Non-parametric spatiotemporal trends in fire: An approach to identify fire regimes variations and predict seasonal effects of fire in Iran - Peyman Karami; Sajad Tavakoli (PLoS ONE, 2025)
Analyzing wildfire complexity provides valuable insights into fire regimes and occurrence patterns within landscapes, enabling targeted land management efforts for sensitive and vulnerable areas. Fire density is a key component of wildfire regimes. In recent years, Iran has experienced significant changes in wildfire activity. This study aims to assess trends in fire density and the probability of wildfire occurrence during summer and autumn using active fire data. Seasonal fire point density (per km2) from 2001 to 2023 was calculated using a kernel function. The Mann-Kendall (MK) test identified areas with significant fire density trends (at the 90% confidence level) for prediction analysis. Environmental variables and points with significant trends were entered into a MaxEnt model to predict fire risk in summer and autumn. Environmental variables included average temperature, human modification of terrestrial systems, annual precipitation, precipitation of the driest month, elevation, land use/land cover (LULC), land surface temperature (LST), soil organic carbon (SOC), and wind exposure index (WEI). Spatial variations in significant fire density trends for summer and autumn were analyzed using gap analysis and the Kappa index. Influence zone analysis identified zones impacted by increasing wildfire trends within the landscape. Results showed that areas with significant increasing fire density trends covered 326,739.56 km2 in summer and 102,668.85 km2 in autumn. There was minimal overlap between increasing and decreasing fire density trends across seasons, indicating wildfires disproportionately affect natural and agricultural areas in Iran. Influence zone analysis identified 15 fire-prone zones in summer and 3 in autumn, with a significant portion located in the Zagros Mountain forest steppes. The MaxEnt model, based on the area under the curve (AUC) metric, successfully identified high-risk wildfire areas in both seasons. Jackknife analysis indicated that human modification and SOC were crucial indicators of human activities and available fuel for wildfires in both seasons. Predictions showed diverging wildfire risk patterns in summer and autumn. In summer, wildfire risk is high across all regions except deserts and Hyrcanian forests, while in autumn, Hyrcanian mixed forests are also classified as high-risk zones. These findings can help land managers identify influence zones and understand the land uses and vegetation types associated with wildfires, enabling more informed and effective management decisions based on the spatial extent and distribution of fire trends.