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Monjarás-Vega NA, Briones-Herrera CI, Vega-Nieva DJ, et al. Predicting forest fire kernel density at multiple scales with geographically weighted regression in Mexico. Sci Total Environ. 2020;718:137313doi: 10.1016/j.scitotenv.2020.137313.
Monjarás-Vega, N. A., Briones-Herrera, C. I., Vega-Nieva, D. J., Calleros-Flores, E., Corral-Rivas, J. J., López-Serrano, P. M., Pompa-García, M., Rodríguez-Trejo, D. A., Carrillo-Parra, A., González-Cabán, A., Alvarado-Celestino, E., & Jolly, W. M. (2020). Predicting forest fire kernel density at multiple scales with geographically weighted regression in Mexico. The Science of the total environment, 718137313. https://doi.org/10.1016/j.scitotenv.2020.137313
Monjarás-Vega, Norma Angélica, et al. "Predicting forest fire kernel density at multiple scales with geographically weighted regression in Mexico." The Science of the total environment vol. 718 (2020): 137313. doi: https://doi.org/10.1016/j.scitotenv.2020.137313
Monjarás-Vega NA, Briones-Herrera CI, Vega-Nieva DJ, Calleros-Flores E, Corral-Rivas JJ, López-Serrano PM, Pompa-García M, Rodríguez-Trejo DA, Carrillo-Parra A, González-Cabán A, Alvarado-Celestino E, Jolly WM. Predicting forest fire kernel density at multiple scales with geographically weighted regression in Mexico. Sci Total Environ. 2020 May 20;718:137313. doi: 10.1016/j.scitotenv.2020.137313. Epub 2020 Feb 15. PMID: 32088482.
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Sci Total Environ. 2020 May 20;718:137313. doi: 10.1016/j.scitotenv.2020.137313. Epub 2020 Feb 15.

Predicting forest fire kernel density at multiple scales with geographically weighted regression in Mexico.

The Science of the total environment

Norma Angélica Monjarás-Vega, Carlos Ivan Briones-Herrera, Daniel José Vega-Nieva, Eric Calleros-Flores, José Javier Corral-Rivas, Pablito Marcelo López-Serrano, Marín Pompa-García, Dante Arturo Rodríguez-Trejo, Artemio Carrillo-Parra, Armando González-Cabán, Ernesto Alvarado-Celestino, William Matthew Jolly

Affiliations

  1. Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Río Papaloapan y Blvd, Durango S/N Col. Valle del Sur, 34120 Durango, Mexico.
  2. Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Río Papaloapan y Blvd, Durango S/N Col. Valle del Sur, 34120 Durango, Mexico. Electronic address: [email protected].
  3. Instituto de Silvicultura e Industria de la madera, Universidad Juárez del Estado de Durango, Boulevard del Guadiana 501, Ciudad Universitaria, Torre de Investigación, 34120 Durango, Mexico.
  4. Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Río Papaloapan y Blvd, Durango S/N Col. Valle del Sur, 34120 Durango, Mexico. Electronic address: [email protected].
  5. Instituto de Silvicultura e Industria de la madera, Universidad Juárez del Estado de Durango, Boulevard del Guadiana 501, Ciudad Universitaria, Torre de Investigación, 34120 Durango, Mexico. Electronic address: [email protected].
  6. Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Río Papaloapan y Blvd, Durango S/N Col. Valle del Sur, 34120 Durango, Mexico. Electronic address: [email protected].
  7. División de Ciencias Forestales, Universidad Autónoma Chapingo, Km 38.5 carretera México - Texcoco, 56230 Chapingo, Estado de México, Mexico.
  8. Pacific Southwest Research Station, US Department of Agriculture Forest Service, (retired), 4955 Canyon Crest Drive, Riverside, CA 92507, USA.
  9. School of Environmental and Forest Sciences, University of Washington, Mailbox 352100, University of Washington, Seattle, WA 98195, USA. Electronic address: [email protected].
  10. USDA Forest Service, Missoula Fire Sciences Laboratory, Missoula, MT 59808, USA. Electronic address: [email protected].

PMID: 32088482 DOI: 10.1016/j.scitotenv.2020.137313

Abstract

Identifying the relative importance of human and environmental drivers on fire occurrence in different regions and scales is critical for a sound fire management. Nevertheless, studies analyzing fire occurrence spatial patterns at multiple scales, covering the regional to national levels at multiple spatial resolutions, both in the fire occurrence drivers and in fire density, are very scarce. Furthermore, there is a scarcity of studies that analyze the spatial stationarity in the relationships of fire occurrence and its drivers at multiple scales. The current study aimed at predicting the spatial patterns of fire occurrence at regional and national levels in Mexico, utilizing geographically weighted regression (GWR) to predict fire density, calculated with two different approaches -regular grid density and kernel density - at spatial resolutions from 5 to 50 km, both in the dependent and in the independent human and environmental candidate variables. A better performance of GWR, both in goodness of fit and residual correlation reduction, was observed for prediction of kernel density as opposed to regular grid density. Our study is, to our best knowledge, the first study utilizing GWR to predict fire kernel density, and the first study to utilize GWR considering multiple scales, both in the dependent and independent variables. GWR models goodness of fit increased with fire kernel density search radius (bandwidths), but saturation in predictive capacity was apparent at 15-20 km for most regions. This suggests that this scale has a good potential for operational use in fire prevention and suppression decision-making as a compromise between predictive capability and spatial detail in fire occurrence predictions. This result might be a consequence of the specific spatial patterns of fire occurrence in Mexico and should be analyzed in future studies replicating this methodology elsewhere.

Copyright © 2020 Elsevier B.V. All rights reserved.

Keywords: Biomass; Fire occurrence drivers; GAM; GWR; Human factors; Kernel bandwidth

Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this pa

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