- Author
-
Brumby, S. P.
|
Harvey, N. R.
|
Bloch, J. J.
|
Theiler, J.
|
Perkins, S.
|
Young, A. C.
|
Szymanski, J. J.
- Title
- Evolving Forest Fire Burn Severity Classification Algorithms for Multi-Spectral Imagery.
- Coporate
- Los Alamos National Laboratory, Los Alamos, NM
- Report
-
LA-UR-01-1500
March 1, 2001
11 p.
- Distribution
- For more information contact: Website: http://lib-www.lanl.gov/la-pubs/00367083.pdf AVAILABLE FROM National Technical Information Service (NTIS), Technology Administration, U.S. Department of Commerce, Springfield, VA 22161. Telephone: 1-800-553-6847 or 703-605-6000; Fax: 703-605-6900; Rush Service (Telephone Orders Only) 800-553-6847; Website: http://www.ntis.gov
- Keywords
-
forest fires
|
algorithms
|
classifications
|
wildland fires
|
fire damage
- Identifiers
- multispectral imagery; genetic proramming; supervised classification
- Abstract
- Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100% tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial color/infrared photography.