基于随机森林模型的地表细小死可燃物载量估算  

Estimation of fine dead fuel load based on Random Forest Model

作　　者：刘世好 胡稳 杨旸谷 阳胜男 徐云 张家阳 LIU Shihao;HU Wen;YANG Yanggu;YANG Shengnan;XU Yun;ZHANG Jiayang(Central South Academy of Inventory and Planning,National Forestry and Grassland Administration,Changsha 410014,Hunan,China;College of Environment and Ecology,Hunan Agricultural University,Changsha 410125,Hunan,China;Changsha Central South Forestry Investigation,Planning and Design Co.Ltd.,Changsha 410014,Hunan,China)

机构地区：[1]国家林业和草原局中南调查规划院,湖南长沙410014 [2]湖南农业大学环境与生态学院,湖南长沙410125 [3]长沙中南林业调查规划设计有限公司,湖南长沙410014

出　　处：《浙江农林大学学报》2025年第2期312-320,共9页Journal of Zhejiang A&F University

基　　金：湖南省科技厅重点研发项目(2022NK2045)。

摘　　要：【目的】研究地表细小死可燃物载量估算方法及空间分布。【方法】基于遥感数据、野外样地调查结果,通过随机森林模型,估算湖南省安化县172个乔木林和竹林标准地的地表细小死可燃物载量,并分析各因子在估算过程中的重要性。【结果】(1)随机森林模型对地表细小死可燃物载量的估算精度较高,在训练集和验证集上的决定系数(R2)分别为0.930和0.724,均方根误差分别为0.2623和0.4166 t·hm^(-2),均通过了0.01水平的置信度检验。(2)估算过程中各因子的重要性存在显著差异,重要性指数排名从大到小依次为植被类型因子(39.95%)、林分因子(7.23%)、地形因子(3.91%)、光谱特征指数因子(3.82%)。(3)安化县地表细小死可燃物载量为1.18~6.19 t·hm^(-2),高可燃物载量的区域集中于江南镇、田庄乡、马路镇、烟溪镇、乐安镇、梅城镇和滔溪镇。【结论】随机森林模型可较好地应用于地表细小死可燃物载量估算,可为区域森林管理和保护,以及减少林火风险提供可靠方法。[Objective]The objective is to study the estimation method and spatial distribution of fine dead fuel load on the ground surface.[Method]Based on remote sensing data and field survey results,a Random Forest Model was employed to estimate the land surface fine dead fuel load in 172 standard plots of arboreal forests and bamboo forests in Anhua County,Hunan Province,and the importance of each factor in the estimation process was analyzed.[Result](1)The Random Forest Model had high accuracy in estimating the surface fine dead fuel load.The determination coefficients(R²)on the training set and validation set were 0.930 and 0.724 respectively,and the root mean square errors were 0.2623 and 0.4166 t·hm^(-2) respectively,both of which passed the confidence test at 0.01 level.(2)There were significant differences in the importance of each factor in the estimation process,and the importance index ranking from the largest to the smallest was vegetation type factor(39.95%),stand factor(7.23%),terrain factor(3.91%)and spectral feature index factor(3.82%).(3)The surface fine dead fuel load in Anhua County was 1.18-6.19 t·hm^(-2),and the areas with high fuel load were mainly concentrated in Jiangnan Town,Tianzhuang Township,Malu Town,Yanxi Town,Le’an Town,Meicheng Town,and Taoxi Town.[Conclusion]The Random Forest Model can be well applied to estimate the surface fine dead fuel load,and can provide a reliable method for regional forest management and conservation,as well as for forest fire risk mitigation.

关 键 词：随机森林模型 地表细小死可燃物 可燃物载量 林分因子 Sentinel-2A 森林火灾 

分 类 号：S771.8[农业科学—森林工程] S762.2[农业科学—林学]