Processing LiDAR Data To Predict Natural Hazards
- Created: Monday, 01 September 2008
ELF-Base and ELF-Hazards (wherein “ELF” signifies “Extract LiDAR Features” and “LiDAR” signifies “light detection and ranging”) are developmental software modules for processing remotesensing 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, ELFderived 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.
This program was written by Ian Fairweather and Robert Crabtree of HyPerspectives Inc. and Stacey Hager of Yellowstone Ecological Research Center for Stennis Space Center.
Inquiries concerning rights for its commercial use should be addressed to:
2048 Analysis Drive Suite C
Bozeman, MT 59718
Phone No.: (406) 556-9880
Refer to SSC-00279, volume and number of this NASA Tech Briefs issue, and the page number.