Imaging Spectrometry

by ;
Edition: CD
Format: Hardcover
Pub. Date: 2002-02-01
Publisher(s): Kluwer Academic Pub
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Summary

A significant step forward in the world of earth observation was made with the development of imaging spectrometry. Imaging spectrometers measure reflected solar radiance from the earth in many narrow spectral bands. Such a spectroscopical imaging system is capable of detecting subtle absorption bands in the reflectance spectra and measure the reflectance spectra of various objects with a very high accuracy. As a result, imaging spectrometry enables a better identification of objects at the earth surface and a better quantification of the object properties than can be achieved by traditional earth observation sensors such as Landsat TM and SPOT. The various chapters in the book present the concepts of imaging spectrometry by discussing the underlying physics and the analytical image processing techniques. The second part of the book presents in detail a wide variety of applications of these new techniques ranging from mineral identification, mapping of expansive soils, land degradation, agricultural crops, natural vegetation and surface water quality. CD-ROM included: This volume contains a CD-ROM with hyperspectral remote sensing datasets as well as full color images of a selection of illustrations which are printed as black and white figures in the book.

Table of Contents

Acknowledgments xiii
About the Editors xv
Contributors xvii
Introduction xxi
Part I: Basic principles of imaging spectrometry 1(62)
Basic physics of spectrometry
3(14)
F.D. van der Meer
Introduction
3(1)
Radiation principles
4(1)
Surface scattering properties
4(1)
Reflectance spectroscopy
5(1)
Reflectance properties of materials
6(9)
Minerals and Rocks
7(3)
Vegetation
10(1)
Soils
11(2)
Water
13(1)
Man-made and other materials
14(1)
The effect of the atmosphere
14(1)
Mixing problematics
15(2)
Imaging spectrometry: Basic analytical techniques
17(46)
F.D. van der Meer
S.M. de Jong
W. Bakker
Introduction
17(1)
Imaging spectrometry: airborne systems
18(3)
Airborne Simulators
20(1)
Imaging spectrometry: spaceborne instruments
21(3)
Spaceborne versus airborne data
24(7)
Costs of a spaceborne system
25(1)
Costs of an airborne system
26(1)
Coverage
27(1)
Resolution
28(1)
Geometry
28(1)
Processing
29(1)
Flexibility
30(1)
Complementary data
30(1)
Pre-processing
31(1)
Laboratory set-up of a pre-processing calibration facility
31(1)
The spectral pre-processing chain
32(3)
Spatial pre-processing
35(1)
Quality control: signal to noise characterization
35(4)
The ``homogeneous area method''
36(1)
The ``local means and local variances method''
36(1)
The ``geostatistical method''
37(1)
Noise Adjustment
38(1)
Atmospheric correction
39(2)
Relative Reflectance
39(1)
Absolute Reflectance
40(1)
Re-sampling and image simulation
41(3)
Analytical processing techniques
44(16)
Introduction
44(1)
Binary encoding
44(1)
Waveform characterization
45(2)
Spectral Feature Fitting (SFF)
47(1)
Spectral Angle Mapping (SAM)
47(1)
Spectral unmixing
47(4)
Iterative spectral unmixing
51(4)
Constrained energy minimization (CEM)
55(1)
Foreground-background analysis
55(1)
Classification
56(1)
Cross Correlogram Spectral Matching (CCSM)
56(1)
Geophysical inversion
57(3)
Endmember selection for spectral unmixing and other feature finding algorithms
60(3)
Part II prospective applications of imaging spectrometry 63(298)
Imaging spectrometry for surveying and modelling land degradation
65(22)
S.M. de Jong
G.F. Epema
Introduction
65(1)
Processes of land degradation
66(4)
Soils and Degradation
66(2)
Vegetation and Degradation processes
68(2)
Spectrometry for land degradation
70(1)
Soils
71(5)
Classical Soil Description Methods
71(1)
Soil Spectral Properties
72(4)
Mapping soil degraded state by imaging spectrometry and spectral unmixing
76(4)
Mapping Soil Units using imaging spectrometry and Spectral Matching
77(3)
Vegetation
80(3)
Spectral Reflectance of Vegetation
80(1)
Conventional Remote Sensing and Vegetation
81(1)
Assessing Vegetation Properties for Erosion Models from spectroscopical images
82(1)
Contextual approaches to land cover mapping
83(3)
Conclusions
86(1)
Field and imaging spectrometry for identification and mapping of expansive soils
87(24)
S. Chabrillat
A.F.H. Goetz
H.W. Olsen
L. Krosley
Introduction
87(8)
Nature of expansive soils
87(4)
Spectroscopic indicators of clay minerals
91(4)
Field and laboratory analyses
95(5)
Expansive soils in the Front Range Urban Corridor (Colorado)
95(2)
Field sampling and laboratory analyses
97(1)
Relationships between reflectance, mineralogy and swelling potential
98(2)
Hyperspectral image analysis
100(8)
Expansive clays in Colorado: remote sensing considerations
100(2)
Images acquisition and analysis
102(3)
Mapping results
105(3)
Conclusions
108(3)
Imaging spectrometry and vegetation science
111(46)
L. Kumar
K. Schmidt
S. Dury
A. Skidmore
Introduction
111(2)
Spectroscopy versus spectrometry
113(2)
Fundamental factors affecting vegetation reflectance
115(6)
Leaf optical properties
115(1)
reflectance (400-700 nm.)
115(2)
The reflectance red-edge (690- 720nm)
117(1)
The near-infrared region (700-1300nm)
117(2)
The mid-infrared region (1300 - 2500nm)
119(1)
BRDF
119(2)
Vegetation reflectance curve
121(6)
Pigments
121(3)
Green Leaf Structure
124(3)
Leaf optical models
127(3)
Introduction
127(1)
Ray tracing models
127(1)
Models based on the Kubelka-Munk (K-M) theory
128(1)
The Plate models
129(1)
Stochastic models
130(1)
Vegetation biochemistry
130(7)
Chemical Compounds in Plants and methods of estimation
130(3)
Empirical approach
133(4)
Reflectance models for foliar biochemical estimations
137(3)
Radiative transfer modelling
137(1)
Leaf radiative models
137(1)
Canopy reflectance models
138(1)
Spectral analogies between leaves and canopies
139(1)
Applications: Field spectroscopy for vegetation studies
140(6)
Phenological studies
140(1)
Pigment correlations
141(1)
Water status
142(2)
Plant stress
144(2)
Applications: Airborne imaging spectroscopy for vegetation studies
146(4)
Extracting biophysical variables (e.g. LAI, FAPAR, Cover)
146(3)
Physically-based methods
149(1)
Hyperspectral-BRDF inverse modelling
150(3)
Introduction
150(2)
Biomass/Yield
152(1)
Other applications
153(4)
Extracting Biochemical variables
153(1)
Carbon fluxes
154(1)
Cover
154(3)
Imaging spectrometry for agricultural applications
157(44)
J.P.G.W. Clevers
R. Jongschaap
Introduction
157(1)
Role of imaging spectroscopy in agriculture
158(5)
Introduction
158(1)
Spectral analysis (PCA)
159(1)
Case study
160(1)
MAC Europe campaign
160(1)
Test site
160(1)
AVIRIS
160(1)
Results
161(1)
Conclusions
162(1)
Red-edge index
163(12)
Introduction
163(1)
Definition red-edge index
163(1)
Simulations using radiative transfer models
163(2)
SAIL model
165(1)
PROSPECT model
165(1)
Sensitivity analysis
166(1)
Simulation results at the leaf level
167(1)
Simulation results at the canopy level
167(6)
Atmospheric influence
173(1)
Discussion and conclusions
174(1)
Case study I - Red-edge index and crop nitrogen status
175(4)
Introduction
175(1)
Estimation of nitrogen status
175(1)
Estimation of nitrogen deficiency
176(1)
Managing nitrogen stress
176(1)
Experimental data
176(1)
Set-up of the potato trials
176(1)
Crop measurements
177(1)
Results and discussion
177(1)
Conclusions
178(1)
Case study II - A framework for crop growth monitoring
179(11)
Introduction
179(1)
Framework for yield prediction
179(1)
Crop growth models
179(2)
Estimating LAI
181(1)
Estimating leaf angle distribution (LAD)
182(1)
Estimating leaf optical properties in the PAR region
182(1)
Linking optical remote sensing with crop growth models
183(1)
Experimental data
184(1)
Introduction
184(1)
Ground truth
185(1)
Meteorological data
185(1)
Spectra of single leaves
185(1)
CropScan™ ground-based reflectances
185(1)
CAESAR
185(1)
Results
186(1)
Measurements of leaf optical properties
186(1)
Estimating LAD
186(1)
Estimating LAI
187(1)
Estimating leaf optical properties
187(1)
Results calibration SUCROS
188(1)
Conclusions
189(1)
Case study III - Using MERIS for deriving the red-edge index
190(7)
Introduction
190(1)
Data sets
191(1)
Results and discussion
192(1)
AVIRIS spectra
192(1)
Red-edge index simulation with MERIS
192(3)
Upscaling to the MERIS resolution
195(1)
Conclusions
195(2)
Conclusions
197(4)
Imaging spectrometry and geological applications
201(18)
F. van der Meer
H. Yang
H. Lang
Introduction
201(1)
Mineral mapping; surface mineralogy
201(1)
Mineral mapping; exploration
202(1)
Mineral mapping; lithology
203(1)
Vegetation stress and geobotany
204(1)
Environmental geology
204(1)
Petroleum related studies
205(1)
Atmospheric effects resulting from geologic processes
205(1)
Thermal infrared studies
206(1)
Case-study I: Petroleum case study: seepage detection at Bluff using mineral alteration
206(7)
Mineral alteration
206(1)
The Bluff area and petroleum geology
207(3)
Probe-1 imaging spectrometer data
210(1)
Mineral alteration mapping for microseepage detection at Bluff
211(1)
Quaternary eolian loess deposits (Qe)
211(1)
Dakota Sandstone (Kd)
211(1)
Brushy Basin Member (Jmb)
211(1)
Recapture Member (Jmr)
211(2)
Bluff Sandstone (Jb)
213(1)
Mapping gray-green colored rocks that may associate with hydrocarbon microseepage
213(1)
Case-study II: mining
213(5)
Background on the mining problem
213(2)
Data and analysis
215(2)
Validation and interpretation
217(1)
Discussion
218(1)
Imaging spectrometry and petroleum geology
219(24)
F. van der Meer
H. Yang
S. Kroonenberg
H. Lang
P. van Dijk
K. Scholte
H. van der Werff
Introduction
219(1)
Spectra of organics
219(3)
Hydrocarbon microseepage
222(3)
Introduction
222(2)
Microbial effects and Hydrocarbon-induced surface manifestations
224(1)
Detecting hydrocarbon-induced surface manifestations by remote sensing
225(6)
Bleached red beds
226(1)
Clay mineral alteration
226(1)
Carbonates
227(1)
Geobotanical anomalies
228(3)
In Summary
231(1)
Future trends
232(1)
Trends in exploration
232(1)
Trends in monitoring emissions
233(1)
A case study from Santa Barbara, southern California
233(10)
Petroleum geology of the Southern Californian Basins
233(4)
Hyperspectral data analysis
237(6)
Imaging spectrometry for urban applications
243(40)
E. Ben Dor
Introduction
243(1)
Remote Sensing for Urban Applications
244(1)
Aspects of Remote Sensing of the Urban Environment
245(1)
HSR and Urban Applications
246(2)
Spectral Properties of Urban Material
248(14)
Building a Spectral Library of Urban Objects from the existing database
251(1)
The PUSL Spectra
251(1)
The CASL Spectra
252(7)
Summary for Urban Library from Existing Database
259(1)
Building a Spectral Library of Urban Objects from in Situ Measurements
260(2)
Summary for Urban Library from in-Situ Measurements
262(1)
Spectral Pattern Recognition
262(10)
Examining the Spectral-Based Information for Urban Mapping in the VIS-NIR Region
262(1)
Examining the Spectral-Based Information for Urban Mapping in the VIS-NIR-SWIR Region
263(1)
Examining the Spectral-Based Information for Urban Mapping, Using the TIR Region
264(6)
Special Benefit of the HSR over Urban Areas: Asphalt and Shade
270(1)
Summary and Conclusion for the Spectral Analysis
271(1)
Remote Sensing of the Urban Atmosphere using HSR
272(1)
Recent HSR Urban Applications: A Discussion
273(3)
Recommendation for HSR Utilization over Urban Areas
276(4)
Data Evaluation and Processing
277(1)
Spectral Preprocessing steps:
277(2)
Spatial Pre-processing
279(1)
Atmospheric Correction
279(1)
Mixed Pixel Problem
280(1)
General Summary
280(3)
Imaging spectrometry in the Thermal Infrared
283(24)
M.J. Abrams
S. Hook
M.C. Abrams
Introduction
283(1)
Theory
284(3)
Thermal Emission
284(1)
Spectral Emissivity
284(1)
Emissivity of rocks and minerals
284(3)
Current TIR airborne systems
287(14)
Early scanners
287(1)
TIMS
287(7)
ATLAS, AMSS, AAS, MIVIS
294(2)
Master
296(2)
Sebass
298(3)
Satellite instruments
301(2)
ASTER
301(1)
MTI
302(1)
Future of Hyperspectral TIR Imaging
303(4)
Major Trends in Hyperspectral Remote Sensing
303(2)
The Future - 1-5 Year Forecast:
305(1)
The Future: 5-15 Year Forecast
306(1)
Imaging spectrometry of water
307(54)
A.G. Dekker
V.E. Brando
J.M. Anstee
N. Pinnel
T. Kutser
E.J. Hoogeboom
S. Peters
R. Pasterkamp
R. Vos
C. Olbert
T.J.M. Malthus
Introduction
307(1)
Light in water
308(19)
Introduction to the theory
308(2)
Optically deep waters
310(1)
Optical properties of the water column for optically deep waters
311(1)
The inherent optical properties
311(2)
Radiometric variables and apparent optical properties
313(3)
The diffuse apparent optical properties
316(1)
The two-flow model for irradiance
316(5)
An analytical model for the irradiance reflectance
321(2)
Optical shallow waters
323(2)
Light above water
325(1)
Water surface effects
325(1)
Atmospheric effects and atmospheric correction
326(1)
Optically deep and shallow waters: applications and case studies
327(31)
Introduction
327(1)
Optically deep inland and estuarine waters
327(1)
Imaging spectrometry of optically deep inland waters
327(7)
Imaging spectrometry of optically deep estuaries
334(6)
Conclusions for imaging spectrometry of optically deep inland and estuarine waters
340(1)
Optically shallow waters
341(1)
Bathymetry and bright substrate mapping
341(4)
Macrophyte/seagrass and macro-algae mapping
345(12)
Conclusions imaging spectrometry of optically shallow waters
357(1)
Conclusions
358(3)
Acronyms 361(4)
Index 365(6)
References 371

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