Terrigenous Mass Movements [electronic resource] : Detection, Modelling, Early Warning and Mitigation Using Geoinformation Technology /
Pradhan, Biswajeet.editor.
Buchroithner, Manfred.editor.
Terrestrial mass movements (i.e. cliff collapses, soil creeps, mudflows, landslides etc.) are severe forms of natural disasters mostly occurring in mountainous terrain, which is subjected to specific geological, geomorphological and climatological conditions, as well as to human activities. It is a challenging task to accurately define the position, type and activity of mass movements for the purpose of creating inventory records and potential vulnerability maps. Remote sensing techniques, in combination with Geographic Information System tools, allow state-of-the-art investigation of the degree of potential mass movements and modeling surface processes for hazard and risk mapping. Similarly, through statistical prediction models, future mass-movement-prone areas can be identified and damages can to a certain extent be minimized. Issues of scale and selection of morphological attributes for the scientific analysis of mass movements call for new developments in data modeling and spatio-temporal GIS analysis. The book is a product of a cooperation between the editors and several contributing authors, addressing current issues and recent developments in GI technology and mass movements research. Its fundamental treatment of this technology includes data modeling, topography, geology, geomorphology, remote sensing, artificial neural networks, binomial regression, fuzzy logic, spatial statistics and analysis, and scientific visualization. Both theoretical and practical issues are addressed.
Description based upon print version of record.
Terrigenous MassMovements; Preface; Contents; 1 An Automated Approach for Detection of Shallow Landslides from LiDAR Derived DEM Using Geomorphological Indicators in a Tropical Forest; Abstract; 1.1...Introduction; 1.2...Study Area; 1.3...Geomorphological Features and Landslides; 1.4...The Parameter: Slope Curvature; 1.5...Data Used; 1.6...LiDAR Processing Steps; 1.6.1 Potential Landslide Zone Classification; 1.6.2 Surface Roughness Index; 1.6.3 Vegetation Index; 1.6.4 Breaklines; 1.6.5 Landslide Probability Index; 1.6.6 Implementation; 1.7...Results and Discussion; 1.8...Conclusions and Perspective
AcknowledgmentsReferences; 2 Landslide Susceptibility Mapping Using a Spatial Multi Criteria Evaluation Model at Haraz Watershed, Iran; Abstract; 2.1...Introduction; 2.2...Study Area; 2.3...Methodology; 2.4...Construction of Spatial Database; 2.4.1 Landslide Inventory Map; 2.4.2 Slope; 2.4.3 Aspect; 2.4.4 Altitude; 2.4.5 Plan Curvature; 2.4.6 Lithology; 2.4.7 Land Use; 2.4.8 Distance from Rivers; 2.4.9 Distance from Roads; 2.4.10 Distance from Faults; 2.4.11 Topographic Wetness Index (TWI); 2.4.12 Stream Power Index (SPI); 2.4.13 Stream Transport Index (STI); 2.5...Landslide Susceptibility Mapping
2.6...Validation of the Landslide Susceptibility Map2.7...Concluding Remarks; References; 3 Soft Computing Modeling in Landslide Susceptibility Assessment; Abstract; 3.1...Introduction; 3.2...General Concepts; 3.2.1 Landslide Susceptibility; 3.2.2 Landslide Hazard; 3.2.3 Landslide Risk; 3.3...Fuzzy Modeling and Relations; 3.3.1 Basic Fuzzy Concepts; 3.3.2 Fuzzy Relations; 3.3.3 Fuzzy Inference Systems; 3.3.3.1 Mamdani Fuzzy Model; 3.3.3.2 Sugeno Fuzzy Model; 3.3.3.3 Tsukamoto Fuzzy Model; 3.3.4 Landslide Susceptibility Applications of Fuzzy Approach; 3.4...Artificial Neural Networks (ANN)
3.4.1 Understanding Biological Neural Networks and ANN Relation3.4.2 Understanding ANN principles; 3.4.3 Perceptron; 3.4.4 ANN Types and Terminology; 3.4.5 Back Propagation Neural Network (BPNN); 3.4.5.1 Feed Forward Processes of the BPNN; 3.4.5.2 Calculation of error; 3.4.5.3 Back Propagation Processes of the BPNN; 3.4.6 Constructing an ANN; 3.4.7 Use of Artificial Neural Networks in Susceptibility Assessments; 3.5...Further Recommendations; References; 4 Application and Verification of Fractal Approach to Landslide Susceptibility Mapping; Abstract; 4.1...Introduction; 4.2...Study Area
4.3...Description of Methodology4.4...Application and Validation of Fractal Statistics to Landslide Susceptibility Mapping; 4.4.1 Landslide Susceptibility Mapping; 4.4.2 Verification of the Landslide Susceptibility Maps; 4.5...Discussion; 4.6...Conclusions; Acknowledgments; References; 5 Preliminary Slope Mass Movement Susceptibility Mapping Using DEM and LiDAR DEM; Abstract; 5.1...Introduction; 5.2...Problem Identification/Conceptual Background; 5.3...Review of Literature; 5.4...Study Area/Experimental Site; 5.4.1 Vaud County; 5.4.2 Bagnes Valley; 5.5...Data and Tools; 5.5.1 Data; 5.5.2 Tools
5.5.2.1 HISTOFIT
Includes bibliographical references.
Terrestrial mass movements (i.e. cliff collapses, soil creeps, mudflows, landslides etc.) are severe forms of natural disasters mostly occurring in mountainous terrain, which is subjected to specific geological, geomorphological and climatological conditions, as well as to human activities. It is a challenging task to accurately define the position, type and activity of mass movements for the purpose of creating inventory records and potential vulnerability maps. Remote sensing techniques, in combination with Geographic Information System tools, allow state-of-the-art investigation of the degree of potential mass movements and modeling surface processes for hazard and risk mapping. Similarly, through statistical prediction models, future mass-movement-prone areas can be identified and damages can to a certain extent be minimized. Issues of scale and selection of morphological attributes for the scientific analysis of mass movements call for new developments in data modeling and spatio-temporal GIS analysis. The book is a product of a cooperation between the editors and several contributing authors, addressing current issues and recent developments in GI technology and mass movements research. Its fundamental treatment of this technology includes data modeling, topography, geology, geomorphology, remote sensing, artificial neural networks, binomial regression, fuzzy logic, spatial statistics and analysis, and scientific visualization. Both theoretical and practical issues are addressed.
Geographical information systems.
Geophysics.
Physical geography.
Remote sensing.
Geographical Information Systems/Cartography.
Geophysics/Geodesy.
Physical Geography.
Remote Sensing/Photogrammetry.