广西杉木立木材积模型优化筛选研究  

Study on the Optimization and Selection of Cunninghamia lanceolata Standing Volume Model in Guangxi

作　　者：黄孝发 张伟 岑巨延 吴国欣 黄淑莹 HUANG Xiaofa;ZHANG Wei;CEN Juyan;WU Guoxin;HUANG Shuying(Guangxi Forestry Survey and Design Institute,Nanning 530001,China;Guangxi Gaofeng State-Owned Forest Farm,Nanning 530001,China)

机构地区：[1]广西壮族自治区林业勘测设计院,南宁530011 [2]广西国有高峰林场,南宁530001

出　　处：《林业资源管理》2022年第6期61-67,共7页Forest Resources Management

基　　金：广西林业科研项目(桂林科字[2015]28号);广西林业科研项目(桂林科字[2016]第24号)。

摘　　要：为实现杉木林蓄积量调查与估测的精准量化,基于林业数表编制作业获取的标准立地245株杉木树高、冠幅、胸径和带皮材积等林业资源调查数据,采用非线性估计法建立11种一元立木材积模型,通过求解模型参数并进行优异比较,渐进拟合出最优的胸径-树高-冠幅三元材积模型。结果表明:1)无论树高、胸径还是冠幅,11种单因子变量估测立木材积的曲线模型均以幂函数的检验参数最优。2)基于模型确定系数判定拟合模型优劣呈现三元>二元>一元。其中,包含树高、胸径和冠幅三元非线性最优材积模型确定系数0.988,总相对误差0.087%,总系统误差0.57%,模型预估精度99.40%。3)胸径-树高-冠幅三元材积模型充分集成了林分结构参数信息,其高精度低误差特点对林业数表编制、森林蓄积量监测具有重要参考价值。In order to provide accurate model for the forest timber volume estimation, using the data of 245 Cunninghamia lanceolata trees obtained on the standard sampling sites by the forestry table compilation work, both one variable and binary model, as well as multiple variables model based on diameter at breast height(DBH),tree height(H) and crown width(Cw) as independent variables were optimized and constructed.The 11 types of curve model were fitted firstly with one independent variable.And then binary model was established using power function.In the end, the DBH-H-Cw multiple independent variables volume model was established by nonlinear regression estimation method, which was regarded as the best model for the fitted and tested parameters.The results showed that: 1) The power function was regarded as the best model among the 11 types of curve model.2) As far as the determinant coefficient and significant test level were concerned, multiple independent variables model was superior to two independent variables model, and then which was superior to one variable model.The most optimized multiple independent variables model combined of DBH,H and Cw had the determinant coefficient of 0.988,the contrast error of 0.087%,the total error of 0.57%,and the prediction accuracy of 99.40%.3) The most optimized model can drag the maximum loading information of the data obtained from forest timber structure, and provide measurement basis for highly accurate estimation method to the forest volume.

关 键 词：非线性回归法 杉木 材积模型 广西 

分 类 号：S758[农业科学—森林经理学]