Bayesian spatio-temporal modeling of the Brazilian fire spots between 2011 and 2022

Abstract

Wildfires are among the most common natural disasters in many world regions and actively impact life quality. These events have become frequent due to climate change, other local policies, and human behavior. Fire spots are areas where the temperature is significantly higher than in the surrounding areas and are often used to identify wildfires. This study considers the historical data with the geographical locations of all the "fire spots" detected by the reference satellites covering the Brazilian territory between January 2011 and December 2022, comprising more than 2.2 million fire spots. This data was modeled with a spatio-temporal generalized linear mixed model for areal unit data, whose inferences about its parameters are made in a Bayesian framework and use meteorological variables (precipitation, air temperature, humidity, and wind speed) and a human variable (land-use transition and occupation) as covariates. The meteorological variables humidity and air temperature showed the most significant impact on the number of fire spots for each of the six Brazilian biomes.

Keywords: Bayesian modeling; Brazilian wildfires; Spatio-temporal modeling; Wildfires risk factors.

Figure 1

Detailed flowchart of the methodology used in this paper, including the data collection, data treatment, data organization, data visualization, and spatial-temporal modeling. Blue arrows are related to the steps and operations from data collection to data treatment, organization, and visualization. Red arrows are associated with the spatio-temporal modeling.

Figure 2

Daily number of fire spots in the Brazilian territory between January 1, 2011, and December 31, 2022.

Figure 3

Daily number of fire spots between January 1, 2011, and December 31, 2022, for each of the six biomes: Amazônia, Cerrado, Atlantic forest (Mata Atlântica), Caatinga, Pampa, and Pantanal, respectively.

Figure 4

Heat map with the total number of fire spots per year for each of the 5570 Brazilian municipalities. The lines represent the borders of the Brazilian states. The plots were generated with the R software.

Figure 5

Heat map with the monthly average number of fire spots for each of the 5570 municipalities between 2011 and 2022. The lines represent the borders of the Brazilian states. The plots were generated with the R software.

Figure 6

Observed and fitted average number of fire spots, per model. Observed values are represented by black dots, fitted values are represented by the solid blue line, and 95% CI are represented by dashed red lines. The plots were generated with the R software.

Figure 7

Heat maps with the sign of the incremental slope parameter ${\delta }_i$ relative to the i-th Brazilian municipality by biome, being null if zero is contained within its 95% credible interval. The plots were generated with the R software. The limits of the municipalities belonging to the Mata Atlântica biome were removed to allow better visualization, given their small sizes.

Figure 8

Heat maps with the spatial Pearson residuals for Brazilian municipalities, per model. The plots were generated with the R software. The limits of the municipalities belonging to the Mata Atlântica biome were removed to allow better visualization, given their small sizes.

Figure 9

Relative frequencies of observed data and simulated data from the posterior predictive distribution, for all six models. The plots were generated with the R software.