CONTENTS Acknowledgements i List of figures vi List of tables viii List of acronyms ix 1 Introduction 1 1.1 Problem statement 1 1.2 Remote sensing for landslide inventory mapping .2 1.3 Research objectives 4 1.4 Test area 5 1.5 Structure of the thesis 8 2 Accuracy Assessment of DSMs 9 2.1 Introduction 9 2.1.1 Automatic DSM generation 12 2.2 Study area 13 2.3 Methods 14 2.3.1 DGPS survey 14 2.3.2 Processing of Cartosat-1 data 15 2.3.3 Accuracy assessment 16 2.3.4 Effect of valley orientation 19 2.4 Results 21 2.4.1 DSM Global accuracy 21 2.4.2 Effect of valley orientation on DSM accuracy 22 2.4.3 Effect of shadow on DSM generation 22 2.5 Discussion 23 2.6 Conclusion 27 Acknowledgements 28 3 Volumetric analysis of landslides 29 3.1 Introduction 29 3.1.1 Landslide volume estimation 30 3.2 Area and data analysis 32 3.2.1 Test area 32 3.2.2 DSM generation 33 3.2.3 Volumetric analysis 36 3.3 Results and discussion 36 3.3.1 Accuracy assessment of volume 39 3.4 Conclusion 40 Acknowledgements 41 4 Characterisation and detection of landslides 43 4.1 Introduction 43 4.2 Landslide characterisation from satellite data and DEM 46 4.3 Materials and methods 48 4.3.1 Study area 48 4.3.2 Data sources 49 4.3.2.1 Satellite data 49 4.3.2.2 DEM 49 4.3.3 Segmentation technique 50 4.3.4 Approach for landslide recognition and classification 51 4.3.4.1 Identification of landslide candidates (Step-1) 51 4.3.4.2 Separation of landslides from false positives (Step-2) 51 4.3.4.3 Identification of landslide types (Step-3) .53 4.4 Results 54 4.4.1 Extraction of landslide candidate objects 54 4.4.2 Landslide recognition 55 4.4.3 Landslide classification 59 4.4.4 Accuracy assessment 61 4.5 Discussion 63 4.6Conclusion 66 5 Segment optimisation and data-driven thresholding 69 5.1 Introduction 69 5.2 Segmentation and thresholding methods 71 5.2.1 Segmentation methods 71 5.2.2 Thresholding methods 73 5.3 Data set, area and methodology 73 5.3.1 Knowledge-based detection of landslides 74 5.3.2 Optimisation of segments (Sub-module 1) 75 5.3.2.1 Optimisation strategy with objective function 77 5.3.3 Extraction of landslide candidates (Sub-module 2) 78 5.3.3.1 Thresholding by K-means 78 5.3.4 Identification of false positives (Sub-module 3) 79 5.3.4.1 Linking optimal scales 79 5.3.5 Classification of landslides (Sub-module 4) 79 5.3.6 Transferability of the method 80 5.4 Results 80 5.4.1 Training area (Okhimath) result 84 5.4.2 Testing area (Darjeeling) result 88 5.5 Accuracy assessment 89 5.6 Conclusion 94 Acknowledgement 95 6 Historical landslide inventories from panchromatic images 97 6.1 Introduction 97 6.2 Materials and method 99 6.2.1 Data sources 99 6.2.2 Pre-processing of satellite data 100 6.2.2.1 Image geometric correction 100 6.2.2.2 TOA reflectance calculation 100 6.2.2.3 Image Normalisation 101 6.2.3 Detection approach 102 6.2.3.1 Identification of landslide candidates 102 6.2.3.2 Identification landslide false positives 104 6.2.3.3 Classification of landslide types 104 6.2.3.4 Detection from time-series images 104 6.3 Results and discussion 106 6.3.1 Landslide candidates 106 6.3.2 Detection of landslides 107 6.3.4 Accuracy assessment 111 6.3.4.1 Comparing landslide density 115 6.4 Conclusion 117 Acknowledgements 118 7 Use of semi-automatically derived landslide inventories 119 7.1 Introduction 119 7.2 Methodology and data 120 7.2.1 Preparation of multi-temporal landslide inventory 121 7.2.2 Generation of landslide susceptibility map 123 7.2.2.1 Input data 123 7.2.2.2 Weights of evidence (wofe) method 125 7.2.3 Landslide hazard assessment 127 7.2.3.1 Estimation of spatial probability 127 7.2.3.2 Estimation of temporal probability 127 7.2.4 Landslide risk assessment 128 7.3 Results and discussion 130 7.3.1 Landslide susceptibility assessment 130 7.3.2 Landslide hazard assessment 135 7.3.3 Risk assessment 135 7.4 Conclusion 137 Acknowledgements 138 8 Synthesis 139 8.1 Introduction 139 8.2 The role of DEM for detection of landslides 140 8.3 Knowledge-based object-oriented method 141 8.3.1 Detection of landslides using multispectral images 141 8.3.2 Detection of landslides using panchromatic images 143 8.4 Use of the semi-automated landslide detection technique 144 8.4.1 Rapid event-based mapping 144 8.4.2 Hazard and risk assessment 145 8.5 Future work 146 8.5.1 Towards an operational method in India 147 Bibliography 149 Summary 161 Samenvatting 165 Biography 169 Publications 170 ITC Dissertation List 171