Evaluation of a Satellite-based Landslide Algorithm using Global Landslide Inventories
Dalia Bach, Lamont-Doherty Earth Observatory, Columbia University, New York
A global, satellite-based landslide algorithm has been developed using surface information and multi-satellite rainfall data. The technique integrates surface parameters such as slope, land cover, soils, and elevation with TRMM multi-satellite precipitation data to obtain an estimate of areas susceptible to landslides in near-real time. This research compares the predictions from the global landslide algorithm run retrospectively for individual years with global landslide inventories to assess both the relative skill of the technique and the value of currently available landslide information on a global scale.
This algorithm represents the first phase in identifying landslide hazards at this scale. The evaluation provides insight into the necessary considerations and potential adaptations to the algorithm for improved landslide hazard forecasting at both global and regional scales. The next step in this research is to regionalize the landslide algorithm, focusing on the Central America and Caribbean region.