Mapping Co-Seismic Landslides in Vegetated Areas by Incorporating Tri-Temporal Logical Information in Change Detection Method

Efficient and reliable co-seismic landslide mapping is essential for emergency response, facilitating rescue, and post-disaster reconstruction after an earthquake. Many earthquake-prone areas globally are vegetated, making it imperative to conduct focused research on co-seismic landslides in these areas. Remote sensing images offer significant advantages for obtaining co-seismic landslide maps due to their large area coverage and short revisit time. However, many existing methods can hardly meet the accurate, robust, and efficient needs of co-seismic landslide mapping, as they encounter difficulties in differentiating co-seismic landslides from historical landslides and face complications due to other seismic-induced changes. To address these challenges, an automatic change detection-based method for fine co-seismic landslide mapping in vegetated areas was proposed. This method enabled automated landslide mapping in areas with vegetation through change detection and ensemble learning strategy incorporating logical information and spectral features of tri-temporal remote sensing images. Three experiments were carried out through PlanetScope images covering typical areas in China and Japan. The results showed that the proposed method outperformed comparative methods, including iterated principal component analysis (ITPCA), spectral angle mapper (SAM), and deep change vector analysis (DCVA). The overall accuracy (OA) of the proposed approach exceeded 94%, and both the Kappa coefficient and F1-score surpassed 0.87. These findings confirmed the superiority of the proposed method and demonstrated its promising prospects in emergency response in the future.