A spatiotemporal probabilistic modelling of storm-induced shallow landsliding using aerial photographs and logistic regression

Abstract

Landslides constitute one of the major natural hazards that could cause significant losses of life and property. Mapping or delineating areas prone to landsliding is therefore essential for land-use activities and management decision making in hilly or mountainous regions. A landslide hazard map can be constructed by a qualitative combination of maps of site conditions, including geology, topography and geomorphology, by statistical methods through correlating landslide occurrence with geologic and geomorphic factors, or by using safety factors from stability analysis. A landslide hazard map should provide information on both the spatial and temporal probabilities of landsliding in a certain area. However, most previous studies have focused on susceptibility mapping, rather than on hazard mapping in a spatiotemporal context. This study aims at developing a predictive model, based on both quasi-static and dynamic variables, to determine the probability of landsliding in terms of space and time. The study area selected is about 13 km2 in North Lantau, Hong Kong. The source areas of the landslides caused by the rainstorms of 18 July 1992 and 4-5 November 1993 were interpreted from multi-temporal aerial photographs. Landslide data, lithology, digital elevation model data, land cover, and rainfall data were digitized into a geographic information system database. A logistic regression model was developed using lithology, slope gradient, slope aspect, elevation, slope shape, land cover, and rolling 24 h rainfall as independent variables, since the dependent variable could be expressed in a dichotomous way. This model achieved an overall accuracy of 87.2%, with 89.5% of landslide grid cells correctly classified and found to be performing satisfactorily. The model was then applied to rainfalls of a variety of periods of return, to predict the probability of landsliding on natural slopes in space and time. It is observed that the modelling techniques described here are useful for predicting the spatiotemporal probability of landsliding and can be used by land-use planners to develop effective management strategies. Copyright © 2003 John Wiley and Sons, Ltd.