Temporal patterns of active fire density and its relationship with a satellite fuel greenness index by vegetation type and region in Mexico during 2003–2014

Abstract

Background: Understanding the temporal patterns of fire occurrence and their relationships with fuel dryness is key to sound fire management, especially under increasing global warming. At present, no system for prediction of fire occurrence risk based on fuel dryness conditions is available in Mexico. As part of an ongoing national-scale project, we developed an operational fire risk mapping tool based on satellite and weather information. Results: We demonstrated how differing monthly temporal trends in a fuel greenness index, dead ratio (DR), and fire density (FDI) can be clearly differentiated by vegetation type and region for the whole country, using MODIS satellite observations for the period 2003 to 2014. We tested linear and non-linear models, including temporal autocorrelation terms, for prediction of FDI from DR for a total of 28 combinations of vegetation types and regions. In addition, we developed seasonal autoregressive integrated moving average (ARIMA) models for forecasting DR values based on the last observed values. Most ARIMA models showed values of the adjusted coefficient of determination (R 2 adj) above 0.7 to 0.8, suggesting potential to forecast fuel dryness and fire occurrence risk conditions. The best fitted models explained more than 70% of the observed FDI variation in the relation between monthly DR and fire density. Conclusion: These results suggest that there is potential for the DR index to be incorporated in future fire risk operational tools. However, some vegetation types and regions show lower correlations between DR and observed fire density, suggesting that other variables, such as distance and timing of agricultural burn, deserve attention in future studies.

Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico

“…The temporal evolution of the observed and predicted total monthly burned area with an aggregation distance of 1125 m in the period of study is shown in Figure 3. The highest values of burned area were observed in the months of April and May, particularly in the years of 2013, 2012 and 2017, which have been documented to be dry years in Mexico, in addition to human ignition pattern factors affecting fire occurrence [51,52]. Examples of MODIS MCD64A1 burned area and aggregated active fire perimeters for different aggregation distances are shown in Figure 4.…”

“…Some examples of potential limitations of the aggregation algorithm from active fire data are shown Figure 6. Figure 6a,b illustrate the need for avoiding potential overestimating artifacts, obtained in some areas with an aggregation distance of 1500 m (Figure 6b), compared with the potential ability of the aggregation distance of 1125 m (Figure 6a) to differentiate small fire events from each other, particularly in areas where small burns from agricultural expansion, frequent in Mexico (e.g., [52]), dominate the landscape. An example of potential burned area omissions, given the absence of active fire detections in a cloudy tropical forest area, is shown in Figure 6c.…”

“…Available information from this ongoing research project currently includes field calibrated Sentinel fire perimeters for some of the largest and most intense fires of 2019 in the state of Durango, NW Mexico [63], (location shown in Figure 1), including a very large fire of 29,329 ha (Figure 7a) and two twin fires of 12,142 and 2631 ha (Figure 7b). from each other, particularly in areas where small burns from agricultural expansion, frequent in Mexico (e.g., [52]), dominate the landscape. An example of potential burned area omissions, given the absence of active fire detections in a cloudy tropical forest area, is shown in Figure 6c.…”

“…In our study, larger distances (i.e., 1500 m) for MODIS and VIIRS active fires aggregation resulted in a global overestimation of fire perimeters (Table 1), and sometimes in the creation of artefacts by merging two spatially adjacent fire events that occurred in the same month (Figure 4d,h and Figure 6b). While some of these artefacts might be eliminated by including a narrower temporal constraint [9,72], overestimations might still be possible by utilizing aggregation distances that are too large, particularly in areas where simultaneous small agricultural burns are occurring [33], which are common in the central and southern part of the country [51,52]. The use of larger aggregation distances in grassand shrub-dominated areas, as illustrated in Figure 6d, should nevertheless be investigated in future studies to improve the perimeter delineation by accounting for fuel-specific variations in fire rate of spread [25,29,73].…”

“…The period of study was from January 2012 to June 2018. While fire season in Mexico is mainly concentrated in the months of March to June [51,52], burned area and active fires from all months in the period of study were considered. Monthly burned area perimeters from the study period were downloaded from the FTP server of the University of Maryland.…”