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<p>This study utilizes Sentinel-2, Sentinel-5 Precursor (Sentinel-5P), European Centre for Medium-Range Weather Forecast’s Atmospheric Reanalysis version 5 (ERA5), Global Human Settlement Layer, Open Street Map and Shutter Radar Topographic Mission for vegetation analysis, atmospheric pollutants, fire risk assessment and recovery dynamics in the world’s largest Chilgoza pine (<i>Pinus gerardiana</i>) forests using geospatial techniques in the Koh-e-Suleiman region, Pakistan from 2020 to 2024. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to estimate emissions’ dispersion. In contrast, the Autoregressive Integrated Moving Average model was used to predict future vegetation and pollution trends. The 2022 wildfire significantly impacted around 34 km<sup>2</sup> of forest land. The Normalized Difference Vegetation Index and differenced Normalized Burn Ratio were used to assess vegetation loss, both showed significant deterioration followed by slow regeneration in succeeding years. Sentinel-5P data indicated significant increases in NO<sub>2</sub>, CO, and Ultraviolet Aerosol Index (UV-AI) during the fire period, with UV-AI showing the strongest association with fire. According to the HYSPLIT model, more than 90% of fire-related emissions moved northward and northeastward, affecting areas outside of the fire-affected zone. The ERA5-based meteorological analysis revealed mean temperatures over 25 °C, total precipitation below 10 mm, and Standardized Precipitation Index values around − 0.20, indicating severe drought conditions in May 2022. This study emphasizes the importance of integrating Remote Sensing and Geographic Information System-based multi-criteria analysis for informed decision-making in forest fire management and ecological restoration planning.</p>

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Fire risk assessment and post-fire evaluation of the world’s largest Chilgoza pine forests using geospatial techniques in the Koh-e-Suleiman region, Pakistan

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Farzana Kiran,
  • Muhammad Farooq Iqbal

摘要

This study utilizes Sentinel-2, Sentinel-5 Precursor (Sentinel-5P), European Centre for Medium-Range Weather Forecast’s Atmospheric Reanalysis version 5 (ERA5), Global Human Settlement Layer, Open Street Map and Shutter Radar Topographic Mission for vegetation analysis, atmospheric pollutants, fire risk assessment and recovery dynamics in the world’s largest Chilgoza pine (Pinus gerardiana) forests using geospatial techniques in the Koh-e-Suleiman region, Pakistan from 2020 to 2024. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to estimate emissions’ dispersion. In contrast, the Autoregressive Integrated Moving Average model was used to predict future vegetation and pollution trends. The 2022 wildfire significantly impacted around 34 km2 of forest land. The Normalized Difference Vegetation Index and differenced Normalized Burn Ratio were used to assess vegetation loss, both showed significant deterioration followed by slow regeneration in succeeding years. Sentinel-5P data indicated significant increases in NO2, CO, and Ultraviolet Aerosol Index (UV-AI) during the fire period, with UV-AI showing the strongest association with fire. According to the HYSPLIT model, more than 90% of fire-related emissions moved northward and northeastward, affecting areas outside of the fire-affected zone. The ERA5-based meteorological analysis revealed mean temperatures over 25 °C, total precipitation below 10 mm, and Standardized Precipitation Index values around − 0.20, indicating severe drought conditions in May 2022. This study emphasizes the importance of integrating Remote Sensing and Geographic Information System-based multi-criteria analysis for informed decision-making in forest fire management and ecological restoration planning.