMA
283(1)
7.3.4.3 MTMF
284(1)
7.3.4.4 CEM
285(1)
7.3.5 Estimating and Mapping the Abundance of Minerals Using Spectral Modeling Methods
286(6)
7.3.6 Mapping Minerals Using Advanced Techniques and Methods
292(8)
7.3.6.1 ANNs
292(1)
7.3.6.2 Expert Systems
293(2)
7.3.6.3 SVMs
295(5)
7.4 Hyperspectral Applications in Soil Sciences
300(8)
7.4.1 Spectral Characteristics of Soils
300(1)
7.4.2 Review of Hyperspectral Applications in Soils
301(7)
7.4.2.1 Soil Degradation (Salinity, Erosion, and Deposition)
301(3)
7.4.2.2 Soil Organic Matter (SOM) and Soil Organic Carbon (SOC)
304(1)
7.4.2.3 Soil Moisture
305(1)
7.4.2.4 Soil Contamination
306(1)
7.4.2.5 Soil Classification and Mapping
307(1)
7.5 Hyperspectral Applications in Geology: Case Studies
308(9)
7.5.1 Case I: Mapping Multiple Surficial Materials Using HyMap Data-Derived Absorption Features
308(2)
7.5.1.1 Study Area, HSI Data, and Image Preprocessing
309(1)
7.5.1.2 Mapping Methodology
309(1)
7.5.1.3 Mapping Results
310(1)
7.5.1.4 Concluding Remarks
310(1)
7.5.2 Case II: Mapping Surface Hydrothermal Alteration Minerals Using Airborne AVIRIS and Satellite Hyperion Imagery
310(5)
7.5.2.1 Study Area and Hyperspectral Data
310(1)
7.5.2.2 Mapping Methodology
310(3)
7.5.2.3 Results
313(1)
7.5.2.4 Concluding Remarks
314(1)
7.5.3 Case III: Mapping Volcanogenic Massive Sulfide Deposits Using HyMap Imagery
315(11)
7.5.3.1 Study Area and Hyperspectral Data
315(1)
7.5.3.2 Mapping Methodology
315(1)
7.5.3.3 Results
315(2)
7.5.3.4 Concluding Remarks
317(1)
7.6 Summary
317(1)
References
317(8)
Chapter 8 Hyperspectral Applications to Vegetation 325(64)
8.1 Introduction
325(1)
8.2 Spectral Characteristics of Typical Green Plants
326(11)
8.2.1 Green Leaf Structure and Plant Spectral Reflectance Curve
326(5)
8.2.1.1 Visible Light Absorbed by Multiple Plant Pigments
328(1)
8.2.1.2 NIR Radiation Reflected by Multiscattering of Internal Cellular Structure
328(3)
8.2.1.3 MIR Energy Absorbed by Water and Other Biochemical Constituents
331(1)
8.2.2 Spectral Characteristics of Plant Biophysical Parameters
331(3)
8.2.2.1 Leaf Area Index, Specific Leaf Area, and Crown Closure
331(2)
8.2.2.2 Species and Composition
333(1)
8.2.2.3 Biomass, NPP, and fPAR or fAPAR
334(1)
8.2.3 Spectral Characteristics of Plant Biochemical Parameters
334(3)
8.2.3.1 Pigments: Chlorophylls, Carotenoids, and Anthocyanins
335(1)
8.2.3.2 Nutrients: N, P, and K
335(1)
8.2.3.3 Leaf Water Content
336(1)
8.2.3.4 Other Biochemicals: Lignin, Cellulose, Pectin, and Protein
336(1)
8.3 Analytical Techniques and Methods Needed in Vegetation Applications
337(19)
8.3.1 Plant Spectral Derivative Analysis
337(4)
8.3.2 Plant Spectral Absorption Feature and Wavelength Position Variable Analysis
341(1)
8.3.3 Spectral Vegetation Index Analysis
342(2)
8.3.4 Plant Spectral Unmixing Analysis
344(1)
8.3.5 Plant Spectral Matching Analysis
344(1)
8.3.6 Plant Spectral Classification Analysis
345(1)
8.3.7 Empirical/Statistical Analysis Methods
346(3)
8.3.8 Physically Processing-Based Modeling Methods
349(1)
8.3.9 Bioparameter Mapping Methods
350(6)
8.4 Estimation of Biophysical Parameters
356(8)
8.4.1 Plant Canopy LAI, SLA, and Crown Closure
356(3)
8.4.2 Plant Species and Composition
359(2)
8.4.3 Plant Biomass, NPP, fPAR or fAPAR
361(3)
8.5 Estimation of Biochemical Parameters
364(10)
8.5.1 Plant Pigments: Chls, Cars, and Anths
364(3)
8.5.2 Plant Nutrients: N, P, and K
367(3)
8.5.3 Leaf and Canopy Water Content
370(2)
8.5.4 Other Plant Biochemicals: Lignin, Cellulose, and Protein
372(2)
8.6 Summary
374(1)
References
375(14)
Chapter 9 Hyperspectral Applications to Environments 389(62)
9.1 Introduction
389(1)
9.2 Estimation of Atmospheric Parameters
389(16)
9.2.1 Water Vapor (H20)
391(7)
9.2.2 Clouds
398(2)
9.2.3 Aerosols
400(3)
9.2.4 Carbon Dioxide (CO2)
403(2)
9.3 Snow and Ice Hydrology
405(5)
9.4 Coastal Environments and Inland Waters
410(18)
9.4.1 Inland Waters
411(6)
9.4.2 Coastal Environments
417(11)
9.5 Environmental Hazards and Disasters
428(5)
9.5.1 Mining Wastes and Tailings
428(2)
9.5.2 Biomass Burning
430(2)
9.5.3 Landslide Monitoring
432(1)
9.6 Urban Environments
433(6)
9.6.1 Spectral Properties of Urban Materials
433(1)
9.6.2 Urban Materials and LULC Types
434(5)
9.6.3 Urban Thermal Environment
439(1)
9.7 Summary
439(1)
References
440(11)
Index 451
Dr. Ruiliang Pu is currently an Associate Professor with the School of Geosciences, University of South Florida (USF). He earned a Ph.D. (Cartography & Geographic Information System) in 2000, conducted at University of California at Berkeley, from Chinese Academy of Sciences. He was a visiting scholar in Institute for Space and Terrestrial Sciences, Ontario, Canada and a visiting scholar at The University of Calgary.He was an Assistant Researcher at UC Berkeley, and a Visiting Research Scientist with Earth System Science Interdisciplinary Center, University of Maryland at College Park before moving to University of South Florida (USF) in 2006. His research experience and interests are in remote sensing, GIS and spatial statistics with direct applications to natural hazard monitoring, land use/cover change detection, biophysical and biochemical parameters extraction, and coastal and terrestrial ecosystems modeling. He has published more than 82 (41 related to hyperspectral remote sensing application studies on vegetation/plants
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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