Combination of optical images and SAR images for detecting landslide scars, using a classification and regression tree

Abstract

Landslides are some of the most destructive and recurrent natural hazards worldwide. Landslides are triggered by natural phenomena such as extreme rainfall and earthquakes, causing human and economic losses. A rapid response to landslide events is necessary to assess damage mitigation and save lives and property. This study developed a landslide detection model using differential spectral indices and amplitude ratio changes with a classification and regression tree (CART), aiming to detect landslide scars after the occurrence of these events in Asian regions for testing different environment condition. The multi-temporal SAR and optical stack images were pre-processed to reduce speckle noise, seasonal noise, and atmospheric noise. This study explored change detection approaches with a minimum threshold of amplitude ratio change (Aratio), using Sentinel−1 images and the relative difference in the normalized difference vegetation index (rdNDVI), differential bare soil index (dBSI), and differential brightness index (dBI) was obtained using Sentinel−2 images. The accuracy of the model was examined by F1-scores. The accuracy of the model for landslide detection was considered moderately good to excellent. As a result of the landslide detection model, amplitude ratio change detection improved the model as revealed by the F1-scores. Moreover, this study found that differential spectral indices could be used to classify the types of landslides (deep-seated and shallow landslides) according to the level of surface changes and texture of the collapsed material after landslide events.