Wildfire Detection in the Iztaccíhuatl-Popocatépetl Protected Natural Area Using Spectral Indices and Logistic Regression

Abstract

Wildfires are part of terrestrial ecosystem processes; however, their frequency and intensity have recently increased due to both natural and anthropogenic factors. Geospatial data are essential for analyzing land cover changes at high spatial resolution, making the development of tools that use this information to detect burned areas particularly important, especially in regions of high ecological value. This study aimed to detect burned areas within the Iztaccíhuatl–Popocatépetl Protected Natural Area in central Mexico using a logistic regression model based on spectral variables such as NDVI, RBRc, and SWIR2 derived from Sentinel-2 imagery. The agreement between observed and classified data yielded Kappa coefficients and overall accuracy values of 0.79. Model performance varied with probability threshold: low thresholds achieved higher metrics, while high thresholds produced a more conservative delineation that was spatially more coherent with the reference polygons, prioritizing pixels with higher probability of being affected and generating maps more consistent with actual burned areas. Overall, the model performed well in detecting burned areas, providing a useful tool for fire monitoring. However, it is recommended to conduct analyses by vegetation type to increase model accuracy, as phenological variability associated with vegetation types can influence spectral responses and reduce precision.