Landslide hazard and risk assessment using semi-automatically created landslide inventories

Landslide inventories prepared manually from remote sensing data or through field surveys have shown to be useful for preparation of landslide susceptibility and hazard maps. Recent literatures show several studies have been carried out to prepare landslide inventories from satellite data by automatic methods. However, almost no attempt has been made to validate the effect of such inventories on landslide hazard and risk assessment. In this paper we have shown how landslide inventories prepared by semi-automatic methods from post-event satellite images can be used in the assessment of landslide susceptibility, hazard and risk in the High Himalayan terrain in India. A susceptibility map was made using the weights-of-evidence method, wherein weights were derived using the semi-automatically prepared historical landslide inventories combined with a series of pre-disposing factor maps. Seven evidence layers were used for the calculation of weights, selected in such a way that the majority could be derived from satellite data. Validation was done using the test data created through a temporal subsetting of the inventories. Temporal probability was calculated through Gumbel frequency distribution analysis using daily rainfall data of a 13 year period for which landslide inventories were prepared from satellite data. Spatial probability was determined by calculating landslide density for the inventories per susceptibility class that represent a given return period. Elements at risk, such as buildings and roads, were interpreted from a high resolution Cartosat-1 (2.5 m) image. Absolute vulnerability of the buildings and roads were multiplied with landslide spatial probability to derive the total loss for different return-period scenarios and shown in a risk curve. This study has shown that the inventories prepared semi-automatically can be used for landslide hazard and risk assessment in a data-poor environment. (C) 2012 Elsevier B.V. All rights reserved.