Srivastava / Mukherjee / Gupta Remote Sensing Applications in Environmental Research

1. Auflage 2014
ISBN: 978-3-319-05906-8
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 212 Seiten

Reihe: Society of Earth Scientists Series

Remote Sensing Applications in Environmental Research is the basis for advanced Earth Observation (EO) datasets used in environmental monitoring and research. Now that there are a number of satellites in orbit, EO has become imperative in today's sciences, weather and natural disaster prediction. This highly interdisciplinary reference work brings together diverse studies on remote sensing and GIS, from a theoretical background to its applications, represented through various case studies and the findings of new models. The book offers a comprehensive range of contributions by well-known scientists from around the world and opens a new window for students in presenting interdisciplinary and methodological resources on the latest research. It explores various key aspects and offers state-of-the-art research in a simplified form, describing remote sensing and GIS studies for those who are new to the field, as well as for established researchers.

Autoren/Hrsg.

Mukherjee, Saumitra

Gupta, Manika

Islam, Tanvir

Srivastava, Prashant K.

Inhaltsverzeichnis

1;Foreword;6
2;Contents;8
3;About the Editors;10
4;Introduction;13
5;Part IClassical Remote Sensing Applications;17
6;1 Remote Sensing-Based Determination of Conifer Needle Flushing Phenology over Boreal-Dominant Regions;18
6.1;Abstract;18
6.2;1…Introduction;19
6.3;2…Materials and Methods;20
6.3.1;2.1 Description of the Study Area and Data Requirements;20
6.3.2;2.2 Generation of AGDD Maps;22
6.3.3;2.3 Determination of AGDD and NDWI Thresholds for CNG Occurrence;22
6.3.4;2.4 Integration of both AGDD and NDWI Threshold for CNG Occurrence;23
6.3.5;2.5 Mapping of CNF Using the Best Prediction Criteria;24
6.4;3…Results and Discussion;24
6.4.1;3.1 Determination of AGDD Threshold for CNF Occurrence;24
6.4.2;3.2 Determination of NDWI Thresholds for CNF Occurrence;26
6.4.3;3.3 Integration of both AGDD and NDWI Thresholds;27
6.4.4;3.4 Spatial Dynamics of CNF Across the Landscape;28
6.5;4…Concluding Remarks;29
6.6;Acknowledgements;30
6.7;References;30
7;2 Information System for Integrated Watershed Management Using Remote Sensing and GIS;32
7.1;Abstract;32
7.2;1…Introduction ;33
7.2.1;1.1 Why Management of Natural Resources on Watershed Basis?;33
7.2.2;1.2 Role of Geographic Information System (GIS) and Remote Sensing (RS) in Watershed Management;34
7.2.3;1.3 Decision Support System in Watershed Management;35
7.2.4;1.4 Need for Advanced and Augmented Techniques for Watershed Management;36
7.3;2…Study Area ;38
7.4;3…Conceptual Design ;38
7.4.1;3.1 Data Used;39
7.4.2;3.2 Tools and Technologies Used;40
7.4.3;3.3 System Architecture;41
7.5;4…Online Generation and Implementation of WATMIS ;41
7.6;5…Conclusion ;45
7.7;References;46
8;3 Sensitivity Exploration of SimSphere Land Surface Model Towards Its Use for Operational Products Development from Earth Observation Data;50
8.1;Abstract;50
8.2;1…Introduction;51
8.3;2…Sensitivity Analysis: An Overview;52
8.4;3…Materials and Methods;55
8.4.1;3.1 SimSphere Model;55
8.4.2;3.2 The Bayesian GSA Method;56
8.4.3;3.3 BACCO Implementation;58
8.5;4…Results;60
8.5.1;4.1 Emulator Validation;60
8.5.2;4.2 SA Results;60
8.6;5…Discussion;62
8.7;6…Conclusions;68
8.8;Acknowledgments;68
8.9;References;68
9;4 Remote Estimation of Land Surface Temperature for Different LULC Features of a Moist Deciduous Tropical Forest Region;72
9.1;Abstract;72
9.2;1…Introduction;73
9.3;2…Materials and Methods;74
9.3.1;2.1 Study Area and Datasets;74
9.3.2;2.2 Image Interpretation for LULC;74
9.3.3;2.3 Surface Temperature Estimation;75
9.4;3…Results and Discussions;78
9.5;Acknowledgments;81
9.6;References;81
10;5 Geospatial Strategy for Estimation of Soil Organic Carbon in Tropical Wildlife Reserve;84
10.1;Abstract;84
10.2;1…Introduction;85
10.3;2…Materials and Methods;86
10.3.1;2.1 Study Area;86
10.3.2;2.2 Data Used;87
10.3.3;2.3 Image Interpretation;87
10.3.4;2.4 Estimation of Soil Organic Carbon;88
10.4;3…Results and Discussion;89
10.4.1;3.1 Land Use Land Cover Classification;89
10.4.2;3.2 Bare Soil Index;90
10.4.3;3.3 Soil Type Map;92
10.4.4;3.4 Soil Organic Carbon and Regression Analysis;94
10.5;4…Conclusion;95
10.6;Acknowledgment;96
10.7;References;96
11;Part IIAdvanced Remote Sensing Applications;99
12;6 A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithm and J48 Decision Tree Models in Spatial Prediction of Shallow Landslides at Lang Son City, Vietnam;100
12.1;Abstract;100
12.2;1…Introduction;101
12.3;2…Study Area and Spatial Database;102
12.3.1;2.1 Study Area Characteristics;102
12.3.2;2.2 Spatial Database;102
12.3.2.1;2.2.1 Landslide Inventory;102
12.3.2.2;2.2.2 Digital Elevation Model and Derivatives;104
12.3.3;2.3 Lithology and Distance to Faults;104
12.3.4;2.4 Land Use and Soil Type;104
12.4;3…Methodology;104
12.4.1;3.1 Training and Validation Dataset;104
12.4.2;3.2 Fuzzy Unordered Rules Induction Algorithm;107
12.4.3;3.3 Decision Tree;111
12.4.4;3.4 Bagging;112
12.4.5;3.5 Generation of Landslide Susceptibility Maps;114
12.5;4…Validation and Comparison of Landslide Susceptibility Models;115
12.5.1;4.1 Model Performance and Evaluation;115
12.5.2;4.2 Model Validation;118
12.5.3;4.3 Relative Contribution of the Conditioning Factors;119
12.6;5…Conclusion;120
12.7;Acknowledgement;122
12.8;References;122
13;7 Application of Geo-Spatial Technique for Flood Inundation Mapping of Low Lying Areas;125
13.1;Abstract;125
13.2;1…Introduction;126
13.3;2…Study Area and Datasets;127
13.3.1;2.1 Tapi Basin;127
13.3.2;2.2 Surat City;127
13.3.2.1;2.2.1 Geology and Soil Conditions;128
13.3.2.2;2.2.2 Ground Water Table;128
13.3.2.3;2.2.3 Climate;129
13.3.2.4;2.2.4 Temperature and Rainfall;129
13.3.2.5;2.2.5 Demography/Population in the Study Area;129
13.3.3;2.3 Hydraulic Structures at LTB;129
13.3.3.1;2.3.1 Ukai Dam;129
13.3.3.2;2.3.2 Kakrapar Weir;131
13.3.3.3;2.3.3 Singanpur Weir;132
13.3.4;2.4 Flood Event 2006;133
13.3.5;2.5 Data Collection;134
13.4;3…Methodology;134
13.5;4…Results and Discussion;136
13.6;5…Validation;140
13.7;6…Conclusion;141
13.8;Acknowledgments;141
13.9;References;141
14;8 Spatial Variations in Vegetation Fires and Carbon Monoxide Concentrations in South Asia;143
14.1;Abstract;143
14.2;1…Introduction;144
14.3;2…Data Sets and Methodology;146
14.3.1;2.1 Vegetation Fires;146
14.3.2;2.2 MOPITT CO Retrievals;146
14.3.3;2.3 Aerosol Optical Depth (AOD) and Aerosol Small Mode Fraction (SMAF);147
14.3.4;2.4 Spatial Gridding, Ordinary Linear Regression (OLR) and Locally Weighted regression;147
14.4;3…Results and Discussion;149
14.5;Acknowledgements;158
14.6;References;158
15;9 Land Use Fragmentation Analysis Using Remote Sensing and Fragstats;162
15.1;Abstract;162
15.2;1…Introduction;163
15.3;2…Study Area;164
15.4;3…Materials and Methods;164
15.4.1;3.1 Classification of Satellite Data;164
15.4.2;3.2 Fragmentation Analysis;166
15.5;4…Result and Discussion;167
15.5.1;4.1 Land Use and Land Cover Distribution;167
15.5.2;4.2 Landscape Level Metrics;169
15.5.3;4.3 Class Level Metrics;170
15.5.4;4.4 Class Level Metric Analysis;178
15.5.5;4.5 Land Fragmented Class Analysis;181
15.5.6;4.6 Estimating Effects on Water Quality;183
15.6;5…Conclusion;185
15.7;Acknowledgments;185
15.8;References;185
16;10 Chlorophyll Retrieval Using Ground Based Hyperspectral Data from a Tropical Area of India Using Regression Algorithms;188
16.1;Abstract;188
16.2;1…Introduction;189
16.3;2…Materials and Methods;190
16.3.1;2.1 Field Experiment and Canopy Spectral Measurements;190
16.3.2;2.2 Data Analysis;191
16.3.3;2.3 Chlorophyll Estimation;192
16.3.4;2.4 Statistical Analysis;193
16.3.4.1;2.4.1 Descriptive Statistics;193
16.3.4.2;2.4.2 Regression Analysis;194
16.3.4.3;2.4.3 Paired t Test;195
16.4;3…Results and Discussion;195
16.4.1;3.1 Descriptive Statistics of the Data;195
16.4.2;3.2 Continuum Removal and REIP Evaluation;195
16.4.3;3.3 Calculation of Vegetation Indices;198
16.4.4;3.4 Relationship of Chlorophyll with Vegetation Indices;198
16.4.5;3.5 Sensitivity Analysis of the Empirical Relationships Developed;202
16.5;4…Conclusion;203
16.6;Acknowledgment;203
16.7;References;203
17;11 Remote Sensing Based Identification of Painted Rock Shelter Sites: Appraisal Using Advanced Wide Field Sensor, Neural Network and Field Observations;206
17.1;Abstract;206
17.2;1…Introduction;206
17.3;2…Material and Methodology;208
17.3.1;2.1 Study Area;208
17.3.2;2.2 Satellite and GIS Datasets;209
17.3.3;2.3 Classifiers/Algorithms Implemented in this Study;210
17.3.3.1;2.3.1 Artificial Neural Network (ANN);210
17.3.3.2;2.3.2 Maximum Likelihood Classification (MLC);212
17.4;3…Accuracy Assessment of the Classified Images;213
17.5;4…Results and Discussion;214
17.5.1;4.1 Land Covers Distribution and Accuracy Assessment;214
17.5.2;4.2 Interpretation of the Results and Archaeological Relevance;216
17.6;5…Conclusion;220
17.7;Acknowledgment;221
17.8;References;221