California: Researchers at the University of California, United States believe satellite dataset could be used for predicting landslides and to save precious life and property in remote mountainous regions.
Typically, landslides occur in mountainous regions where radar and gauge measurements (used in standard global landslide models) are not available. In some countries, even the ground-based observations are limited. The researchers at University of California have developed a model that is based on satellite data on rainfall, topographical features of slopes, and land cover. Researchers tested the model on a dataset of previous landslides and they found that it predicted the historical events reliably. The same model could be tested on real-time dataset to predict potential landslides, particularly in remote mountainous regions. However, the model “cannot be considered as a general landslide model” as it does not take earthquake-triggered landslides into account, and is not designed for small-scale landslides (local events not reported in the NASA global landslide inventory, which is the data used to calibrate the model). But it can be “coupled with a local physical model to improve landslide monitoring prediction” by first using the satellite model to identify landslide hotspots and then applying a physical model for slope failure to the hotspots. “Our model has been developed with satellite data so that it can be used (globally) in remote and topographically complex regions. Most previous landslide studies have been at a local or regional scale,” said Amir AghaKouchak, co-author and assistant professor at the Center for Hydrometeorology and Remote Sensing in Irvine.