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 70-100per cent 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
colour/infrared photography.
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