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Advances in Earth Observation of Global Change

E-BookPDF1 - PDF WatermarkE-Book
283 Seiten
Englisch
Springer Netherlandserschienen am04.08.20102010
Global Change studies are increasingly being considered a vital source of information to understand the Earth Environment, in particular in the framework of human-induced climate change and land use transformation. Satellite Earth Observing systems provide a unique tool to monitor those changes. While the range of applications and innovative techniques is constantly increasing, this book provides a summary of key case studies where satellite data offer critical information to understand the causes and effects of those environmental changes, minimizing their negative impacts. This book will be of interest to researchers and practitioners in the field of remote sensing, geographical information, meteorology and environmental sciences. Also scientists and graduate up to post-graduate level students in environmental science will find valuable information in this book.mehr
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Produkt

KlappentextGlobal Change studies are increasingly being considered a vital source of information to understand the Earth Environment, in particular in the framework of human-induced climate change and land use transformation. Satellite Earth Observing systems provide a unique tool to monitor those changes. While the range of applications and innovative techniques is constantly increasing, this book provides a summary of key case studies where satellite data offer critical information to understand the causes and effects of those environmental changes, minimizing their negative impacts. This book will be of interest to researchers and practitioners in the field of remote sensing, geographical information, meteorology and environmental sciences. Also scientists and graduate up to post-graduate level students in environmental science will find valuable information in this book.
Details
Weitere ISBN/GTIN9789048190850
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2010
Erscheinungsdatum04.08.2010
Auflage2010
Seiten283 Seiten
SpracheEnglisch
IllustrationenXIII, 283 p.
Artikel-Nr.1573999
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Preface;5
2;Contents;7
3;Contributors;9
4;1 The Role of Small Satellite Missions in Global Change Studies;14
4.1;1.1 Introduction;15
4.2;1.2 Small Satellite Missions: Facts and Trends;16
4.2.1;1.2.1 General Facts;17
4.2.2;1.2.2 Trends;18
4.3;1.3 Resolution Requirements for Space Borne Remote Sensing;18
4.4;1.4 Resolution Capabilities of Small Satellite Systems;21
4.4.1;1.4.1 Spatial Resolution;21
4.4.2;1.4.2 Spectral Resolution;21
4.4.3;1.4.3 Temporal Resolution;22
4.5;1.5 Status of Small Satellites;22
4.6;1.6 Constellations;23
4.6.1;1.6.1 Disaster Monitoring Constellation DMC-1;25
4.6.1.1;DEIMOS-1;25
4.6.2;1.6.2 RapidEye;26
4.7;1.7 Conclusion;27
4.8;References;28
5;2 Spatial Pattern Analysis of Water-Driven Land Cover Change in Aridzone, Northwest of China;29
5.1;2.1 Introduction;30
5.2;2.2 Methodology;31
5.2.1;2.2.1 Study Area and Data;31
5.2.2;2.2.2 Image Classification;32
5.2.3;2.2.3 Establishment of Land Cover Change Trajectories;32
5.2.4;2.2.4 Spatial Pattern Analysis;32
5.3;2.3 Results and Analysis;33
5.3.1;2.3.1 Classification and Area Statistics;33
5.3.2;2.3.2 Trajectories of Farmland Change;34
5.3.3;2.3.3 Impacts of Water Supply on Farmland Changes;35
5.4;2.4 Discussion;36
5.5;2.5 Conclusions;36
5.6;References;37
6;3 A Spatial Logistic Regression Model for Simulating Land Use Patterns: A Case Study of the Shiraz Metropolitan Area of Iran;39
6.1;3.1 Introduction;39
6.2;3.2 Methodology;41
6.2.1;3.2.1 Data Preparation;42
6.2.2;3.2.2 Simulation Process;42
6.2.3;3.2.3 Model Evaluation;43
6.2.4;3.2.4 Prediction;43
6.3;3.3 Parameterization of SLR for Shiraz Metropolitan Area;44
6.3.1;3.3.1 Study Area and Data Sources;44
6.3.2;3.3.2 Implementing the SLR;44
6.3.2.1;3.3.2.1 Absorbing Excursion Spaces;46
6.3.2.2;3.3.2.2 Transportation;46
6.3.2.3;3.3.2.3 Landscape Feature;46
6.4;3.4 Results and Discussions;46
6.4.1;3.4.1 Model Coefficients;48
6.4.2;3.4.2 Model Evaluation;49
6.4.3;3.4.3 Forecasting;50
6.5;3.5 Conclusions;52
6.6;References;53
7;4 Abu Dhabi Island: Analysis of Development and Vegetation Change Using Remote Sensing (19722000);55
7.1;4.1 Introduction;56
7.2;4.2 Data;56
7.3;4.3 Methodology;57
7.3.1;4.3.1 Feature Identification and Selection;57
7.3.2;4.3.2 Classification;58
7.4;4.4 Results;59
7.5;4.5 Discussion;63
7.6;4.6 Conclusions;64
7.7;References;64
8;5 Remote Sensing of Coastal Water Quality in the Baltic Sea Using MERIS;66
8.1;5.1 Introduction;66
8.2;5.2 Characteristics of Optically Complex Waters;67
8.3;5.3 Physics of the Problem;68
8.4;5.4 Mathematical View;69
8.5;5.5 Atmospheric Correction;70
8.6;5.6 Principal Component Inversion;72
8.7;5.7 Monitoring of Water Quality;75
8.8;5.8 Conclusions;77
8.9;References;77
9;6 Remote Sensing for Water Quality Monitoring in Apalachicola Bay, USA;79
9.1;6.1 Introduction;79
9.2;6.2 Water Quality Indicators for Coastal and Estuarine Ecosystems;81
9.3;6.3 Remote Sensing for Estuarine Water Quality Monitoring;82
9.3.1;6.3.1 TSS Monitoring;82
9.3.2;6.3.2 Chlorophyll-a Monitoring;83
9.4;6.4 Remote Sensing of Water Quality in Apalachicola Bay;84
9.4.1;6.4.1 Estimating TSS Concentrations;84
9.4.2;6.4.2 Estimating Chlorophyll-a Concentrations;85
9.5;6.5 Summary;87
9.6;References;87
10;7 Extracting Cryospheric Information over Lowlands from L-Band Polarimetric SAR Data;89
10.1;7.1 Introduction;89
10.2;7.2 Polarimetric Studies over Snow-Covered Agricultural Fields;90
10.2.1;7.2.1 Data;90
10.2.2;7.2.2 Qualitative Polarimetric Analysis;92
10.3;7.3 State of the Cryosphere by Means of Statistical Learning Method;94
10.3.1;7.3.1 SVM Background;94
10.3.2;7.3.2 Methodology;96
10.3.3;7.3.3 Results;97
10.4;7.4 Quantitative Assessments;98
10.4.1;7.4.1 Bare Soil Characterization;99
10.4.2;7.4.2 Electromagnetic Backscattering Modeling for Snow-Covered Frozen Ground;100
10.4.3;7.4.3 Toward the Estimation of Residual Liquid Water Content in Frozen Ground;102
10.5;7.5 Conclusions;103
10.6;References;104
11;8 Variability of Northern Hemisphere Spring Snowmelt Dates Using the AVHRR Polar Pathfinder Snow Cover During 19822004;106
11.1;8.1 Introduction;106
11.2;8.2 Data and Methods;108
11.3;8.3 Results;109
11.3.1;8.3.1 Interannual Variability in Smtd;109
11.3.2;8.3.2 Atmospheric Circulations Drive Large-Scale Interannual Variability in Smtd;112
11.3.3;8.3.3 Snow Temperature Sensitivity Regions (TSRs);115
11.4;8.4 Summary and Discussions;116
11.5;References;118
12;9 MODIS Snow Monitoring Over the Tibetan Plateau;120
12.1;9.1 Introduction;121
12.2;9.2 Data;122
12.2.1;9.2.1 MODIS Snow Mapping;122
12.2.2;9.2.2 GTOPO30 DEM Data;124
12.3;9.3 Seasonal Variations of Snowpack Over the Tibetan Plateau;124
12.3.1;9.3.1 Snow Distribution and Annual Cycle;124
12.3.2;9.3.2 Terrain Characteristics of Snow Cover Distribution;127
12.3.3;9.3.3 Snow Ablation;130
12.3.4;9.3.4 Interannual Variability and Linear Trend;131
12.4;9.4 Discussion and Conclusion;132
12.5;References;133
13;10 The Global Geodetic Observing System (GGOS):Detecting the Fingerprints of Global Changein Geodetic Quantities;134
13.1;10.1 Introduction;135
13.2;10.2 Geodesys Contribution to Earth Observation;137
13.2.1;10.2.1 The Global Geodetic Reference Frames;138
13.2.2;10.2.2 Role of Geodetic Observations for Science;138
13.3;10.3 GGOS: A Multi-technique, Multi-layered Yet Integrated System;139
13.3.1;10.3.1 A Value-Chain from Observations to Applications;139
13.3.2;10.3.2 A System-of-Systems;140
13.3.3;10.3.3 A Multi-layered System;142
13.3.4;10.3.4 Integration Through Multiple Links;142
13.3.5;10.3.5 An Integrated System Sensing Atmosphere, Hydrosphere, and Solid Earth;143
13.4;10.4 Global Change Results;144
13.5;10.5 Future Developments;150
13.6;References;151
14;11 Monitoring Radial Tectonic Motions of Continental Borders Around the Atlantic Ocean and Regional Sea Level Changes by Space Geodetic Observations;153
14.1;11.1 Introduction;153
14.2;11.2 Methodology;155
14.3;11.3 Results;157
14.4;11.4 Sea Level Rise;159
14.5;11.5 Conclusion and Discussion;161
14.6;References;163
15;12 GNSS Activities for Natural Disaster Monitoring and Climate Change Detection at GFZ An Overview;166
15.1;12.1 Introduction;166
15.2;12.2 GNSS Sensor Station Developments;167
15.3;12.3 GNSS-Based Component for Tsunami Early Warning Systems;169
15.4;12.4 GNSS Reflectometry;170
15.5;12.5 GNSS Seismology;172
15.6;12.6 GNSS Atmospheric Sounding;174
15.6.1;12.6.1 Ground-Based GNSS Meteorology;174
15.6.2;12.6.2 Spaced-Based Atmosphere Sounding;176
15.7;12.7 Summary;177
15.8;References;177
16;13 Satellite Imagery for Landslide Mapping in an Earthquake-Struck Area;179
16.1;13.1 Introduction;179
16.2;13.2 Study Area;181
16.3;13.3 Research Methodology;183
16.3.1;13.3.1 Data Acquisition and Collection;184
16.3.2;13.3.2 Image Preprocessing;185
16.3.3;13.3.3 Image Transformation;186
16.3.4;13.3.4 Change Detection;187
16.3.5;13.3.5 Thematic Accuracy Assessment;188
16.4;13.4 Results;189
16.5;13.5 Conclusions;189
16.6;References;190
17;14 Relations Between Human Factors and Global Fire Activity;193
17.1;14.1 Introduction;193
17.2;14.2 Methods;194
17.2.1;14.2.1 Active Fire Database;194
17.2.2;14.2.2 Explanatory Variables: Generation of GIS Database;195
17.2.3;14.2.3 Statistical Analysis of Input Data;196
17.3;14.3 Results;197
17.3.1;14.3.1 Spatial Patterns of Fire Indices;197
17.3.2;14.3.2 Relations with AFD;198
17.3.3;14.3.3 Relations with SDAFD;199
17.3.4;14.3.4 Relations with LFP;202
17.4;14.4 Discussion;202
17.5;References;204
18;15 The Use of Remote Sensing Data and Meteorological Information for Food Security Monitoring, Examples in East Africa;206
18.1;15.1 Introduction;206
18.2;15.2 Region of Interest;207
18.3;15.3 Meteorological and Remote Sensing Data;207
18.3.1;15.3.1 Rainfall;207
18.3.2;15.3.2 Vegetation Condition;208
18.4;15.4 The Models;209
18.5;15.5 The Bulletins;210
18.6;15.6 Some Important Parameters;215
18.6.1;15.6.1 Agriculture -- Crop Mask;215
18.6.2;15.6.2 Crop Phenology;216
18.6.3;15.6.3 National Agriculture Statistics;216
18.7;15.7 Field Assessment;218
18.8;15.8 Data Dissemination;219
18.9;15.9 Research and Development;220
18.10;References;220
19;16 Application of an Early Warning System for Floods;222
19.1;16.1 Introduction;223
19.2;16.2 The Structure of the Project;224
19.2.1;16.2.1 Data;224
19.2.2;16.2.2 Pre-elaboration;225
19.3;16.3 Analysis of Historical Data;226
19.3.1;16.3.1 Hydrological Analysis;227
19.3.2;16.3.2 Automatic Calculation of Some Drainage Basin Parameters;229
19.4;16.4 Real Time System;232
19.4.1;16.4.1 Correction Factor Algorithm and Data Correction in Real Time;232
19.4.2;16.4.2 Detection of Critical Rainfalls in Real Time with Grid Computing System;237
19.4.3;16.4.3 Results and Conclusion;238
19.5;References;241
20;17 L-Band and C-Band Combined Interferometric Monitoring of the Wenchuan Earthquake;243
20.1;17.1 Introduction;243
20.2;17.2 Methodology;246
20.2.1;17.2.1 Two-Pass DInSAR Approach;246
20.2.2;17.2.2 Estimation of Horizontal and Vertical Components;248
20.3;17.3 DInSAR Results;249
20.3.1;17.3.1 PALSAR;249
20.3.2;17.3.2 ASAR;250
20.4;17.4 Combined Observation;252
20.5;17.5 Validation;252
20.6;17.6 Conclusion;255
20.7;References;256
21;18 Uncovering the SpaceTime Patterns of Change with the Use of Change Analyst Case Study of Hong Kong;258
21.1;18.1 Introduction;258
21.2;18.2 Land Use Change Modeling;259
21.3;18.3 Study Area;260
21.4;18.4 Methodology;261
21.4.1;18.4.1 Logistic Regression;261
21.4.2;18.4.2 Spatial Sampling;262
21.4.3;18.4.3 GIS-Based Predictor Variables;263
21.4.4;18.4.4 Data Compilation;263
21.5;18.5 Results and Discussion;265
21.5.1;18.5.1 Logistic Regression Results;265
21.5.2;18.5.2 Evaluation of the Model;267
21.5.3;18.5.3 Prediction Results;267
21.6;18.6 Discussion;269
21.7;18.7 Conclusion;269
21.8;References;270
22;19 Change Detection of Sea Ice Distribution in SAR Imagery Using Semi-variogram of Intrinsic Regionalization Model;272
22.1;19.1 Introduction;272
22.2;19.2 Intrinsic Model Based on Bigamma and Mosaic Random Functions;273
22.2.1;19.2.1 Intrinsic Model;273
22.2.2;19.2.2 Bigamma Random Function;274
22.2.3;19.2.3 Poisson Tessellation Based Mosaic Random Function;276
22.3;19.3 Parameter Estimation for the Mixture Model;277
22.3.1;19.3.1 Experimental Semi-variogram;277
22.3.2;19.3.2 Theory Semi-variogram;278
22.3.3;19.3.3 Parameter Estimation by Least-Squares Adjustment;278
22.4;19.4 Experimental Results;278
22.5;19.5 Conclusions;282
22.6;References;282
23;Index;284mehr

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