Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): D. Vega-Nieva; J. Briseño-Reyes; M. Nava-Miranda; E. Calleros-Flores; P. López-Serrano; J. Corral-Rivas; E. Montiel-Antuna; M. Cruz-López; M. Cuahutle; R. Ressl; E. Alvarado-Celestino; A. González-Cabán; E. Jiménez; J. Álvarez-González; A. Ruiz-González; R. Burgan; H. Preisler
    Date: 2018
    Source: Forests. 9(4): 190
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Southwest Research Station
    PDF: Download Publication  (11.0 MB)


    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.

    Publication Notes

    • You may send email to to request a hard copy of this publication.
    • (Please specify exactly which publication you are requesting and your mailing address.)
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.


    Vega-Nieva, D.; Briseño-Reyes, J.; Nava-Miranda, M.; Calleros-Flores, E.; López-Serrano, P.; Corral-Rivas, J.; Montiel-Antuna, E.; Cruz-López, M.; Cuahutle, M.; Ressl, R.; Alvarado-Celestino, E.; González-Cabán, A.; Jiménez, E.; Álvarez-González, J.; Ruiz-González, A.; Burgan, R.; Preisler, H. 2018. Developing models to predict the number of fire hotspots from an accumulated fuel dryness index by vegetation type and region in Mexico. Forests. 9(4): 190.


    Google Scholar


    MODIS, fire hotspots, fire occurrence risk, fire danger systems

    Related Search

    XML: View XML
Show More
Show Fewer
Jump to Top of Page