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Spatial and Statistical Characterization of Forest Fuels Distribution
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Characterization of forest fuels is a key component of the successful implementation of any fire management system. Great advances have been made in the characterization of forest cover by the use of remote sensing technology. Characterization of understory fuels, however, has been limited always by the difficulty of current remote sensing products to observe through the canopy. This limitation underscores the importance of exploring approaches related to site environment characteristics that can influence the production and accumulation of understory fuels. This work explores the relationships between topography, fire history, vegetation type and downed woody fuel loadings in the forested area of the Rincon Mountains in southern Arizona. The Classification and Regression Trees method is used to predict fuel loads through the influence of predictor variables. Results show that fine woody fuel loads tend to be explained by vegetation type and slope, while coarse woody fuels are predicted best by differences in elevation.
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