Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling

Land managers need cost-effective methods for mapping and characterizing forest fuels quickly and accurately. The launch of satellite sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite sensor, and gradient modeling, for mapping fuel layers for fire behavior modeling with FARSITE and FLAMMAP. Empirical models, based upon field data and spectral information from an ASTER image, were employed to test the efficacy of ASTER for mapping and characterizing crown closure and crown bulk density. Surface fuel models (National Forest Fire Laboratory (NFFL) 1-13) were mapped using a classification tree based upon three gradient layers; potential vegetation type, cover type, and structural stage. The final surface fuel model layer had an overall accuracy of 0.632 (KHAT = 0.536). Results for the canopy fuel empirical models developed here suggest that vegetation indices incorporating visible wavelengths (i.e. the green red vegetation index (GRVI)) are suitable for predicting crown closure and crown bulk density (r2 = 0.76. and 0.46, respectively).