文章摘要
汤明华,罗恒春,刘 娟.基于森林资源清查数据的盈江县主要树种单木胸径生长模型研究[J].林业调查规划,2025,50(3):1-9
基于森林资源清查数据的盈江县主要树种单木胸径生长模型研究
Individual Tree DBH Growth Models for Major Tree Species inYingjiang County Based on Forest Inventory Data
  
DOI：
中文关键词: 单木胸径生长模型  多元线性回归  森林资源清查数据
英文关键词: individual tree DBH growth model  multiple linear regression  forest inventory data
基金项目:云南省林业调查规划院“连清”成果应用专项研究课题;云南省科技厅2023 年第二批科技成果转化专项资金(530000231100001753720).
作者单位
汤明华 云南省林业调查规划院生态分院,云南 昆明 650031 
罗恒春 云南省林业调查规划院生态分院,云南 昆明 650031 
刘 娟 云南林业职业技术学院,云南 昆明 650224 
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中文摘要:
      基于盈江县第八次和第九次森林资源连续清查数据,采用多元线性回归方法,分析林木大小因子、林木竞争因子、立地因子及其他林分因子对7个主要树种单木胸径生长的影响。分别以5 年的胸径生长量(D2-D1)、胸径平方增长量(D22 -D12)、胸径增长量的自然对数(ln(D2-D1+1))以及胸径平方增长量的自然对数(ln(D22 -D12+1))为因变量,通过相关性分析、模型比较、评价以及交叉验证的方法对7个树种构建单木胸径生长模型预估效果进行检验,最终筛选出不同树种最优单木胸 径生长模型。结果表明,林木大小因子、林木竞争因子、立地因子及其他林分因子在不同程度上对 盈江县7个树种胸径生长具有极显著影响;栎类、西南桦、青冈、木荷、旱冬瓜、其他软阔和其他硬阔 的最优单木胸径生长模型因变量分别为ln(D22 -D12 +1)、D2-D1、D2-D1、ln(D22 -D12 +1)、ln(D2-D1+ 1)、ln(D22 -D12 +1)、ln(D22 -D12 +1);基于模型拟合结果、结果检验及残差分析,7个树种单木胸径生 长模型拟合效果较好,具有一定的适用性。
英文摘要:
      Based on the 8th and 9th Continuous Forest Inventory data of Yingjiang County, this study employed multiple linear regression to analyze the effects of tree size factors, competition factors, site conditions, and other stand factors on the individual tree diameter at breast height(DBH) growth of seven major tree species. Four dependent variables were used to construct individual tree DBH growth models for each species: 5-year DBH increment(D2-D1), squared DBH increment(D22 -D12 ), natural logarithm of DBH increment (ln(D2-D1+1)), and natural logarithm of squared DBH increment (ln(D22 -D12 +1)). The models were evaluated through correlation analysis, model comparison, assessment, and cross-validation to select the optimal DBH growth model for each species. The results showed that tree size, competition, site conditions, and other stand factors had significant influences on the DBH growth of the seven species in Yingjiang County. The optimal dependent variables for Quercus L. , Betula alnoides, Cyclobalanopsis glauca, Schima superba, Alnus nepalensis, other broad-leaved softwood, and other broadleaved hardwood were ln(D22 -D12 +1), D2-D1, D2-D1, ln(D22 -D12 +1), ln(D2-D1+1), ln(D22 -D12 +1), ln (D22 -D12 +1), respectively. Based on model fitting, validation, and residual analysis, the individual tree DBH growth models for the seven species demonstrated good performance and applicability.
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