Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images

We present a method for the semi-automatic recognition and mapping of recent rainfall induced shallow landslides. The method exploits VHR panchromatic and HR multispectral satellite images, and was tested in a 9.4 km2 area in Sicily, Italy, where on 1 October 2009 a high intensity rainfall event caused shallow landslides, soil erosion, and inundation. Pre-event and post-event images of the study area taken by the QuickBird satellite, and information on the location and type of landslides obtained in the field and through the interpretation of post-event aerial photographs, were used to construct and validate a set of terrain classification models. The models classify each image element (pixel) based on the probability that the pixel contains (or does not contain) a new rainfall induced landslide. To construct and validate the models, a procedure in five steps was adopted. First, the pre-event and the post-event images were pan-sharpened, ortho-rectified, co-registered, and corrected for atmospheric disturbance. Next, variables describing changes between the pre-event and the post-event images attributed to landslide occurrence were selected. Next, three classification models were calibrated in a training area using different multivariate statistical techniques. The calibrated models were then applied in a validation area using the same set of independent variables, and the same statistical techniques. Lastly, combined terrain classification models were prepared for the training and the validation areas. The performances of the models were evaluated using four-fold plots and receiver operating characteristic curves. The method proved capable of detecting and mapping the new rainfall induced landslides in the study area. We expect the method to be capable of detecting analogous shallow landslides caused by similar (rainfall) or different (e.g. earthquake) triggers, provided that the event slope failures leave discernable features captured by the post-event satellite images, and that the terrain information and satellite images are of adequate quality. The proposed method can facilitate the rapid production of accurate landslide event-inventory maps, and we expect that it will improve our ability to map landslides consistently over large areas. Application of the method will advance our ability to evaluate landslide hazards, and will foster our understanding of the evolution of landscapes shaped by mass-wasting processes.

Research highlights
► We present a method, based on satellite images, for landslide semi-automatic mapping.
► Change detection methods are combined to detect different changes in the images.
► Classification models are applied to the change detection results to map landslides.
► The performance of the classification models are measured in a validation area.