Near real-time landslide hazard assessment using remotely sensed data

Remote sensing data offers the unique perspective to provide situational awareness of hydrometeorological hazards over large areas in a way that is impossible to achieve with in situ data. Recent work has shown that rainfall-triggered landslides, while typically local hazards that occupy small spatial areas, can be approximated over regional scales in near real-time. By leveraging data from the Global Precipitation Measurement (GPM) mission, topographic data from the Shuttle Radar Topography Mission (SRTM) and other remote and in situ sources, we can represent the conditions for landslide triggering over broad regions. The landslide hazard assessment for situational awareness (LHASA) model integrates satellite precipitation data, a modeled and satellite-based soil moisture product and susceptibility information to improve the characterization of areas that may experience landslide activity at regional and global scales. The goal of LHASA is to better inform decision-making and disaster response agencies on landslide hazards at the regional and global scale. This system outputs straightforward landslide hazard assessment products available in near real-time that can be used to identify landslide-prone areas and the general timing of landslide initiation. This presentation summarizes the results of this modeling framework, discusses the utility of remote sensing products for landslide hazard characterization, and outlines the path forward for this modeling approach.