Statistics for Spatial Data.

The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan

The quantification of risk has gained importance in many disciplines, including landslide studies. The literature on landslide risk assessment illustrates the developments which have taken place in the last decade and that quantitative risk assessment is feasible for geotechnical engineering on a site investigation scale and the evaluation of linear features (e.g., pipelines, roads). However, the generation of quantitative risk zonation maps for regulatory and development planning by local authorities still seems a step too far, especially at medium scales (1:10,000–1:50,000). This paper reviews the problem of attempting to quantify landslide risk over larger areas, discussing a number of difficulties related to the generation of landslide inventory maps including information on date, type and volume of the landslide, the determination of its spatial and temporal probability, the modelling of runout and the assessment of landslide vulnerability. An overview of recent developments in the different approaches to landslide hazard and risk zonation at medium scales is given. The paper concludes with a number of new advances and challenges for the future, such as the use of very detailed topographic data, the generation of event-based landslide inventory maps, the use of these maps in spatial-temporal probabilistic modelling and the use of land use and climatic change scenarios in deterministic modelling.

https://www.geo.uzh.ch/~chuggel/riskcourse/vanwesten_landsl_hazard_risk_zonation_bege06.pdf

Landslide hazard and risk zonation—why is it still so difficult?

Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview

A review of statistically-based landslide susceptibility models

The aim of this study is to evaluate the susceptibility of landslides at Yongin, Korea, using a geographic information system (GIS). Landslide locations were identified in the Yongin area from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, timber cover, and geology. These data were collected and constructed into a spatial database using GIS. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, age, diameter, and density of timber were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM satellite image. Landslide susceptibility was analyzed using the landslide occurrence factors by probability and logistic regression methods. The results of the analysis were verified using the landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy. The results can be used to reduce associated hazards, and to plan land use and construction.

Statistical analysis of landslide susceptibility at Yongin, Korea

The purpose of this study is to develop landslide susceptibility analysis techniques using an artiﬁcial neural network and to apply the newly developed techniques to the study area of Yongin in Korea. Landslide locations were identiﬁed in the study area from interpretation of aerial photographs, ﬁeld survey data, and a spatial database of the topography, soil type and timber cover. The landslide-related factors (slope, curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter) were extracted from the spatial database. Using those factors, landslide susceptibility was analysed by artiﬁcial neural network methods. The landslide susceptibility index was calculated by the back-propagation method, which is a type of artiﬁcial neural network method, and the susceptibility map was made with a geographic information system (GIS) program. The results of the landslide susceptibility analysis were veriﬁed using landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. A GIS was used to efﬁciently analyse the vast amount of data, and an artiﬁcial neural network to be an effective tool to maintain precision and accuracy. The results can be used to reduce hazards associated with landslides and to plan land use and construction. Copyright © 2003 John Wiley & Sons, Ltd.

Landslide susceptibility analysis using GIS and artificial neural network

The aim of this study is to evaluate the hazard of landslides at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys. The topographic, soil, forest, geologic, lineament and land cover data were collected, processed and constructed into a spatial database using GIS and remote sensing data. The factors that influence landslide occurrence, such as slope, aspect and curvature of the topography, were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, age, diameter and density of timber were extracted from the forest database. The lithology was extracted from the geological database and lineaments were detected from Indian Remote Sensing (IRS) satellite images. The land cover was classified based on the Landsat Thematic Mapper (TM) satellite image. Landslide hazard areas were anal...

Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea

The Bayesian probability model, using the weight-of-evidence method, was applied to the task of evaluating landslide susceptibility using GIS data. The location chosen for the study was the Janghung area in Korea, which suffered substantial landslide damage following heavy rain in 1998. Using the location of the landslides as well as topographic factors such as soil, forest and land use, the weight-of-evidence method was used to calculate each factor's rating. Tests of conditional independence were performed for the selection of the factors, allowing the large number of combinations of factors to be analyzed. For each factor rating, the contrast values, W+ and W–, were overlaid for landslide susceptibility mapping. The results of the analysis were verified using the observed landslide locations and, among the combinations, the slope, aspect, curvature, soil material and wood types show the best results.

Kyungduck Min

Papers

Statistical analysis of landslide susceptibility at Yongin, Korea

Landslide susceptibility analysis using GIS and artificial neural network

Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea

Landslide susceptibility analysis and verification using the Bayesian probability model

Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area, Korea