Jones / Reinke Innovations in Remote Sensing and Photogrammetry

1. Auflage 2009
ISBN: 978-3-540-93962-7
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 478 Seiten

Remote sensing of our environment is becoming increasingly accessible and important in today's society. This book aims to highlight some of the broad and multi-disciplinary applications, and emerging practices, that remote sensing and photogrammetric technologies lend themselves to. The papers have been selected from the 13th and 14th Australasian Remote Sensing and Photogrammetry Conferences given by experts in remote sensing, spatial analysis and photogrammetry from across the Asia Pacific region. They are presented here as a collection of peer reviewed papers covering research into areas such as data fusion techniques and their applications in environmental monitoring, synoptic monitoring and data processing, terrestrial and marine applications of remote sensing, and photogrammetry.

Autoren/Hrsg.

Jones, Simon

Reinke, Karin

Inhaltsverzeichnis

1;Preface and Acknowledgements;5
2;Contents;8
3;Contributors;12
4;Introduction: Visualising Uncertainty in Environmental Data;18
4.1; Introduction;18
4.1.1; Describing Uncertainty in Environmental Data;19
4.1.2; Representing Environmental Data as a Source of Uncertainty;20
4.1.2.1; Category Uncertainty;21
4.1.2.2; Boundary Uncertainty;22
4.2; Visualisation Methods;23
4.2.1; Methods for Visualising Environmental Uncertainty;23
4.3; Summary;29
4.4;References;29
5;Part I Data Fusion Techniques and Their Applications in Environmental Monitoring;32
5.1;A Comparison of Pixel- and Object-Level Data Fusion Using Lidar and High-Resolution Imagery for Enhanced Classification;33
5.1.1; Introduction;33
5.1.2; Background;34
5.1.2.1; Imagery and Lidar Data Fusion;35
5.1.2.2; Pixel- and Object-Level Fusions;35
5.1.3; Study Area and Materials;36
5.1.3.1; Lidar Data;36
5.1.3.2; Multispectral Imagery;37
5.1.3.3; Colour Imagery;37
5.1.4; Methodology;37
5.1.4.1; Geometric Corrections;37
5.1.4.2; Normalized Digital Surface Model (nDSM) Generation;38
5.1.5; Pixel-Level Fusion;39
5.1.6; Object-Level Fusion;40
5.1.6.1; Image Segmentation;40
5.1.6.2; Class Hierarchy;41
5.1.6.3; Fusion Based on Spectral Properties;42
5.1.6.4; Fusion Based on nDSM Properties;43
5.1.6.5; Fusion Using Contextual Information;43
5.1.7; Accuracy Assessment;43
5.1.8; Results and Discussions;44
5.1.9; Conclusions;46
5.1.10;References;46
5.2;Combining Texture and Hyperspectral Information for the Classification of Tree Species in Australian Savanna Woodlands;48
5.2.1; Introduction;48
5.2.2; Study Site and Datasets;49
5.2.3; Methods;50
5.2.3.1; Airphoto Interpretation;50
5.2.3.2; Image Filtering;51
5.2.3.3; Remotely Sensed Data and Association to Forest Types;51
5.2.3.4; Classification;52
5.2.4; Results;52
5.2.5; Discussion;53
5.2.6; Conclusions;53
5.2.7;References;54
5.3;High-Resolution Satellite Imaging in Remote Regions: A Case Study in Bhutan;56
5.3.1; Introduction;56
5.3.2; QuickBird Basic Imagery;18
5.3.3; Geopositioning;58
5.3.4; Height Modelling;59
5.3.4.1; DSM Generation;59
5.3.4.1.1; ISAE;22
5.3.4.1.2; MPGC;60
5.3.4.1.3; SRTM;60
5.3.4.2; Accuracy Assessment;60
5.3.5; Data Integration: Orthorectification, Pansharpening and Visualisation;63
5.3.6; Concluding Remarks;63
5.3.7;References;29
5.4;A New Dataset for Forest Height Across Australia: Pilot Project to Calibrate ICESat Laser Data with Airborne LiDAR;66
5.4.1; Introduction;67
5.4.2; Research Design;67
5.4.3; Analysis Methods;69
5.4.4; Results;70
5.4.4.1; Initial Continental Summary;70
5.4.4.2; Ground Elevation Comparisons;70
5.4.4.3; Vegetation Comparisons in NE Victoria;71
5.4.4.3.1; ICESat Comparison Case Study in NE Victoria;72
5.4.4.4; Updated Continental Summary;76
5.4.5; Discussion;77
5.4.6; Conclusions;78
5.4.7;References;78
5.5;Linking Biological Survey Information to Remote Sensing Datasets: A Case Study;80
5.5.1; Introduction;80
5.5.1.1; Multi-Spectral Remote Sensing of Vegetation;81
5.5.1.2; Ground Data Collection Issues;81
5.5.1.3; Study Area;84
5.5.1.4; Remotely Sensed Data;85
5.5.2; Development of a Vegetation Ground Data Collection Protocol;85
5.5.2.1; Determining Field Site Dimensions;85
5.5.2.2; Sampling Vegetation Variables Within the Field Site;87
5.5.3; Conclusions;88
5.5.4;References;89
6;Part II Synoptic Monitoring and Data Processing;92
6.1;Characterizing the Landsat Global Long-Term Data Record;93
6.1.1; Introduction;93
6.1.2; US Archive Status;94
6.1.3; International Archive Status;98
6.1.4; Achieving a Global Archive;99
6.1.4.1; Metadata;99
6.1.4.2; Archive Maintenance;99
6.1.4.3; Access Standards;100
6.1.4.4; Data Utility;100
6.1.5; Conclusions;100
6.1.6;References;101
6.2;Evaluation of Alternative Sensors for a Landsat-Based Monitoring Program;102
6.2.1; Introduction;102
6.2.2; Test Areas;103
6.2.3; Image Data and Scene Selection;105
6.2.4; Raw Image Quality Issues;106
6.2.5; Ortho-rectification Issues;107
6.2.6; Calibration;109
6.2.7; Mosaicking;111
6.2.8; Thresholding Issues;112
6.2.9; Comparison of Time Series Results;113
6.2.10; Conclusions;116
6.2.11;References;117
6.3;Evaluation of CBERS Image Data: Geometric and Radiometric Aspects;118
6.3.1; Introduction;118
6.3.2; CBERS Background;119
6.3.3; CBERS Image Datasets;120
6.3.3.1; CBERS Guangzhou Dataset;120
6.3.3.2; CBERS QLD Dataset;121
6.3.3.3; CBERS WA Dataset;121
6.3.4; CBERS Ortho-Rectification;122
6.3.4.1; CBERS WA Dataset;122
6.3.4.2; CBERS Guangzhou Dataset;124
6.3.5; CBERS Radiometric Calibration;125
6.3.5.1; CBERS Radiometric Calibration Approach;126
6.3.5.2; CBERS Calibration Results;127
6.3.6; Thresholding Issues;127
6.3.6.1; CBERS WA Dataset;127
6.3.7; Conclusions;128
6.3.8;References;130
6.4;Mapping and Monitoring Wetlands Around the World Using ALOS PALSAR: The ALOS Kyoto and Carbon Initiative Wetlands Products;131
6.4.1; Introduction;131
6.4.2; PALSAR;132
6.4.3; Wetland Theme;134
6.4.4; Application of ALOS-PALSAR to Wetland Applications;136
6.4.5; Wetland Theme Products;137
6.4.5.1; Tropical Wetland Extent and Properties;138
6.4.5.2; Wetland Extent, Inundation Patterns and Vegetation Change in the Greater Mekong River Basin;138
6.4.5.3; Global Mangrove Extent and Properties;139
6.4.5.4; Tropical Peat Swamp Forests and Properties;140
6.4.5.5; Pan-Asian Mapping and Monitoring of Rice Paddies;143
6.4.6; Conclusions;144
6.4.7;References;144
6.5;Testing of Alternate Classification Procedures Within an Operational, Satellite Based, Forest Monitoring System;147
6.5.1; Background;147
6.5.2; Methodology;149
6.5.2.1; Matching, Random Forests and Other Classifiers;149
6.5.2.2; Data;150
6.5.2.3; Comparison Rationale;151
6.5.3; Results;151
6.5.3.1; Single Date Classification;151
6.5.3.2; Effect of Altering Base Image;154
6.5.3.3; Effect of Classifier on Results After Spatial-Temporal Processing (Step 8);155
6.5.4; Discussion and Conclusion;155
6.5.5;References;158
6.6;An Investigation of the Remote Sensing of Aerosols Based on MODIS Data for Western Australian Conditions;159
6.6.1; Introduction;160
6.6.2; MOD04 and MOD09 Aerosol Retrieval Algorithms;160
6.6.3; Evaluation of AOD Retrieval by MOD04 and MOD09;163
6.6.3.1; Spatial Coverage and Retrieval Validity;163
6.6.4; Reflectance Change from Time-Series BRF;168
6.6.4.1; AOD Retrieval from Time-Series BRF;169
6.6.5; Conclusion and Further Work;172
6.6.6;References;173
6.7;Improved Near-Real Time Atmospheric Correction of MODIS Data for Earth Observation Applications;174
6.7.1; Introduction;174
6.7.2; Methodology;175
6.7.2.1; Algorithm Development;175
6.7.2.2; Sensitivity Study;177
6.7.2.3; The Role of Validation;177
6.7.3; Results and Discussion;179
6.7.3.1; Surface Reflectance Sensitivity;179
6.7.3.2; Field Site Characterisation;180
6.7.3.3; Validation of the MOD09 Algorithm;183
6.7.4; Conclusions and Future Work;184
6.7.5;References;185
6.8;Near-Real Time Satellite Products to Drive Australia-Wide Land Surface Monitoring and Modelling of Surface Water and Energy Balance;186
6.8.1; Introduction;186
6.8.2; Solar Radiation;187
6.8.3; AVHRR Products;190
6.8.3.1; Data Acquisition;191
6.8.3.2; Generic Processing;191
6.8.3.3; AVHRR Reflectance Products;192
6.8.3.4; Land Surface Temperature;192
6.8.3.5; Planned Improvements;194
6.8.4; Conclusions;195
6.8.5;References;196
6.9;Recent and Future Developments in Meteorological Remote Sensing;198
6.9.1; Introduction;199
6.9.2; Polar Orbiting Satellite Data;200
6.9.2.1; ATOVS Atmospheric Sounder;201
6.9.2.2; X-Band Network;202
6.9.2.3; Advanced Atmospheric Sounders;203
6.9.3; Geostationary Satellite Data;203
6.9.4; Data Exchange;204
6.9.4.1; Regional ATOVS Retransmission Service;204
6.9.4.2; Advanced Dissemination Methods;205
6.9.5; Interagency Collaboration;205
6.9.6; Conclusions;206
6.10;Improved Spatial Resolution of Fire Detection with MODIS Using the 2.1 m Channel;208
6.10.1; Introduction and Method;208
6.10.1.1; Apparent Reflectance;210
6.10.1.2; Properties of Detectable Fires;210
6.10.1.3; Determination of Background Reflectance and Change;211
6.10.1.4; Cloud Masking;212
6.10.1.5; Identification of Fire Pixels;212
6.10.2; Results;213
6.10.3; Conclusions;213
6.10.4;References;214
7;Part III Terrestrial Applications of Remote Sensing;215
7.1;Characterizing Eucalypt Leaf Phenology and Stress with Spectral Analysis;216
7.1.1; Introduction;216
7.1.1.1; Remote Sensing of Canopy Health in Eucalypt Forests;216
7.1.1.2; Detecting the Difference Between a Stress Response and Changes Due to Normal Leaf Aging;217
7.1.2; Materials and Methods;218
7.1.2.1; Eucalyptus Globulus;219
7.1.2.2; Eucalyptus Pilularis;221
7.1.3; Results and Discussion;222
7.1.3.1; Eucalyptus Globulus;222
7.1.3.2; Eucalyptus Pilularis;223
7.1.4; An Approach to Detect Stress in Eucalypt Plantations;227
7.1.5;References;229
7.2;Development of Satellite Vegetation Indices to Assess Grassland Curing Across Australia and New Zealand;233
7.2.1; Introduction;233
7.2.1.1; Motivation;233
7.2.1.2; Aims;234
7.2.2; Background;235
7.2.2.1; Grasslands;235
7.2.2.2; Grassland Curing;235
7.2.2.3; Grassfires;235
7.2.2.4; Fuel Moisture Content;236
7.2.2.5; Vegetation Indices;237
7.2.2.6; Satellite Remote Sensors;237
7.2.2.7; Remote Sensing of Grassland Curing;239
7.2.3; Methods;240
7.2.3.1; Field Data;240
7.2.3.2; Satellite Data;242
7.2.4; Results and Discussion;242
7.2.5; Conclusions;246
7.2.6;References;247
7.3;Assessment of Grassland Curing Using Field-Based Spectrometry and Satellite Imagery;250
7.3.1; Background;250
7.3.2; Methods;251
7.3.3; Results and Discussion;253
7.3.4; Conclusions;256
7.3.5;References;257
7.4;Airborne Fire Intelligence;259
7.4.1; Introduction;259
7.4.2; Fire Intelligence;262
7.4.2.1; Daedalus Airborne Scanner;263
7.4.2.2; Digital Air Observers;265
7.4.2.3; Digital Hand-Held Cameras;267
7.4.2.4; FLIR;268
7.4.2.5; TIR Camera System;269
7.4.3; Discussion;270
7.4.4; Conclusion;272
7.4.5;References;273
7.5;Give Me the Dirt: Detection of Gully Extent and Volume Using High-Resolution Lidar;274
7.5.1; Introduction;274
7.5.2; Methods;275
7.5.2.1; Study Sites and Creation of a Baseline of Gully Information;275
7.5.2.2; Object-Oriented Classification of Gullies;277
7.5.2.3; Gully Volume;278
7.5.2.4; Models of Gully Information;278
7.5.2.5; Ancillary Variables;278
7.5.2.6; Extrapolation of Gully Volume at Unsampled Locations;279
7.5.3; Results;280
7.5.3.1; Object-Oriented Classification of Gullies;280
7.5.3.2; Gully Volume;281
7.5.3.3; Extrapolation of Gully Information;281
7.5.4; Discussion;285
7.5.4.1; LiDAR Transects as a Baseline of Gully Information;285
7.5.4.2; Object-Oriented Classification of Gullies;285
7.5.4.3; Transect Gully Volume;286
7.5.4.4; Extrapolation of Gully Information;286
7.5.5; Conclusions;286
7.5.6;References;287
7.6;Integrating Mineralogical Interpretation of HyLogger Data with HyMap Mineral Mapping, Mount Painter, South Australia;289
7.6.1; Introduction;289
7.6.2; Geological Context;290
7.6.3; Methods;292
7.6.4; HyMap;292
7.6.5; CSIRO HyLogger;292
7.6.6; Ground Validation;292
7.6.7; Data;292
7.6.7.1; HyMap;293
7.6.7.2; CSIRO HyLogger;294
7.6.7.3; Ground Validation;295
7.6.8; Results;295
7.6.9; Discussion;297
7.6.10; Conclusions;297
7.6.11;References;298
7.7;A Preliminary Study of Mapping Biomass and Cover in NZ Grasslands Using Multispectral Narrow-Band Data;299
7.7.1; Materials and Methods;299
7.7.1.1; New Zealand Grasslands;300
7.7.2; Spectral and Biophysical Measurements;303
7.7.3; Results;303
7.7.3.1; Biophysical Measurements;303
7.7.4; Conclusions;307
7.7.5;References;308
7.8;Comparing Common Hyperspectral Vegetation Indices for Their Ability to Estimate Seasonal Nitrogen and Other Variables in Winter Wheat Across a Growing Season;309
7.8.1; Introduction;309
7.8.2; Methods;311
7.8.2.1; Field Setup;311
7.8.2.2; Hyperspectral Measurements;311
7.8.2.3; Sample Analysis;312
7.8.2.4; Data Processing;312
7.8.2.5; Index Calculation;313
7.8.2.6; Relation Between Indices and Vegetation Characteristics;313
7.8.3; Results;313
7.8.3.1; Treatment Effects;313
7.8.3.2; Relation Between Indices and Vegetation Characteristics;315
7.8.3.2.1; Sample Dry Weight;317
7.8.3.2.2; Sample Wet Weight;317
7.8.3.2.3; Green Leaf Area Index;318
7.8.3.2.4; Nitrogen Concentration;319
7.8.3.2.5; Nitrogen Per Square Meter;319
7.8.3.2.6; Yield;319
7.8.4; Discussion;319
7.8.4.1; Yield Forecasting;321
7.8.4.2; Data Acquisition and Accuracy;321
7.8.5; Conclusions;322
7.8.6; Appendix: Summary of the Indices Used in this Study;322
7.8.6.1; Indices Typically Used for Biomass (Green Leaf Area);322
7.8.6.2; Indices Typically Used for Chlorophyll/Foliar N;323
7.8.6.3;References;324
7.9;The Spectral Response of Pastures in an Intensively Managed Dairy System;326
7.9.1; Introduction;326
7.9.2; Approach;329
7.9.2.1; Study Site;329
7.9.2.2; Image Data;329
7.9.2.3; Ground Measurements;330
7.9.2.3.1; Surrogates for Paddock Biomass;330
7.9.2.3.2; Timing Ground-Measurements;330
7.9.2.3.3; Paddock Selection;331
7.9.2.3.4; Laying Out the Sample Grid Within the Paddock;331
7.9.2.3.5; Making Pasture Cuts;331
7.9.2.4; Data Analysis;331
7.9.2.4.1; Paddock Masks;331
7.9.2.4.2; Within-Paddock and within-Grid Statistics;332
7.9.3; Results;332
7.9.4; Discussion;334
7.9.4.1; The Impact of Time Since Grazing on Remotely-Sensed Observations;334
7.9.4.1.1; Spectral Response Soon After Grazing;334
7.9.4.1.2; Spectral Response Across the Optimum Grazing Range;335
7.9.4.2; Ground Sampling Logistics;335
7.9.4.3; Implications of These Results;336
7.9.5; Conclusions;336
7.9.6;References;337
7.10;Using Pasture Growth Rate Data in a National Agricultural Drought Assessment Monitoring Tool;339
7.10.1; Introduction;340
7.10.2; The Nams Project;340
7.10.2.1; Project Objectives;341
7.10.2.2; What Is the NAMS?;341
7.10.2.3; Reports;341
7.10.3; Method of Data Acquisition, Processing and Analysis of PGR Data for use in NAMS;342
7.10.3.1; Acquisition;342
7.10.3.2; Processed Outputs;342
7.10.4; Discussion;344
7.10.5; Conclusions;344
7.10.6;References;346
7.11;Investigating the Potential for Mapping Fallow Management Practises Using MODIS Image Data;347
7.11.1; Introduction;347
7.11.2; Overview of Study Area;348
7.11.3; Data;348
7.11.3.1; Field Records;348
7.11.3.2; Image Data;350
7.11.3.2.1; MODIS Bands and Indices;350
7.11.4; Methodology;350
7.11.4.1; Formatting Field Observations;351
7.11.4.2; Image Analysis;351
7.11.4.2.1; Extraction and Classification of Spectral Reflectances;351
7.11.4.3; Statistical Analysis;352
7.11.4.3.1; Univariate Analysis of Band and Index Variables;352
7.11.4.3.2; Multivariate Logistic Regression Employing Band and Index Variables;352
7.11.4.3.3; Spraying Events and Non-spraying Events;353
7.11.4.4; Predicting Non-cultivation or Cultivation During Fallow Times;353
7.11.5; Results;353
7.11.5.1; Cultivated vs Non-cultivated Fallows;354
7.11.5.1.1; Eight-Day 500 m Data;354
7.11.5.1.2; Sixteen-Day 250 m Data;355
7.11.5.2; Cultivated and Non-cultivated Periods;357
7.11.5.2.1; Eight-Day 500 m Data;357
7.11.5.2.2; Sixteen-Day 250 m Data;358
7.11.5.2.3; Comparing 16-Day 250 m and 8-day 500 m Data Using Same Variables;359
7.11.5.3; Spraying vs. Non-spraying Events;359
7.11.5.4; Predicting Non-cultivation or Cultivation During Fallow Times;360
7.11.6; Discussion and Conclusion;361
7.11.7;References;363
7.12;Spectral Mixture Analysis for Ground-Cover Mapping;365
7.12.1; Introduction;365
7.12.2; Data;366
7.12.2.1; Satellite Images;367
7.12.2.2; Field Data;367
7.12.3; Methods;367
7.12.4; Results;370
7.12.5; Discussion and Conclusion;373
7.12.6;References;374
7.13;Vineclipper: A Proximal Search Algorithm to Tie Gps Field Locations to High Resolution Grapevine Imagery;376
7.13.1; Introduction;377
7.13.2; Methods;379
7.13.2.1; Spatial Data Collection;379
7.13.2.2; Steps of the VineClipper Algorithm;379
7.13.2.2.1; Calculate Centre Line of Row Closest to GPS Point (Line AA);382
7.13.2.2.2; Closest Point on the Centre Line of a Vine Row to a Given Coordinate (Vine Centre Point) is Located;383
7.13.2.2.3; The Pixels to be Used Around the Vine Centre (Sample Vine Pixels) are Identified;383
7.13.2.3; VineClipper Output Data;385
7.13.3; Conclusions;385
7.13.4;References;386
7.14;Modelling Weed Distribution Across the Northern Australia Using Very Extensive Transects;388
7.14.1; Introduction;388
7.14.2; Methods;389
7.14.2.1; Study Area;389
7.14.2.2; Transect Sampling;390
7.14.2.3; Predictor Variable Selection;391
7.14.2.4; Univariate Analysis of Spatial Data;393
7.14.2.5; Multivariate Analysis of Spatial Data;394
7.14.2.6; Generalised Additive Modelling Trials using GRASP;394
7.14.3; Results;394
7.14.3.1; Models;396
7.14.3.2; Testing the Model with Independent Data;397
7.14.3.3; Comparisons with Land Tenure;403
7.14.3.4; Comparisons with Biogeographic Subregions;403
7.14.4; Discussion;404
7.14.5;References;406
7.15;Automated Reconstruction of Buildings Using a Hand Held Video Camera;408
7.15.1; Introduction;408
7.15.2; Methodology;409
7.15.2.1; Image Sequence;409
7.15.2.2; Blur Metric;410
7.15.2.3; Registration of Key Frames;412
7.15.2.4; Feature Registration;414
7.15.3; Discussion;417
7.15.4; Conclusions and Future Work;418
7.15.5;References;418
8;Part IV Marine Applications of Remote Sensing;420
8.1;Mapping Seagrass Biomass with Photo-Library Method;421
8.1.1; Introduction;421
8.1.2; Method;422
8.1.2.1; Study Area;422
8.1.2.2; Photo-Transect Method;423
8.1.2.3; Biomass Samples;423
8.1.2.4; Satellite Imagery;424
8.1.2.5; Photo-Library Method;424
8.1.3; Results and Discussion;424
8.1.4; Conclusions;427
8.1.5;References;428
8.2;A Comparison of Bathymetric Signatures Observed on ERS SAR and LANDSAT TM Images Over the Timor Sea;430
8.2.1; Introduction;430
8.2.2; Methods;431
8.2.2.1; Study Area;431
8.2.2.2; Satellite and Ancillary Data;432
8.2.2.2.1; Satellite Data;432
8.2.2.2.2; Wind Fields;432
8.2.2.2.3; Bathymetry;433
8.2.2.2.4; Tides and Currents;433
8.2.3; Observations;433
8.2.3.1; ERS SAR and Landsat TM;433
8.2.3.2; Archived Landsat Images;439
8.2.4; Discussion;441
8.2.5; Conclusions;442
8.2.6;References;443
8.3;Optical Properties of Water Bodies in the Torres Strait, Australia, from Above-Water Reflectance;445
8.3.1; Introduction;445
8.3.2; Method;447
8.3.2.1; In Water Backscattering;447
8.3.2.2; Absorption, Suspended Matter and Chlorophyll;448
8.3.2.3; Above Water Surface Reflectance;448
8.3.2.4; Reflectance Model;449
8.3.3; Results;450
8.3.4; Conclusion;452
8.3.5;References;452
8.4;Accordance of MERIS Standard Products over the Gulf of Finland to the Parameters Measured Under Regular MonitoringProgram;454
8.4.1; Introduction;454
8.4.2; Material and Methods;455
8.4.2.1;Study Area and Chlorophyll a in Situ Data;456
8.4.2.2; MERIS Chlorophyll Retrieval Algorithms and Image Analyses;457
8.4.3; Results and Discussion;458
8.4.4; Conclusions;463
8.4.5;References;463
8.5;Spectral Identification of Oil Slicks on the Ocean;465
8.5.1; Introduction;465
8.5.2; Materials and Methods;466
8.5.2.1; Data Description;466
8.5.2.2; Oil Slick Appearance and BRDF;466
8.5.2.3; Detection Limits of SWIR Diagnostic Hydrocarbon Bands;467
8.5.3; Spectral Signature of an Oil Slick;469
8.5.3.1; General Characteristics of Production Waters Slick;469
8.5.3.2; Spectral Analysis;471
8.5.3.2.1; Methodology;471
8.5.3.2.2; Evolution of the Radiance Ratio Along the Slick;474
8.5.3.2.3; Possible Origin of Spectral Expression of an Oil Slick;475
8.5.4; Results;475
8.5.5; Conclusions;476
8.5.6;References;477