You are here: Home
/ Publication Information
Title: Remote sensing techniques aid in preattack planning for fire management
Author: Salazar, Lucy Anne;
Source: Res. Paper PSW-RP-162. Berkeley, CA: Pacific Southwest Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture; 19 p
Publication Series: Research Paper (RP)
Description: Remote sensing techniques were investigated as an alternative for documenting selected prettack fire planning information. Locations of fuel models, road systems, and water sources were recorded by Landsat satellite imagery and aerial photography for a portion of the Six Rivers National Forest in northwestern California. The two fuel model groups used were from the 1978 National Fire Danger Rating System and the Northern Forest Fire Laboratory. Landsat-derived fuel model data were digitized and computer analyzed by unsuperivesed and guided clustering techniques to produce a fuel model map of the area. Overall Landsat classification accuracies of fuel models were moderate-71 percent. This was mainly due to the incompatibilities found between fuel model descriptions and remote sensing capabilities. The results suggest, however, that a basic preattack plan that is moderately reliable, quickly attainable, and easily updated is feasible by applying remote sensing techniques.
Keywords: remote sensing, preattack planning, fire management, presuppression planning
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
XML: View XML
Salazar, Lucy Anne 1982. Remote sensing techniques aid in preattack planning for fire management. Res. Paper PSW-RP-162. Berkeley, CA: Pacific Southwest Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture; 19 p
Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility