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Position: Home > Articles > Forest Biomass Evaluation Method Based on Principle Component Analysis,Clustering and SVR Forest Engineering 2014,30 (6) 17-21

基于主成分、聚类与SVR组合算法的森林生物量估算方法研究

作  者：
高萌;王霓虹;李丹;刘立臣
单  位：
东北林业大学机电工程学院;东北林业大学信息与计算机工程学院
关键词：
生物量估算;主成分分析;系统聚类;SVR算法
摘  要：
以孟家岗林场二类清查数据为基础,对1371个小班的11项指标进行主成分分析,并采用系统聚类法对小班进行分类,进而利用支持向量回归算法分别进行生物量模型训练。结果表明:7个主成分指标可反映87.995%的生物量信息;1371个小班可分为5类,各类训练模型的预测精度均在89%以上,且均以v-SVR模型为最优。在得到的5类生物量训练模型基础上估算林场森林乔木层生物量,无需分起源、树种、立地类型,能够在保证生物量估算精度的同时,大大减少工作量,可为区域生物量的估算提供一种新的方法。
译  名：
Forest Biomass Evaluation Method Based on Principle Component Analysis,Clustering and SVR
作  者：
Gao Meng;Wang Nihong;Li Dan;Liu Lichen;College of Information and Computer Engineering,Northeast Forestry University;Gollege of Mechanical and Electrican Engineering,North east Fouestry University;
关键词：
biomass estimation;;principle component analysis;;system clustering;;SVR algorithm
摘  要：
Based on the forest resources inventory data of Mengjiagang forest farm,principle component analysis was used to extract the principle components of 11 indexes for 1371 subcompartments,and system clustering method was used to classify these subcompartments,and support vector regression algorithm was used to train the biomass estimation model based on the classified subcompartments. Experiment results showed that 7 principle components can reflect 87. 995% information of biomass; 1371 subcompartments can be divided into 5 types,and the prediction accuracy are all above 89% and v-SVR model was proved to be the optimal one among the training models. Therefore,it is possible to estimate the tree layer biomass based on the 5 types of biomass models without the information of origin,species,and site. The method can guarantee the biomass estimation accuracy while greatly reducing the workload,which provides a new regional biomass estimation method.

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