Detecting and monitoring the Shallow Landslides in Agricultural Environments using Google Earth and Sentinel-2 images: two case studies

The detection and monitoring of shallow surface landslides in agricultural environments using remote sensing imagery present several critical challenges. These landslides are often very small, resulting in a limited number of pixels representing the landslide body. Moreover, their occurrence is frequently intermittent, as seasonal rainfall may trigger slope failures that are subsequently altered or erased by agricultural practices such as plowing, making multi-temporal analysis complex. To monitor these landslides a methodology originally developed for the detection of buried archaeological remains was adopted, as both applications face comparable detection constraints. The approach is based on the quantitative analysis of “tonal” differences between pixels corresponding to landslide-affected areas and those of the surrounding stable terrain. Several image-processing products were generated to enhance and measure these subtle spectral and tonal variations. This quantitative framework plays a key role in reducing subjectivity related to the experience of photo-interpreters and in limiting uncertainties associated with image processing and interpretation.