A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

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.Keywords: landslide damage detection; the 2018 torrential rain event in hiroshima; Japan; the 2018 Mw6.7 hokkaido earthquake; synthetic aperture radar (SAR) intensity imagery

Breaking Limits of Remote Sensing by Deep Learning From Simulated Data for Flood and Debris-Flow Mapping

“…Landslide detection using SAR intensity imagery is an alternative to optical image-based approaches in adverse weather conditions. However, its accuracy is limited by the presence of layovers and shadowing, particularly for narrow debris flows [19]- [22]. Interferometric SAR has been demonstrated to be advantageous in detecting large-scale, slowly moving landslides [23].…”

Investigation of the Effect of the Dataset Size and Type in the Earthquake-Triggered Landslides Mapping: A Case Study for the 2018 Hokkaido Iburu Landslides

“…The study area is located in the southwest of the Hokkaido region ( Figure 1A). The dominant land cover of the study area consists of forests and paddy fields and has a rugged, mountainous, and high slope topography (Zhang et al, 2019;Adriano et al, 2020). Hokkaido, which includes the study area, is a tectonically active region in the world.…”

“…Active and passive remote sensing systems offer great advantages in rapid landslide mapping (Aimaiti et al, 2019). With remote sensing systems, Optical images (Zhao et al, 2017;Shao et al, 2019), synthetic aperture radar (SAR) (Aimaiti et al, 2019;Adriano et al, 2020), LIDAR systems (Liu et al, 2019), Unmanned Aerial Vehicle (UAV) systems (Comert et al, 2019) or synthesis of optical and SAR images (Shirvani et al, 2019) can be used in the landslide mapping. The most common method preferred in mapping landslides is visual image interpretation (Guzzetti et al, 2012;Rosi et al, 2018).…”

Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping

“…Landslide detection using SAR intensity imagery is an alternative to optical image-based approaches in adverse weather conditions. But its accuracy is limited by the presence of layovers and shadowing particularly for narrow debris flows [19]- [22]. Interferometric SAR has been demonstrated to be advantageous in detecting large-scale, slowly moving landslides [23].…”