The goal of this article is to revise several aspects of the well-known classification of landslides, developed by Varnes (1978). The primary recommendation is to modify the definition of landslide-forming materials, to provide compatibility with accepted geotechnical and geological terminology of rocks and soils. Other, less important modifications of the classification system are suggested, resulting from recent developments of the landslide science. The modified Varnes classification of landslides has 32 landslide types, each of which is backed by a formal definition. The definitions should facilitate backward compatibility of the system as well as possible translation to other languages. Complex landslides are not included as a separate category type, but composite types can be constructed by the user of the classification by combining two or more type names, if advantageous.

The Varnes classification of landslide types, an update

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

Probabilistic landslide hazard assessment at the basin scale

The aim of this study is to evaluate the landslide hazards at Selangor area, Malaysia, using Geographic Information System (GIS) and Remote Sensing. Landslide locations of the study area were identified from aerial photograph interpretation and field survey. Topographical maps, geological data, and satellite images were collected, processed, and constructed into a spatial database in a GIS platform. The factors chosen that influence landslide occurrence were: slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, land cover, vegetation index, and precipitation distribution. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors by frequency ratio and logistic regression models. The results of the analysis were verified using the landslide location data and compared with probability model. The comparison results showed that the frequency ratio model (accuracy is 93.04%) is better in prediction than logistic regression (accuracy is 90.34%) model.

C.F. Lee

Papers

Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong

Replay of the 1987 Val Pola Landslide, Italian Alps