Automatic mapping of surfaces affected by forest fires in Spain using AVHRR NDVI composite image data

In this work, we describe the statistical techniques used to analyze images from the National Oceanic and Atmospheric Administration's advanced very high resolution radiometer for the calculation and mapping of surfaces affected by large forest fires in Spain in 1993 and 1994. Maximum value normalized difference vegetation index (NDVI) composites (MVCs) were generated for every ten-day period over the two years of the study. Two techniques, one regression analysis and the other differenc ing, were applied to the NDVI-MVCs both before and after each fire event to determine detection thresholds of change and to delineate and objectively evaluate the burned surfaces. The comparison between the single-fires bu med areas predicted by the techniques and that provided by the Spanish Forestry Service (ground based) showed that the regression algorithm was more reliable, giving rise to virtually no bins (-0.9%) and a root mean square error (RMS) of 20.3%, both calculated as a percentage of the mean bw med area of the whole sample. The technique of differencing provided worse results with a 3.2% bias and a 23.5% RMS error. Likewise, a comparison between the perimeters of the large fires supplied by official data (GPS-based) and those obtained by the regression method confirmed the validity of the technique not only for calculating fire size, but also for mapping of large forest fires. (C) Elsevier Science Inc., 1997.