Abstract
Timely information about landslides during or immediately after an event is an invaluable source for emergency response and management. Using an active sensor, synthetic aperture radar (SAR) can capture images of the earth’s surface regardless of weather conditions and may provide a solution to the problem of mapping landslides when clouds obstruct optical imaging. The 2018 Hokkaido Eastern Iburi earthquake (Mw 6.6) and its aftershocks not only caused major damage with severe loss of life and property but also induced many landslides across the area. To gain a better understanding of the landslides induced by this earthquake, we proposed a method of landslide mapping using pre- and post-event Advanced Land Observation Satellite 2 Phased Array L-band Synthetic Aperture Radar 2 (ALOS-2 PALSAR-2) images acquired from both descending and ascending orbits. Moreover, the accuracy of the classification results was verified by comparisons with high-resolution optical images, and ground truth data (provided by GSI, Japan). The detected landslides show a good match with the reference optical images by visual comparison. The quantitative comparison results showed that a combination of the descending and ascending intensity-based landslide classification had the best accuracy with an overall accuracy and kappa coefficient of 80.1% and 0.45, respectively.
Highlights
Earthquakes are one of the most dangerous natural disasters in the world
In case 2, we examined the performance of synthetic aperture radar (SAR) intensity for landslide mapping
The decision tree classification was established based on the calculated pre- and co-event coherence difference, the pre- and post-event intensity difference, digital elevation models (DEMs), and slope
Summary
A moderate to severe earthquake can trigger landslides in mountainous regions [1]. These landslides may cause injuries and loss of human life, may cause damage to infrastructure, and may lead to enormous economic losses. Quickly identifying and mapping these landslides has great importance for emergency response and restoration activities after disasters [2]. Owing to their capability for wide-area observations, relatively low cost and rapid advances, remote sensing satellite observations have enabled us to effectively detect and monitor landslides at individual and regional scales. The datasets from previous SAR sensors (e.g., ERS-1/-2, ENVISAT and ALOS PALSAR) and the new generation of C, X, and L-band SAR images provided by RADARSAT-2, Sentinel-1A, ALOS-2 PALSAR-2, TerraSAR-X, Tandem-X and the COSMO-SkyMed constellation have enabled us to determine historical and current landslides with high precision [8,9,10]
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