A semiautomatic pixel-object method for detecting landslides using multitemporal ALOS-2 intensity images

Bruno Adriano, Naoto Yokoya, Hiroyuki Miura, Masashi Matsuoka, Shunichi Koshimura

    Research output: Contribution to journalArticlepeer-review

    11 Citations (Scopus)

    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.

    Original languageEnglish
    Article number561
    JournalRemote Sensing
    Volume12
    Issue number3
    DOIs
    Publication statusPublished - 2020 Feb 1

    Keywords

    • Japan
    • Landslide damage detection
    • Synthetic aperture radar (SAR) intensity imagery
    • The 2018 Mw6.7 hokkaido earthquake
    • The 2018 torrential rain event in hiroshima

    ASJC Scopus subject areas

    • Earth and Planetary Sciences(all)

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