インドのタミルナードゥ州の森林火災の生態学的リスク評価と管理:戦略的資源配分と火災緩和のためのマックスエンットモデルベースのアプローチ
Gowhar Meraj1,2, Shizuka Hashimoto2, Rajarshi Dasgupta3
1Department of Biology, Chemistry and Environmental Sciences, College of Arts and Sciences, American University of Sharjah, Sharjah, UAE.
Risk analysis : an official publication of the Society for Risk Analysis
|September 4, 2025
まとめ
タミルナードゥ州の森林火災リスクマッピングでは 農地や牧草地の近くにある高リスク地域が 特定されました これにより,森林火災を標的として計画し,効率的な資源配分が可能になります.
科学分野:
- 生態モデリング
- 林業科学
- 気候変動の影響に関する研究
背景:
- 森林火災は自然界の生態系の一部ですが 人為活動と気候変動により 森林火災の頻度と強度が増加しています
- タミルナードゥの森は 人間の圧力や 気候の変化により 火災のリスクが高まっています
- 効果的な火災管理は,生態学的および社会経済的安定に不可欠です.
研究 の 目的:
- タミルナードゥ州全域の森林火災発生確率マップを作成する.
- タミルナードゥ州森林局 (TNFD) に火災対策の戦略を指導する.
- 森林火災の危険性の主要な要因を特定する.
主な方法:
- 訓練のために2020年の火災データと検証のために2021-2022年のデータを使用した存在のみの最大エントロピー (MaxEnt) モデルを使用しました.
- 1kmの解像度で19の地形,気候,人類による予測を組み込みました.
- 森林域の低,中,高リスクゾーンに火災の可能性を分類した.
主要な成果:
- MaxEntモデルは優れた予測精度を示した (AUC=0. 92,テスト精度=0. 88).
- 農耕地 (32.8%) と牧草地 (25.8%) に近い場所が火災発生の最も重要な予測要因でした.
- 6.4%の範囲を構成する高リスクゾーンは,最近の点火の54%を占め,管理の高度なレバレッジを示しています.
結論:
- 森林火災管理と資源配分のための重要な地域を効果的に特定します.
- このアプローチは,限られたデータを持つ熱帯地域で,火災撲滅の努力を強化するための移行可能なモデルを提供します.
- 将来の研究は,気象と燃料の湿度データを統合し,早期警告システムを改善する必要があります.
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