Locating landslides using multi-temporal satellite images

Landuse/landcover change detection using remotely sensed images has been widely investigated. Most applications of this type involve either image differencing or image classification using multi-temporal images. If multi-temporal images are to be used for quantitative analysis based on their radiometric information, as in the case of change detection or landuse classification, geometric rectification and radiometric correction must be performed priori to subsequent image analyses. In particular, geometric rectification has significant effect on the accuracy of landuse change detection in areas of rugged terrain. Remote sensing image rectification is commonly done by applying a polynomial trend mapping (PTM) model to image coordinates and map coordinates of ground-control-points. A major drawback of the PTM model is that it does not capture the random characteristics of terrain elevation. In this study an ordinary kriging approach is applied for image-to-image registration. The approach considers residuals of the PTM model as anisotropic random fields and employs the ordinary kriging method for spatial interpolation of the residual random fields. Band-ratioing technique was also employed for relative radiometric normalization. From the grey-level histograms of pre- and post-event band-ratio images, we determined the percentage of landuse changes in the study area. Image differencing was then performed using the pre- and post-event band-ratio image pair. Finally, a grey-level threshold of the band-ratio difference image is determined as the value whose exceeding probability equals the areal percentage of landuse change. DTM data of the study area were also used to further restrict landslide areas to steep slope areas. (C) 2003 COSPAR. Published by Elsevier Ltd. All rights reserved.