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Processing LiDAR Data to Predict Natural HazardsELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.
Document ID
Acquisition Source
Stennis Space Center
Document Type
Other - NASA Tech Brief
Fairweather, Ian
(HyPerspectives Bozeman, MT, United States)
Crabtree, Robert
(HyPerspectives Bozeman, MT, United States)
Hager, Stacey
(NASA Stennis Space Center Stennis Space Center, MS, United States)
Date Acquired
August 24, 2013
Publication Date
September 1, 2008
Publication Information
Publication: NASA Tech Briefs, September 2008
Subject Category
Meteorology And Climatology
Report/Patent Number
Distribution Limits
Public Use Permitted.
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