Spatiotemporal pattern analysis of wildfire occurrence in the Golestan province, northern Iran

Wildfires, though a natural phenomenon, can have devastating impacts on human lives, property, and ecosystems, making it crucial to understand their spatiotemporal patterns to effectively manage and mitigate their risks. For that, this study examined the spatial and temporal patterns of wildfires in the Golestan Province, northern Iran, between 2015 and 2022. The study utilized historical fire data from forest services in the province, encompassing 2154 fire points. Spatial pattern analysis was conducted using the K, L, and G functions from the Spstat, an R package, while spatiotemporal pattern analysis was performed using the Least Squares linear regression method and the Space Time Pattern Mining toolbox. The results indicated a clustered pattern of fire occurrence, with a higher prevalence between 300-900 m elevation and on moderate slopes (10-30%). The north, northwest, and northeast aspects were also associated with a higher concentration of wildfires. Summer months (June-August) exhibited significantly higher incidences of fires compared to spring (April-May), with a peak in 2017 followed by a substantial decline in 2022. The study provides valuable insights for forest managers, law enforcement, and fire agencies to develop targeted strategies for mitigating wildfire impacts and promoting sustainable forest management practices.

Spatiotemporal pattern analysis of wildfire occurrence in the Golestan province, northern Iran Keywords:

Golestan province; Spatiotemporal analysis; Wildfires , Risk