Trends in Global and Mexico Research in Wildfires: A Bibliometric Perspective

Zamudio, Daniel Alejandro Cadena-; Guerra, Betsabé Ruiz; Vázquez, José Luis Arellano; Garnica, José Germán Flores; Avilés, Leslie Carnero; Aguilar, Rocío Toledo; Cantú, David; Bautista, Adriana Antonio; Mayo-Hernández, Juan; Quiroz, David Castillo; Nava, Norma Tolama; Guevara, Roger

doi:10.4236/ojf.2023.132012

Cited by 1 publication

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These uncertainties are more evident when attempting to apply those fire weather indices in other countries that may have different biophysical and social conditions to those where they were developed (e.g., [22,23]). Thus, it is worth stressing that the majority of the analysis of fire occurrence against fire weather indices has been performed in data-abundant countries, e.g., the United States, Canada, Australia, China, and some European countries (e.g., [24]). Comparatively, fewer studies are available for global hotspots of fire activity in countries with great biodiversity, e.g., Mexico (e.g., [25,26]) or Latin American countries (e.g., [27,28]).…”

Section: Introductionmentioning

confidence: 99%

Autoregressive Forecasting of the Number of Forest Fires Using an Accumulated MODIS-Based Fuel Dryness Index

Vega-Nieva,

Briseño-Reyes,

López-Serrano

et al. 2023

Forests

View full text Add to dashboard Cite

There is a need to convert fire danger indices into operational estimates of fire activity to support strategic fire management, particularly under climate change. Few studies have evaluated multiple accumulation times for indices that combine both dead and remotely sensed estimates of live fuel moisture, and relatively few studies have aimed at predicting fire activity from both such fuel moisture estimates and autoregressive terms of previous fires. The current study aimed at developing models to forecast the 10-day number of fires by state in Mexico, from an accumulated Fuel Dryness Index (FDI) and an autoregressive term from the previous 10-day observed number of fires. A period of 50 days of accumulated FDI (FDI50) provided the best results to forecast the 10-day number of fires from each state. The best predictions (R2 > 0.6–0.75) were obtained in the largest states, with higher fire activity, and the lower correlations were found in small or very dry states. Autoregressive models showed good skill (R2 of 0.99–0.81) to forecast FDI50 for the next 10 days based on previous fuel dryness observations. Maps of the expected number of fires showed potential to reproduce fire activity. Fire predictions might be enhanced with gridded weather forecasts in future studies.

show abstract