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当前位置: 首页 > 文章 > 黑龙江省主要林分类型林分碳储量预估模型 东北林业大学学报 2017 (8) 30-38
Position: Home > Articles > Carbon Stock Predicting Models of Main Forest Types in Heilongjiang Province Journal of Northeast Forestry University 2017 (8) 30-38

黑龙江省主要林分类型林分碳储量预估模型

作  者：
贾炜玮;林键
单  位：
东北林业大学
关键词：
林分类型;林分变量;哑变量;林分碳储量模型
摘  要：
利用2期黑龙江省森林资源连续清查数据(2005—2010年),根据林分变量和林分碳储量间的关系构建了林分水平的全树碳储量预估模型,并对应不同起源选择不同的模型形式,用加权最小二乘法消除了异方差。由于地域的不同,相同林分类型碳储量可能存在差异,因此在构建的碳储量模型基础上,利用哑变量方法构建考虑不同地域的林分碳储量模型。结果表明:区分起源的林分碳储量模型对于天然林和人工林都具有良好的拟合精度,R~2均大于0.94,模型评价指标中平均相对误差均在±6.00%以内,平均相对误差绝对值基本小于10%,仅黑桦天然林为15.33%。大部分模型的预测精度在95%以上。利用哑变量方法构建的考虑不同地域的林分碳储量通用模型的R~2均大于0.94,平均相对误差均较小,平均相对误差绝对值均在小于8%,预测精度都在95%以上。对于包含区域哑变量的通用模型,在满足相同的林分平均断面积条件及其他变量、参数a、c不变时,不同区域对应的参数b值越大,相应区域碳储量越大;在满足相同的林分平均高(或者林分年龄条件)及其他变量、参数a、b不变时,不同区域对应的参数c值越大,相应区域碳储量越大。
译  名：
Carbon Stock Predicting Models of Main Forest Types in Heilongjiang Province
作  者：
Jia Weiwei;Lin Jian;Northeast Forestry University;
关键词：
Stand types;;Stand variables;;Dummy variables;;Stand-level carbon model
摘  要：
The carbon stock prediction model on stand level was developed based on the relationship between stand variables and carbon stocks and the forest continuous inventory data of 2005-2010. The weighted nonlinear least square was used to eliminate the heteroscedasticity for the models of the different origins. Due to the difference between regions,the carbon of the same forest types may be different. Therefore,the carbon stock models of different regions were developed by using the dummy variable approach. The forest carbon model for natural forest and plantation which distinguished the origins had good performance,and R~2 was greater than 0.94. Model evaluation statistics of mean error percent was within the range of± 6.00%,and the mean absolute error percent was less than 10% except for the 15.33% for black birch natural forest. Most of the model prediction accuracy is above 95%. With dummy variable method and considering the different regions of compatible forest carbon,R~2 was greater than 0.94,and the mean error were smaller. The mean absolute error percent was less than 8%. The prediction accuracy was all above 95%. As for the dummy variable used to develop the general model,the carbon stock of the specific region was increased with the increasing of value for the parameter b on the condition of the mean basal area,and the parameters of a and c were fixed. The carbon stock was increased with the increasing of c for the different regions when the height of forest stand,other stand variables,and the parameters of a and b were constant.

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