Laser scanning applications in landslide assessment

• Other Authors: Pradhan, Biswajeet (Editor)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer, 2017.
• Subjects: Landslide hazard analysis.; elektronické knihy; electronic books
• ISBN: 9783319553429; 9783319553412
• Physical Description: 1 online resource

This book is related to various applications of laser scanning in landslide assessment. Landslide detection approaches, susceptibility, hazard, vulnerability assessment and various modeling techniques are presented. Optimization of landslide conditioning parameters and use of heuristic, statistical,...

Table of Contents:

• Preface; Contents; Introduction; 1 Laser Scanning Systems in Landslide Studies; 1.1 Introduction; 1.1.1 Laser Scanning Techniques; 1.1.2 System Components; 1.1.3 Measurement Theory; 1.1.4 Accuracy and Resolution; 1.2 LiDAR Data Processing Methods; 1.2.1 LiDAR Digital Surface Model (DSM) Filtering; 1.2.2 Registration; 1.2.3 Geometric and Radiometric Calibrations; 1.3 Main LiDAR Data Products Used for Landslide Modeling; 1.3.1 Altitude; 1.3.2 Slope; 1.3.3 Aspect; 1.3.4 Curvature; 1.3.5 Hydrological Factors; 1.4 LiDAR in Landslide Applications; 1.4.1 Landslide Detection (Inventory Mapping).
• 1.4.2 Landslide Susceptibility Modeling1.4.3 Detection and Characterization of Landslides Movements; 1.4.4 Simulation of Debris Flow and Rockfall; 1.5 Discussion and Conclusion; References; Landslide Detection Techniques; 2 A Supervised Object-Based Detection of Landslides and Man-Made Slopes Using Airborne Laser Scanning Data; 2.1 Introduction; 2.2 Study Area and Data; 2.2.1 Location of Study Area; 2.2.2 LiDAR Data; 2.2.3 Geological Characteristics of the Study Area; 2.3 Methodology; 2.3.1 Data Pre-processing and Preparation of Landslide Factors; 2.3.2 Image Segmentation.
• 2.3.3 Classification2.3.3.1 Classifier; 2.3.3.2 Bayes; 2.3.3.3 k-NN; 2.3.3.4 SVM; 2.3.3.5 DT; Important DT Parameters; 2.3.3.6 RF; 2.3.3.7 Landslide and Cut Slope Detection; 2.3.3.8 Validation; Visual Interpretation; Transferring to Testing Subset; Field Validation; Accuracy Assessment; 2.4 Results; 2.4.1 Landslide Detection Results; 2.4.2 Results of Landslide Detection in the Training Site; 2.4.2.1 RF; 2.4.2.2 SVM; 2.4.3 Results of Landslide Detection in Testing Site 1; 2.4.3.1 RF; 2.4.3.2 SVM; 2.4.4 Results of Landslide Detection in Testing Site 2; 2.4.4.1 RF; 2.4.4.2 SVM.
• 2.4.5 Cut Slope and Landslide Detection Results2.4.6 Results of Image Segmentation; 2.4.7 Results of Accuracy Assessment; 2.5 Discussion; 2.6 Conclusion; References; 3 Optimized Rule Sets for Automatic Landslide Characteristic Detection in a Highly Vegetated Forests; 3.1 Introduction; 3.2 Types of Landslides; 3.2.1 Deep-Seated Landslide; 3.2.2 Shallow Landslide; 3.3 Study Area; 3.4 Material and Method; 3.4.1 LiDAR Data; 3.4.2 Object-Based Image Analysis (OBIA); 3.4.3 Image Segmentation; 3.4.4 Correlation-Based Feature Selection; 3.4.5 DT Classifiers; 3.4.6 Landslide Mapping; 3.5 Results.
• 3.5.1 Segmentation Parameters Selected Using a Fuzzy Logic Supervised Approach3.5.2 Attributes Selected Using the CFS Approach; 3.5.3 Rule Sets Developed for Landslide Detection and Characterization; 3.5.4 Model Transferability; 3.6 Discussion; 3.6.1 Accuracy Assessment; 3.6.2 Field Investigation; 3.7 Conclusion; References; 4 Integration of LiDAR and QuickBird Data for Automatic Landslide Detection Using Object-Based Analysis and Random Forests; 4.1 Introduction; 4.2 Study Area; 4.3 Data Used; 4.4 Methodology; 4.4.1 Data Preprocessing and Preparation; 4.4.2 Segmentation Using Taguchi Method.