Non-parametric spatiotemporal trends in fire: An approach to identify fire regimes variations and predict seasonal effects of fire in Iran

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0319993 - e0319993

Published: April 4, 2025

Analyzing wildfire complexity provides valuable insights into fire regimes and occurrence patterns within landscapes, enabling targeted land management efforts for sensitive vulnerable areas. Fire density is a key component of regimes. In recent years, Iran has experienced significant changes in activity. This study aims to assess trends the probability during summer autumn using active data. Seasonal point (per km 2 ) from 2001 2023 was calculated kernel function. The Mann-Kendall (MK) test identified areas with (at 90% confidence level) prediction analysis. Environmental variables points were entered MaxEnt model predict risk autumn. included average temperature, human modification terrestrial systems, annual precipitation, precipitation driest month, elevation, use/land cover (LULC), surface temperature (LST), soil organic carbon (SOC), wind exposure index (WEI). Spatial variations analyzed gap analysis Kappa index. Influence zone zones impacted by increasing landscape. Results showed that covered 326,739.56 102,668.85 There minimal overlap between decreasing across seasons, indicating wildfires disproportionately affect natural agricultural Iran. 15 fire-prone 3 autumn, portion located Zagros Mountain forest steppes. model, based on area under curve (AUC) metric, successfully high-risk both seasons. Jackknife indicated SOC crucial indicators activities available fuel Predictions diverging summer, high all regions except deserts Hyrcanian forests, while mixed forests are also classified as zones. These findings can help managers identify influence understand uses vegetation types associated wildfires, more informed effective decisions spatial extent distribution trends.

Language: Английский

Spatiotemporal Analysis of thermal islands in a Semi-Arid City: A Case Study of Kermanshah, Iran Using Machine Learning and Remote Sensing

Environmental Challenges, Journal Year: 2025, Volume and Issue: unknown, P. 101174 - 101174

Published: May 1, 2025

Language: Английский