Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico

Abstract

Understanding the linkage between accumulated fuel dryness and temporal fire occurrence risk is key for improving decision-making in forest fire management, especially under growing conditions of vegetation stress associated with climate change. This study addresses the development of models to predict the number of 10-day observed Moderate-Resolution Imaging Spectroradiometer (MODIS) active fire hotspots-expressed as a Fire Hotspot Density index (FHD)-from an Accumulated Fuel Dryness Index (AcFDI), for 17 main vegetation types and regions in Mexico, for the period 2011-2015. The AcFDI was calculated by applying vegetation-specific thresholds for fire occurrence to a satellite-based fuel dryness index (FDI), which was developed after the structure of the Fire Potential Index (FPI). Linear and non-linear models were tested for the prediction of FHD from FDI and AcFDI. Non-linear quantile regression models gave the best results for predicting FHD using AcFDI, together with auto-regression from previously observed hotspot density values. The predictions of 10-day observed FHD values were reasonably good with R 2 values of 0.5 to 0.7 suggesting the potential to be used as an operational tool for predicting the expected number of fire hotspots by vegetation type and region in Mexico. The presented modeling strategy could be replicated for any fire danger index in any region, based on information from MODIS or other remote sensors.

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

“…The high FDI values observed in the S or C region for Agriculture and Pasture were not surprising, because these territories are characterized by frequent slash-andburn agricultural activities and clearing of forest for expansion of agriculture, which result in frequent fires (e.g., Rodríguez-Trejo and Fulé 2003;Román-Cuesta et al 2004;Román-Cuesta and Martínez 2006;Rodríguez-Trejo et al 2008;Rodríguez-Trejo et al 2011;Carrillo García et al 2012;Ibarra-Montoya and Huerta-Martínez 2016). These high ignition densities in the C region are consistent with those observed in previous studies of active fire density for the period 2011 to 2015 (Vega-Nieva et al 2018), as well as with the fire suppression registers spatial analysis from Pompa-García et al (2018) and Zúñiga-Vásquez et al (2017b) in Mexico.…”

“…For all of the vegetation types and regions studied, the relationship of FDI with monthly DR was better described with nonlinear than with linear models, suggesting that the relationship of DR with fire occurrence is not linearly proportional (e.g., fire occurrence risk increased very rapidly with increasing DR). Different patterns of FDI and DR relationships were observed for different vegetation types and regions, agreeing with observations that point to a variety of fire regimes resulting from combinations of climatology and fuel types in the country (e.g., Rodríguez-Trejo 2008, 2015Morf ín Ríos et al 2007;Morfin Rios et al 2012;Jardel et al 2014;Vega-Nieva et al 2018).…”

“…Active fires data were filtered by CONABIO following the protocol described by Cruz- López (2007). This data filtering included the consideration of specific thresholds for brightness temperature (Cruz-López 2007), and the use of additional masks such as NDVI, vegetation, or stable light masks to minimize false detections, as described by Cruz- López (2007) and Vega-Nieva et al (2018). Monthly NDVI composite images with a spatial resolution of 1 km × 1 km (MODIS product MOD13A3) from the study period were downloaded from http://modis.…”

“…Studies analyzing the relationships of fire occurrence and fuel dryness at a national scale in Mexico are scarce (e.g., Zúñiga-Vásquez et al 2017a;Pompa-García et al 2018;Vega-Nieva et al 2018), limiting our understanding of the effects of changing climatic conditions on fire risk, and preventing development of operational fire risk decision systems. This is in contrast with countries such as USA, Canada, or Brazil that have developed operational fire risk systems based on temporal and spatial quantification of fuel greenness and associated fire risk (e.g., Deeming et al 1977;Van Wagner 1987;Burgan et al 1998;Preisler et al 2004;Preisler et al 2008;Preisler et al 2011;Preisler et al 2015;Setzer and Sismanoglu 2012;Riley et al 2013).…”

“…The main objective of the project is the development of an operational fire-risk mapping system based on satellite and weather information for Mexico (Vega-Nieva et al 2019a;Vega-Nieva et al 2019b). Vega-Nieva et al (2018) focused on analyzing relationships between a satellite-based fuel greenness index, FPI (Burgan et al 1998), and MODIS active fire registers in the period 2011 to 2015. The models developed in that study were limited to observed fuel greenness values, but further work is required for fire risk forecasting.…”