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使用dNBR和机器学习模型进行森林火灾概率划分:印度奥迪沙邦西米利帕尔生物圈保护区 (SBR) 的案例研究

Rajkumar Guria¹, Manoranjan Mishra², Samiksha Mohanta²

¹Department of Geography, Fakir Mohan University, Vyasa Vihar, Nuapadhi, Balasore, 756089, Odisha, India. rkguria.007@gmail.com.

Environmental science and pollution research international

|January 30, 2025

概括

在西米利帕尔生物圈保护区的40.85%地区,森林火灾风险很高,2021年是峰值年. 机器学习模型将土地利用和植被指数确定为有针对性的消防管理的关键因素.

科学领域:

环境科学,专注于森林生态系统和火灾动态.
遥感和地理空间分析用于环境监测.
在生态风险评估中的机器学习应用.

背景情况:

森林火灾对生物多样性和环境平衡构成重大威胁.
有效的森林火灾概率 (FFP) 识别对于缓解策略至关重要.
西米利帕尔生物圈保护区 (SBR) 面临着反复出现的火灾挑战.

研究的目的:

评估2012年至2023年SBR森林火灾趋势和易感性.
为了比较四个机器学习模型在预测森林火灾概率方面的表现.
确定影响森林火灾易感性的关键条件因素.

主要方法:

使用了四种机器学习模型:XGBTree,AdaBag,随机森林 (RF) 和渐变增强机器 (GBM).
使用三角洲正常化燃烧比率 (dNBR) 指数创建了森林火灾库存,并纳入了19个条件因素.
使用ROC-AUC,MAE,MSE和RMSE指标生成FFP地图和评估模型性能;用于变量重要性的频率比 (FR) 模型.

主要成果:

大约40.85%的SBR被归类为高至非常高的森林火灾易感性.
随机森林模型表现出最高的准确性,AUC为0.965.
土地使用/土地覆盖 (LULC),NDVI和NDMI被确定为对火灾易感性最有影响的因素,其中2021年是火灾峰值年.

结论:

机器学习模型,特别是射频,有效地预测SBR中的森林火灾概率.
生成的精确的FFP地图可以指导有针对性的干预,并加强消防管理策略.
调查结果支持政策制定者和环保主义者通过数据驱动的洞察力减轻森林火灾的影响.

关键词:

生物多样性生物多样性这是一个DNBR.森林火灾的易感性森林火灾的易感性机器学习机器学习西米利帕尔生物圈保护区

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