RT Journal Article
T1 Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico
A1 Vega Nieva, Daniel José
A1 Nava Miranda, María Guadalupe
A1 Calleros Flores, Eric
A1 López Serrano, Pablito Marcelo
A1 Corral Rivas, José Javier
A1 Cruz López, María Isabel
A1 Cuahutle, M.
A1 Ressl, Rainer
A1 Alvarado Celestino, Ernesto
A1 González Cabán, Armando
A1 Jiménez, Enrique
A1 Álvarez González, Juan Gabriel
A1 Ruiz González, Ana Daría
A1 Burgan, R. E.
A1 Preisler, Haiganoush K.
A1 Briseño Reyes, Jaime
A1 Montiel Antuna, Eusebio
K1 MODIS
K1 Fire hotspots
K1 Fire occurrence risk
K1 Fire danger systems
AB 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.
PB MDPI
YR 2018
FD 2018
LK http://hdl.handle.net/10347/22780
UL http://hdl.handle.net/10347/22780
LA eng
NO Vega-Nieva, D.J.; Briseño-Reyes, J.; Nava-Miranda, M.G.; Calleros-Flores, E.; López-Serrano, P.M.; Corral-Rivas, J.J.; Montiel-Antuna, E.; Cruz-López, M.I.; Cuahutle, M.; Ressl, R.; Alvarado-Celestino, E.; González-Cabán, A.; Jiménez, E.; Álvarez-González, J.G.; Ruiz-González, A.D.; Burgan, R.E.; Preisler, H.K. Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico. Forests 2018, 9, 190. https://doi.org/10.3390/f9040190
NO Funding for this work was provided by CONAFOR/CONACYT Project C0-3-2014 “Development of a Forest Fire Danger Prediction System for Mexico”.
DS Minerva
RD 24 abr 2026