Landslide Monitoring Using Change Detection in Multitemporal Optical Imagery

Abstract

Landslides are a kind of geologic hazard triggered by anthropogenic or natural factors. Change detection is an important technique to extract the landslide area from pre- and postdisaster images. As landslides are similar in spectrum to bare land and it is difficult to absolutely calibrate the radiation of multitemporal images, the detection method may lead to significant errors or omissions. By modeling the relative relationship between adjacent pixels from multitemporal images, errors or omissions and illumination influences will be reduced during detection. With the aim of extracting landslides with an automatic and robust process, this letter proposes a practical method based on multitemporal data and spatiotemporal model. First, the normalized difference vegetation index (NDVI) and built-up area presence index (PanTex) features series were produced, which can reflect changes in vegetated and built-up areas, respectively. Then, we used a spatiotemporal context (STC) model to detect landslide from feature series. Finally, the landslide map could be derived. The proposed method was applied to detect landslide using GaoFen (GF) series satellite. The experimental results demonstrated the effectiveness and robustness of our method.