Abstract
The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively.
Highlights
Landslides are significant disasters that occur around the world, causing severe damage to infrastructures and widespread loss of human lives [1,2]
The bottom panels show the area around the town of Kumano, where debris flows were effectively detected in downstream areas
Taking into account that the debris flow orientations were almost parallel to the synthetic aperture radar (SAR) range direction, the extracted segments indicate that our algorithm is robust to different orientation angles between debris flows and microwave radiation
Summary
Landslides are significant disasters that occur around the world, causing severe damage to infrastructures and widespread loss of human lives [1,2]. The aftershocks of the 2015 M7.5 Nepal earthquake triggered landslides at several locations, including on Mount Everest, where 21 people were killed; the most destructive landslide struck Langtang Valley, where 350 people lost their lives [8,9]. In this context, Earth observation technologies represent some of the best methods for monitoring and assessing post-disaster damage following major landslides. As described by Guzzettiat et al [10], the visual interpretation of optical imagery is efficient for detecting fresh landslides; in areas where signs left by terrain failure are visible, images acquired before and after the landslide event can be utilized effectively. This technique is ordinarily time-consuming and requires significant human effort; as such, this method is not suitable for rapid damage response endeavors
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