The rapid and accurate mapping of landslides is critical for emergency response, disaster mitigation, and understanding landslide processes. Here we present a new approach to detect landslides anywhere in the world using freely available synthetic aperture radar data and open source tools in Google Earth Engine. Importantly, our methods do not require specialized processing software or training, which allows the broader hazards community to utilize these state-of-the-art remote sensing tools.
The rapid and accurate mapping of landslides is critical for emergency response, disaster...
Review status: this discussion paper is a preprint. It has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). The manuscript was not accepted for further review after discussion.
Rapid landslide identification using synthetic aperture radar amplitude change detection on the Google Earth Engine
Alexander L. Handwerger1,2,Shannan Y. Jones3,Mong-Han Huang3,Pukar Amatya4,5,6,Hannah R. Kerner7,and Dalia B. Kirschbaum6Alexander L. Handwerger et al.Alexander L. Handwerger1,2,Shannan Y. Jones3,Mong-Han Huang3,Pukar Amatya4,5,6,Hannah R. Kerner7,and Dalia B. Kirschbaum6
Received: 23 Sep 2020 – Accepted for review: 06 Oct 2020 – Discussion started: 07 Oct 2020
Abstract. The rapid and accurate mapping of landslides is critical for emergency response, disaster mitigation, and improving our understanding of where landslides occur. Satellite-based synthetic aperture radar (SAR) can be used to identify landslides, often within days after triggering events, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Although there are many landslide detection methods using SAR, most require downloading a large volume of data to a local system and specialized processing software and training. Here we present a SAR-based amplitude change detection approach designed for those without SAR expertise that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to identify landslides on Google Earth Engine (GEE). We provide strategies that can aid in rapid response and event inventory mapping. We test our GEE-based approach in a ~ 277 km2 area in Hiroshima Prefecture, Japan where ~ 3,800 landslides were triggered by rainfall in July 2018. Our ability to detect landslides improves with the total number of SAR images acquired before and after the landslide event, by combining both ascending and descending acquisition geometry data, and by using topographic data to mask out flat areas unlikely to experience landslides. Importantly, our GEE approach allows the broader hazards and landslide community to utilize these state-of-the-art remote sensing data.
The rapid and accurate mapping of landslides is critical for emergency response, disaster mitigation, and understanding landslide processes. Here we present a new approach to detect landslides anywhere in the world using freely available synthetic aperture radar data and open source tools in Google Earth Engine. Importantly, our methods do not require specialized processing software or training, which allows the broader hazards community to utilize these state-of-the-art remote sensing tools.
The rapid and accurate mapping of landslides is critical for emergency response, disaster...