The remote sensing of fire severity and burned area is fundamental in the evaluation of fire impacts. The current study aimed to: (i) compare Sentinel-2 (S2) spectral indices to predict field-observed fire severity in Durango, Mexico; (ii) evaluate the effect of the compositing period (1 or 3 months), techniques (average or minimum), and phenological correction (constant offset, c, against a novel relative phenological correction, rc) on fire severity mapping, and (iii) determine fire perimeter accuracy. The Relative Burn Ratio (RBR), using S2 bands 8a and 12, provided the best correspondence with field-based fire severity (FBS). One-month rc minimum composites showed the highest correspondence with FBS (R² = 0.83). The decrease in R² using 3 months rather than 1 month was ≥0.05 (0.05–0.15) for c composites and <0.05 (0.02–0.03) for rc composites. Furthermore, using rc increased the R² by 0.05–0.09 and 0.10–0.15 for the 3-month RBR and dNBR compared to the corresponding c composites. Rc composites also showed increases of up to 0.16–0.22 and 0.08–0.11 in kappa values and overall accuracy, respectively, in mapping fire perimeters against c composites. These results suggest a promising potential of the novel relative phenological correction to be systematically applied with automated algorithms to improve the accuracy and robustness of fire severity and perimeter evaluations.

For decades there has been confusion and uncertainty on how to clearly identify the two distinct species, Pinus strobiformis Engelm. and Pinus ayacahuite Ehrenberg. Pinus ayacahuite is further defined as two varieties, var. ayacahuite and var. veitchii. These species are of ecological importance throughout much of Mexico because they offer valuable ecosystem services. The objective in this work was to examine the morphological characteristics of cones and seeds from parent trees from six sites in northern Mexico and three sites in southern Mexico. The results show that the mean seed width and length per tree, mean cone weight per tree, and the 100-seed weight per tree were the best variables to accurately separate P. strobiformis from P. ayacahuite. Using these criteria, we found that P. ayacahuite var. veitchii is more similar to P. strobiformis than to P. ayacahuite var. ayacahuite.

Mapping potential fire intensity is a fundamental tool for fire management planning. Despite the wide use of Fire Radiative Power (FRP) as an indicator of expected fire intensity and fire emissions, very few studies have spatially analyzed the role of remotely sensed proxies of vegetation productivity to explain FRP. The current study aimed at modeling and mapping the relationships between aboveground biomass and Moderate Resolution Imaging Spectroradiometer (MODIS) maximum FRP, at 1 km pixel, in 2011–2020, for each of 46 fuel regions in the entirety of Mexico. Maximum FRP–biomass relationships supported a novel hypothesis of varying constraints of fire intensity. In lower-productivity areas, such as semiarid shrub- and grass-dominated ecosystems, fine fuel loads limited fire occurrence and FRP was positively related to biomass. In the more productive areas, such as temperate or tropical forests, a humped relationship of FRP against biomass was observed, suggesting an intermediate-productivity hypothesis of maximum fire intensity within those regions. In those areas, the highest fire intensity was observed in the intermediate biomass areas, where surface (timber understory) and crown fuel availability, together with higher wind penetration, can result in crown fires. On the contrary, within the most productive areas, the lowest intensity occurred, likely due to weather and fuel (timber litter) limitations.

There is a need to evaluate Sentinel-2 (S2) fire severity spectral indices (SFSIs) for predicting vegetation and soil burn severity for a variety of ecosystems. We evaluated the performance of 26 SFSIs across three fires in seasonally dry oak–pine forests in central-western Mexico. The SFSIs were derived from composites of S2 multispectral images obtained with Google Earth Engine (GEE), processed using different techniques, for periods of 30, 60 and 90 days. Field verification was conducted through stratified random sampling by severity class on 100 circular plots of 707 m², where immediate post-fire effects were evaluated for five strata, including the canopy scorch in overstory (OCS)—divided in canopy (CCS) and subcanopy (SCS)—understory (UCS) and soil burn severity (SBS). Best fits were obtained with relative, phenologically corrected indices of 60–90 days. For canopy scorch percentage prediction, the indices RBR3c and RBR5n, using NIR (bands 8 and 8a) and SWIR (band 12), provided the best accuracy (R² = 0.82). SBS could be best mapped from RBR1c (using 11 and 12 bands) with relatively acceptable precision (R² = 0.62). Our results support the feasibility to separately map OCS and SBS from S2, in relatively open oak–pine seasonally dry forests, potentially supporting post-fire management planning.