A new spectral index named Burned Area Index (BAI), specifically designed for burned land discrimination in the red-near-infrared spectral domain, was tested on multitemporal sets of Landsat Thematic Mapper (TM) and NOAA Advanced Very High Resolution Radiometer (AVHRR) images. The utility of BAI for burned land discrimination was assessed against other widely used spectral vegetation indices: Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Global Environmental Monitoring Index (GEMI). BAI provided the highest discrimination ability among the indices tested. It also showed a high variability within scorched areas, which reduced the average normalized distances with respect to other indices. A source of potential confusion between burned land areas and low-reflectance targets, such as water bodies and cloud shadows, was identified. Since BAI was designed to emphasize the charcoal signal in post-fire images, this index was highly dependent on the temporal permanence of charcoal after fires.

This paper presents methods to generate fuel type maps from remote sensing data at a spatial and temporal scale adequate for operational fire management applications. Fuel type maps account for structural characteristics of vegetation related to fire behaviour and fire propagation. A fuel type classification system adapted to the ecological characteristics of the European Mediterranean basin was adopted for this study. The Cabañeros National Park (in central Spain) area was selected for testing and validating the methods. Fuel type maps were derived from two Landsat TM satellite images and digital elevation data. Atmospheric and topographic corrections of the satellite images were performed to reduce spectral variability. A sensitivity analysis was carried out to determine the most appropriate bands for fuel type mapping. The final classification was checked by an intense field survey, the final classification accuracy being estimated at 83%. The main problem was discriminating among those fuel types that differ only in vegetation height or composition of the understory layer. The mean mapping accuracy was 15 m (0.6 pixels), and no areal discrepancy or boundary displacement with vegetation maps was apparent.