The primary criteria that influence shallow landslides are precipitation intensity, slope, soil type, elevation,
vegetation, and land cover type. Drawing on recent advances in remote sensing technology and the
abundance of global geospatial products, this paper proposed a conceptual framework for a real-time
prediction system (Fig. 1) for rainfall-triggered landslides across the globe. This system combines the
NASA TMPA precipitation information (Fig. 2; http://trmm.gsfc.nasa.gov) and land surface characteristics
to assess landslides. First, a prototype of a global landslide susceptibility map (Fig. 3) is produced using
high-spatial scale DEM, slope, soil type information downscaled from the Digital Soil Map of the World
(sand, loam, silt, clay, etc.), soil texture, and MODIS land cover classification. Second, this map is overlaid
with satellite-based observations of rainfall intensity-duration (Fig. 4), to identify the location and time of
landslide hazards when areas with significant landslide susceptibility are receiving heavy rainfall. This
preliminary landslide detection system shows promising effectiveness by comparing to recent landslide
events that occurred during the TRMM operational period (Table 1). A major outcome of this work is the
availability of a global prospective on rainfall-triggered landslide disasters, only possible because of the
utilization of global satellite products. This type of real-time prediction system for disasters could provide
policy planners with overview information to assess the spatial distribution of potential landslides.
However, ultimate decisions regarding site-specific landslide susceptibility will continue to be made only
after a site inspection.
A global evaluation of this system is underway through comparison with various field databases, web sites
and news reports of landslide disasters. The need for retrospective validation and improvement of this
preliminary system requires continued collection of global landslide data. The prototype of this system can
be enhanced by providing improved satellite remote sensing products and by updating the geospatial
database as more relevant information becomes available. Specifically, the land cover data should be
routinely updated because they are subject to change by human activity. Several future activities are under