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Remote sensing has been shown to be an efficient tool in the study of forest-fire processes. However, a lack of information on the amount of biomass burnt reduces the accuracy of fire severity and emission models. In this study, we use imagery from the Landsat Thematic Mapper to map crown biomass and burn severity for a large Mediterranean area. Considering the specific characteristics of the Mediterranean environment, two methods to extract useful remote sensing data were employed; both sought to analyze relationships between crown biomass and spectral information. As a result, a crown biomass map of Pinus spp. was created for the entire study area, applying nonlinear regression using the variable MID57 (TM5 + TM7) (R2 = 0.651). Considering only P. halepensis pixels that were burnt in the selected fire scar, the relationships between crown biomass and burn severity were found to be high and significant, yielding an R2 value of 0.516. Finally, a logistic regression model was constructed to map the presence or otherwise of high burn severity levels using crown biomass as the independent variable, yielding in the confusion matrix an overall percentage of data points correctly classified of 77% and a Kappa statistic in the validation sample of 0.554.

Fire severity can be considered one of the most influential factors in the postfire development of burnt areas. Ground level studies documenting changes in reflectance values improve the discrimination of spatial fire severity across large areas. The objective of this study was to determine those spectral regions most sensitive to fire severity levels by investigating the relationship between postfire surface materials and reflectance values. A total of 34 field plots were analyzed immediately following two natural fires in Spain. To obtain postfire data, we used a portable structure to obtain vertical photographs and reflectance values in the visible-near-infrared (VIS-NIR) range. Spectral reflectance and convolved Landsat Thematic Mapper (TM) bands were statistically correlated with individual postfire materials and with a fire severity index, the Combustion Products Index (CPI), derived from them. The wavelengths most sensitive to postfire materials were 450.6, 758.2, and 797 nm for ash, black carbon, and vegetation, respectively, although these wavelengths were included within ranges of comparable behavior. Finally, stepwise multiple regression models (SMLRs) were applied to the data. SMLR predicted ash, black carbon, and vegetation levels from reflectance data with accuracies higher than 75%. Estimation from Landsat-TM bands yielded slightly lower accuracies. The CPI severity index was also well estimated using either reflectance data or TM bands (r 2 = 0.925 and r 2 = 0.840 , respectively).