Different methods for topographic correction of Landsat Thematic Mapper images have been assessed in the context of mapping vegetation types. The best results were obtained with a variation of the C method, which takes into account the overcorrection of low illuminated slopes by the original C method. The performance of this method was tested using two criteria: the changes in the spectral characteristics of the image and the reduction in standard deviation of each vegetation type after the correction.

The objective of this paper was to define indices based on reflectance measurements performed by the Landsat Thematic Mapper (TM) sensor for estimating water content of live Mediterranean fuels for fire danger estimation. Seven Landsat TM images were processed and correlated with fuel moisture content (FMC) of several live species of Mediterranean grassland and shrubland. Raw bands were converted to reflectances, and several indices potentially related to water content were calculated from them. Pearson r correlation coefficients and linear regression analysis were computed in order to estimate FMC. Those indices based on the short wave infrared bands (SWIR: 1.4-2.5 w m) and on the contrast between this band and the near-infrared band offered the best estimations. For grassland, the integral of visible and SWIR bands provided the highest correlation, but also raw reflectances and Normalized Difference Vegetation Indices (NDVIs) provide significant r values ( r 2 above 0.8). For shrub species, indices that include SWIR reflectances performed much better than NDVI, because the SWIR band is more sensitive to water absorption, whereas NDVI estimates FMC indirectly, only from the effects of chlorophyll changes due to water variation content and leaf area index. The most significant relations were found with the derivatives of bands 4-5 and 2-3, and again the integral of visible and SWIR bands. Multiple regression analysis provided adjusted r 2 values of 0.84 for grasslands and 0.74 for shrublands. Average errors of 23.45-40% in the estimation of FMC for grasslands were found, depending on which variables were included in the multiple regression. For the FMC estimation of shrub species, errors were lower (from 7.94 to 19.40%), since the range of FMC values was also lower.

Fire risk assessment should take into account the most relevant components associated to fire occurrence. To estimate when and where the fire will produce undesired effects, we need to model both (a) fire ignition and propagation potential and (b) fire vulnerability. Following these ideas, a comprehensive fire risk assessment system is proposed in this paper, which makes extensive use of geographic information technologies to offer a spatially explicit evaluation of fire risk conditions. The paper first describes the conceptual model, then the methods to generate the different input variables, the approaches to merge those variables into synthetic risk indices and finally the validation of the outputs. The model has been applied at a national level for the whole Spanish Iberian territory at 1-km2 spatial resolution. Fire danger included human factors, lightning probability, fuel moisture content of both dead and live fuels and propagation potential. Fire vulnerability was assessed by analysing values-at-risk and landscape resilience. Each input variable included a particular accuracy assessment, whereas the synthetic indices were validated using the most recent fire statistics available. Significant relations (P < 0.001) with fire occurrence were found for the main synthetic danger indices, particularly for those associated to fuel moisture content conditions.