Bayesian spatio-temporal modeling of the Brazilian fire spots between 2011 and 2022.
Sci Rep
; 14(1): 21616, 2024 Sep 16.
Article
en En
| MEDLINE
| ID: mdl-39285167
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
País/Región como asunto:
America do sul
/
Brasil
Idioma:
En
Revista:
Sci Rep
Año:
2024
Tipo del documento:
Article
País de afiliación:
Brasil
Pais de publicación:
Reino Unido