HR: 08:00h
AN: H31L-01 INVITED    [Abstracts]
TI: Comparison of a Global Landslide Event Inventory to a Satellite-based Landslide Algorithm
AU: * Bach, D
EM: dbach@ldeo.columbia.edu
AF: Lamont-Doherty Earth Observatory, Columbia University, 61 Route 9W, Palisades, NY 10964, United States
AU: * Bach, D
EM: dbach@ldeo.columbia.edu
AF: NASA Goddard Space Flight Center, Laboratory for Atmospheres, 8800 Greenbelt Road, Greenbelt, MD 20771, United States
AU: Adler, B
EM: adler@agnes.gsfc.nasa.gov
AF: NASA Goddard Space Flight Center, Laboratory for Atmospheres, 8800 Greenbelt Road, Greenbelt, MD 20771, United States
AU: Hong, Y
EM: yanghong@agnes.gsfc.nasa.gov
AF: NASA Goddard Space Flight Center, Laboratory for Atmospheres, 8800 Greenbelt Road, Greenbelt, MD 20771, United States
AU: Hong, Y
EM: yanghong@agnes.gsfc.nasa.gov
AF: Goddard Earth Science Technology Center/University of Maryland Baltimore County, 5523 Research Park Drive, Suite 320, Baltimore, MD 21228, United States
AU: Hill, S
EM: sh48669@students.salisbury.edu
AF: Salisbury University, 1101 Camden Ave., Salisbury, MD 21801, United States
AB: 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 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. Results indicate that the general pattern of landslide activity (number of total events, geographic distribution, etc.) can be reproduced, but finer-scale distributions and individual events are difficult to match between the forecast and the event inventory. Preliminary results indicate that three-fourths of the landslide events correspond to locations with high susceptibility values based on the satellite-based Landslide Susceptibility Index map. Probability of Detection and False Alarm Rate statistics are presented for the global database, with results varying based on the size of area used for event validation. Results are also shown to be a function of population density with more densely populated areas having higher scores, as expected. This global algorithm represents the first phase in identifying landslide hazards at this scale. With adjustment, the algorithm shows great promise in approaching landslide hazard assessment globally and providing information for the research community to address landslide issues in a broader context. The evaluation also provides insight into the necessary considerations and potential adaptations to the algorithm for improved landslide hazard forecasting on a global scale and the need for international efforts for developing accurate landslide inventories.
DE: 0468 Natural hazards
DE: 0758 Remote sensing
DE: 1810 Debris flow and landslides
SC: Hydrology [H]
MN: 2007 Fall Meeting