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A logistic model was constructed to assess the risk of forest fire and tested over the central region of Mexico. The model incorporates both static and dynamic predictive variables: elevation, aspect, slope, vegetation type, precipitation, Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and cloud cover. The latter three variables were derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images from the four months (November to February) before the fire seasons. Actual forest fires were detected on NOAA-AVHRR images from the four fire seasons (March to May) from 1997 to 2000. Variables included in the model were chosen following a stepwise strategy. Statistically, the January NDVI, the February LST, vegetation type and slope had the greatest influence on the distribution of forest fires; however, elevation and precipitation were also included in the final model. The probability of forest fire occurrence for each fire season from 1997 to 1999 was mapped. The accuracy of the model was estimated to be 79.8% with reference to sensitivity, specificity and receiver operating characteristic (ROC) curves. Model predictions were validated against data from the 2000 fire season. The fire occurrence probability map is useful for designing large-scale management strategies for wildfire prevention not only in the test area of this study but also in regions where the static and dynamic variables can be similarly defined.
Manzo-Delgado et al. (Tue,) studied this question.