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Remote Sensing

Fuels Classification Mapping

To cover remote areas or large land coverage, the use of remote sensing data is fundamental for the definition of fire fuel loads and modeling. The use of imagery provides for fuel classification and monitoring of ecosystems. The changes in ground cover results in the change of spectral response of the imagery.

Fuels can be classified in Fuel Models for Fire Behavior. The groups include grass, light brush, timber, and slash. With the use of algorithms, a classification can automatically establish relationships of the variations of fuels. The classification scheme is based on the vegetation cover type or species, the development stage, percent of vegetation coverage.

Regular updating of information allows for changes in fuel loads, land use changes, and land management practices. Recently, Idaho State University collected data for the "WILDFIRE EFFECTS ON RANGELAND ECOSYSTEMS AND LIVESTOCK GRAZING IN IDAHO" research project. The goal of this study is to develop predictive models of wildfire fuel load and wildfire risk. To understand how remote sensing is used in the field, please follow the above link.

landsat false color image of Pocatello