Automatic landslide mapping from satellite imagery with a topography-driven thresholding algorithm

Abstract

We present an improvement of image classification for landslide mapping by “thresholding”, using topographic information to determine multiple thresholds. We devised a two-steps procedure for automatic classification into landslide or no landslide categories of a change-detection map obtained from satellite imagery. Requirements of the proposed procedure are knowledge of the occurrence of a landslide event, availability of a pre- and postevent pseudo-stereo image pair and a digital elevation model. The novel feature of the approach is represented by the use of slope units as topographic-aware subsets of the scene within which we apply a multiple thresholding method to classify a landslide class membership tuned on the sole landslide spectral response. The method is fully automatic after site-dependent operations, required only once, are performed, and exhibits improved classification performance with limited training requirements. Our automatic procedure is a step forward towards systematic acquisition of landslide events and real-time landslide mapping from satellite imagery.

The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

“…Regardless of their importance, landslide inventories are often not available due to incomplete event records, or as a result of the lack of time and resources to update them, for example in response to extreme events (Malamud et al, 2004). To map landslides across large regions using manual techniques is a highly time consuming task, and although the use of automatic mapping tools is increasing (Alvioli et al, 2018;Borghuis et al, 2007;Kirschbaum and Stanley, 2018;Scheip and Wegmann, 2020), their widespread applicability still presents some limitations. It is particularly challenging in regions hit by the passage of typhoons, where the area affected by landslides can be up to hundreds of km 2 (e.g.…”