Lecture Notes in Geoinformation and Cartography, 2013
Geo-information for Disaster management (Gi4DM) is an annual conference devoted to the use and ap... more Geo-information for Disaster management (Gi4DM) is an annual conference devoted to the use and application of geo-information technology in disaster management. The fundamental goal of the conference is to provide a forum to join science, technology and practice towards better support of risk and disaster management. Seven editions of this series have taken place in different parts of the world (www.gi4dm.net).
Abstract Landslide susceptibility mapping (LSM) along road corridors in the Indian Himalayas is a... more Abstract Landslide susceptibility mapping (LSM) along road corridors in the Indian Himalayas is an essential exercise that helps planners and decision makers in determining the severity of probable slope failure areas. Logistic regression is commonly applied for this purpose, as it is a robust and straightforward technique that is relatively easy to handle. Ordinary logistic regression as a data-driven technique, however, does not allow inclusion of prior information. This study presents Bayesian logistic regression (BLR) for landslide ...
In contrast to the many studies that use expert-based analysis of LiDAR derivatives for landslide... more In contrast to the many studies that use expert-based analysis of LiDAR derivatives for landslide mapping in forested terrain, only few studies have attempted to develop (semi-)automatic methods for extracting landslides from LiDAR derivatives. While all these studies are pixel-based, it has not yet been tested whether objectoriented analysis (OOA) could be an alternative. This study investigates the potential of OOA using only singlepulse LiDAR derivatives, such as slope gradient, roughness and curvature to map landslides. More specifically, the focus is on both LiDAR data segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not allow accurate landslide identification. A multistage procedure has been developed and tested in the Flemish Ardennes (Belgium). The procedure consists of (1) image binarization and multiresolution segmentation, (2) classification of landslide parts (main scarps and landslide body segments) and non-landslide features (i.e. earth banks and cropland fields) with supervised support vector machines at the appropriate scale, (3) delineation of landslide flanks, (4) growing of a landslide body starting from its main scarp, and (5) final cleaning of the inventory map. The results obtained show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of forested deep-seated landslides on soil-covered hillslopes, because more than 90% of the main scarps and 70% of the landslide bodies of an expert-based inventory were accurately identified with OOA. For mountainous areas with bedrock, on the other hand, creation of a transferable model is expected to be more difficult.
This article introduces a strategic initiative, COST Action TD1202, focused on the role of citize... more This article introduces a strategic initiative, COST Action TD1202, focused on the role of citizen sensors in mapping. It outlines the Action’s scope, aims and current status. In particular, the article outlines the potential of citizen science in mapping activities and indicates the scope of current work undertaken by the Action’s four working groups. It is stressed that the Action is at an early stage and that it is open to new members.
Lecture Notes in Geoinformation and Cartography, 2013
Geo-information for Disaster management (Gi4DM) is an annual conference devoted to the use and ap... more Geo-information for Disaster management (Gi4DM) is an annual conference devoted to the use and application of geo-information technology in disaster management. The fundamental goal of the conference is to provide a forum to join science, technology and practice towards better support of risk and disaster management. Seven editions of this series have taken place in different parts of the world (www.gi4dm.net).
Abstract Landslide susceptibility mapping (LSM) along road corridors in the Indian Himalayas is a... more Abstract Landslide susceptibility mapping (LSM) along road corridors in the Indian Himalayas is an essential exercise that helps planners and decision makers in determining the severity of probable slope failure areas. Logistic regression is commonly applied for this purpose, as it is a robust and straightforward technique that is relatively easy to handle. Ordinary logistic regression as a data-driven technique, however, does not allow inclusion of prior information. This study presents Bayesian logistic regression (BLR) for landslide ...
In contrast to the many studies that use expert-based analysis of LiDAR derivatives for landslide... more In contrast to the many studies that use expert-based analysis of LiDAR derivatives for landslide mapping in forested terrain, only few studies have attempted to develop (semi-)automatic methods for extracting landslides from LiDAR derivatives. While all these studies are pixel-based, it has not yet been tested whether objectoriented analysis (OOA) could be an alternative. This study investigates the potential of OOA using only singlepulse LiDAR derivatives, such as slope gradient, roughness and curvature to map landslides. More specifically, the focus is on both LiDAR data segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not allow accurate landslide identification. A multistage procedure has been developed and tested in the Flemish Ardennes (Belgium). The procedure consists of (1) image binarization and multiresolution segmentation, (2) classification of landslide parts (main scarps and landslide body segments) and non-landslide features (i.e. earth banks and cropland fields) with supervised support vector machines at the appropriate scale, (3) delineation of landslide flanks, (4) growing of a landslide body starting from its main scarp, and (5) final cleaning of the inventory map. The results obtained show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of forested deep-seated landslides on soil-covered hillslopes, because more than 90% of the main scarps and 70% of the landslide bodies of an expert-based inventory were accurately identified with OOA. For mountainous areas with bedrock, on the other hand, creation of a transferable model is expected to be more difficult.
This article introduces a strategic initiative, COST Action TD1202, focused on the role of citize... more This article introduces a strategic initiative, COST Action TD1202, focused on the role of citizen sensors in mapping. It outlines the Action’s scope, aims and current status. In particular, the article outlines the potential of citizen science in mapping activities and indicates the scope of current work undertaken by the Action’s four working groups. It is stressed that the Action is at an early stage and that it is open to new members.
Uploads
Papers by Norm Kerle