Automatic detection of landslide impact areas using Google Earth Engine

Abstract

This research introduces an effective framework for automatically detecting landslide impact areas using Google Earth Engine (GEE). The Asia–Pacific region frequently experiences earthquakes and heavy rainfall, leading to frequent landslides that cause loss of life and property. Focusing on landslide catalogues from Taiwan and Japan, the study proposes an automatic landslide detection process using a new method termed multi-bitemporal images (MBTIs), which involves the collection of accumulated changes over time. First, set the event date and collect all images before and after the event. Second, analyse the change pixels in bi-temporal images. Third, review all change pixels to determine the total amount of accumulated changes. This method includes all bi-temporal image sets in the analysis, unlike traditional methods that only use single pairs of bi-temporal images. Clouds are filtered using a pixel-based approach and machine learning techniques. The landslide areas are analysed statistically, and appropriate thresholds for automatic landslide detection are suggested. Using reproducibility, which indicates the percentage of bi-temporal images that detect vegetation loss in mountainous areas, the proposed method achieves a 99% reduction in false positives with a reproducibility requirement of 24.21%, while maintaining true positives at 66.89%. This study analyzed 28–720 bi-temporal image sets from various regions using Sentinel-2 data, revealing that subsequent landslides can be 7–293 times larger than co-seismic landslides. In comparison, subsequent landslides were found to be 3–12 times larger than rainfall-induced landslides. Additionally, the impact of earthquake event on subsequent landslides is 2.3–24.4 times greater than that of rainfall-induced event. By using GEE, the accumulation of hundreds of satellite images can be completed within 15 min, depending on the processing requirements.