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

D Vega-Nieva,R Ressl,E Calleros-Flores,H Preisler,M Nava-Miranda,E Montiel-Antuna,J Briseño-Reyes,J Corral-Rivas,P López-Serrano,A González-Cabán,J Álvarez-González,M Cruz-López,E Alvarado-Celestino,R Burgan,E Jiménez,A Ruiz-González,M Cuahutle

doi:10.3390/f9040190

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 R2 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.

Highlights

Vegetation types were reclassified into the following categories: temperate forest (FOR), dry tropical forest (DTROPF), seasonally dry tropical forest (SDTROPF), seasonally wet tropical forest (SWTROPF), wet tropical forest (WTROPF), wetlands (WET), desert shrubby vegetation (DSHR), natural pastureland (NPAS), and agricultural croplands (AGR) (Figure 1)
The marked variation in observed Fuel Dryness Index (FDI) by vegetation type and region agrees with that reported from the works with the Fire Potential Index (FPI) index, which has a very similar structure to the FDI used here (e.g., [31]), and to previous works in Mexico that have reported distinct Normalized Difference Vegetation Index (NDVI) temporal cycles for different vegetation types (e.g., [50])
The work presents the first effort towards developing an operational model for the prediction of the expected number of fire hotspots expressed as a fire hotspot density index, using remotely sensed weather and NDVI information, at a national level in Mexico

Summary

Introduction

The quantification of the influence of varying fuel dryness on temporal fire occurrence risk is critical for improving decision-making in strategic fire management planning (e.g., [1,2,3]), especially under growing conditions of vegetation stress associated with climate change, which can alter the length and severity of the fire seasons (e.g., [4,5,6,7]).Based on weather information, several operational fire danger systems have been developed to estimate fuel dryness and associated fire occurrence risk (e.g., [8,9,10,11,12,13]). The FPI fuel dryness index has been operationally used for fire danger monitoring and occurrence risk prediction in the United States of America (USA) [28,33,34,35], Indonesia [44], and on the European continent (e.g., [29,30,31,45]), including studies of regional application in northern Spain [46,47,48]. Several of these works have highlighted the need to understand how the same values of a fuel dryness index such as FPI result in different patterns of fire occurrence under different bioclimatic regions and vegetation types

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