Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets   总被引：10，自引：2，他引：8  

Zonghu Liao  Yang Hong  Jun Wang  Hiroshi Fukuoka  Kyoji Sassa  Dwikorita Karnawati  Faisal Fathani 《Landslides》2010,7(3):317-324

An early warning system has been developed to predict rainfall-induced shallow landslides over Java Island, Indonesia. The prototyped early warning system integrates three major components: (1) a susceptibility mapping and hotspot identification component based on a land surface geospatial database (topographical information, maps of soil properties, and local landslide inventory, etc.); (2) a satellite-based precipitation monitoring system () and a precipitation forecasting model (i.e., Weather Research Forecast); and (3) a physically based, rainfall-induced landslide prediction model SLIDE. The system utilizes the modified physical model to calculate a factor of safety that accounts for the contribution of rainfall infiltration and partial saturation to the shear strength of the soil in topographically complex terrains. In use, the land-surface “where” information will be integrated with the “when” rainfall triggers by the landslide prediction model to predict potential slope failures as a function of time and location. In this system, geomorphologic data are primarily based on 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, digital elevation model (DEM), and 1-km soil maps. Precipitation forcing comes from both satellite-based, real-time National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM), and Weather Research Forecasting (WRF) model forecasts. The system’s prediction performance has been evaluated using a local landslide inventory, and results show that the system successfully predicted landslides in correspondence to the time of occurrence of the real landslide events. Integration of spatially distributed remote sensing precipitation products and in-situ datasets in this prototype system enables us to further develop a regional, early warning tool in the future for predicting rainfall-induced landslides in Indonesia.  相似文献