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<p>Forest fires globally, influenced by numerous drivers, resulting in substantial ecological degradation, economic instability, loss of livelihood, and loss of life. Impact of forest fire on the spatial variations of soil nutrients and socio-ecology of communities in the ecologically sensitive region like Central Himalaya are rare. For proper management, integration of knowledge regarding regional problems due to fire, local traditional practices to prevent fire, and present technologies at global level are necessary. This research takes a novel approach to develop comprehensive fire management system by integrating methods including Geographically Weighted Binary Logistic Regression to map fire probability, laboratory analysis of soil samples to assess the impact on soil nutrients, and qualitative surveys to understand the socio-ecological impacts. Parallelly, traditional knowledge on fire prevention is also gathered. By combining these elements, the study aims to create a well-rounded and effective fire management strategy. High fire-risk zones, including Rajaji National Park, Jim Corbett National Park, and Nainital, exhibit a fire probability exceeding 90%, primarily driven by elevation, aspect, temperature, and human proximity. The GWBLR model emerge as an excellent technique of fire risk assessment with 83.1% accuracy. Soil nutrient analysis reveals significant degradation in burned areas, with organic carbon, nitrogen, and potassium levels reduced by over 50%. Agricultural productivity loss, reduction in non-timber forest products, livelihood loss, and outmigration were the major challenges faced by the forest-dependent populous. The study emphasizes integrating geospatial models with traditional knowledge for comprehensive fire management. Moreover, using pine needles for bio-briquettes to reduce fuel loads and enhance early warning systems can mitigate fire risks and the ecological consequences.</p>

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Combining spatial and elementary analysis with qualitative approaches: a novel approach of forest fire risk detection, mitigation, and impact analysis

  • 期刊论文

  • 发表日期：

    2025年7月5日

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  • Surajit Banerjee,
  • Vishwambhar Prasad Sati

摘要

Forest fires globally, influenced by numerous drivers, resulting in substantial ecological degradation, economic instability, loss of livelihood, and loss of life. Impact of forest fire on the spatial variations of soil nutrients and socio-ecology of communities in the ecologically sensitive region like Central Himalaya are rare. For proper management, integration of knowledge regarding regional problems due to fire, local traditional practices to prevent fire, and present technologies at global level are necessary. This research takes a novel approach to develop comprehensive fire management system by integrating methods including Geographically Weighted Binary Logistic Regression to map fire probability, laboratory analysis of soil samples to assess the impact on soil nutrients, and qualitative surveys to understand the socio-ecological impacts. Parallelly, traditional knowledge on fire prevention is also gathered. By combining these elements, the study aims to create a well-rounded and effective fire management strategy. High fire-risk zones, including Rajaji National Park, Jim Corbett National Park, and Nainital, exhibit a fire probability exceeding 90%, primarily driven by elevation, aspect, temperature, and human proximity. The GWBLR model emerge as an excellent technique of fire risk assessment with 83.1% accuracy. Soil nutrient analysis reveals significant degradation in burned areas, with organic carbon, nitrogen, and potassium levels reduced by over 50%. Agricultural productivity loss, reduction in non-timber forest products, livelihood loss, and outmigration were the major challenges faced by the forest-dependent populous. The study emphasizes integrating geospatial models with traditional knowledge for comprehensive fire management. Moreover, using pine needles for bio-briquettes to reduce fuel loads and enhance early warning systems can mitigate fire risks and the ecological consequences.