摘要
【目的】考虑天然混交林的林分密度、直径结构和树种结构,基于代数差分方程构建最适宜的林分平均高与平均胸径关系模型,为天然混交林的立地生产力估计与可持续经营提供理论依据。【方法】以吉林省天然栎类阔叶混交林为研究对象,利用4期连续调查固定样地数据,基于Richards方程构建4种数据结构类型即typeC、typeD、typeE和typeF的基础代数差分方程,比较分析得出最优数据结构类型;基于最优数据结构类型,以5个林分密度指标即林木株数(N)、林分断面积(BA)、林分密度指数(SDIr)、可加林分密度指数(SDIa)和郁闭度(CD),5个直径多样性指数即Shannon均匀度指数(ShaI)、Simpson指数(SimI)、McIntosh均匀度指数(MceI)、Gini系数(GinI)和Berger-Parker指数(BerI),4个树种多样性指数即ShaI、SimI、MceI和BerI,构建并比较分析不同多样性代数差分方程的差异,得出最佳方程为最适宜林分平均高与平均胸径关系模型。【结果】不同数据结构类型的建模效果由好到差排序:typeD>typeC>typeF>typeE。除了typeC,其他3个数据结构类型的模型参数b和r均显著不为零(P<0.01),说明typeD拟合的模型参数检验效果最佳。林分密度指标SDIr的建模效果最好。无论使用哪个林分密度指标,其模型参数b0、r和cSD均显著(P<0.01),说明5个林分密度指标的模型参数检验效果均比较理想。直径多样性指数ShaI的建模效果最好。除了GinI,其他4个直径多样性指数的模型参数b0、r、cSDIr和cDI均显著(P<0.01),表明ShaI、SimI、MceI和BerI均为较理想的直径多样性指数。4个树种多样性指数的建模拟合效果和检验数据效果差别不大。BerI的模型参数b0、r、cSDIr、cShaI和c SP均显著(P<0.01),说明BerI是较理想的树种多样性指数。ShaI、SimI和MceI的模型参数b0、r、cSDIr、cShaI和cSP均不能同时达到0.05显著水平,说明ShaI、SimI和MceI是不理想的树种多样性指数。【结�
[Objective]Considering stand density,diameter structure and tree species structure,the optimal model for stand mean height and mean DBH relationship was constructed using algebraic difference approach.It may provide a theoretical basis for site productivity estimation and sustainable management of natural mixed forests.[Method]Base algebraic difference approaches were modeled with 4 different data structure types,i.e.typeC,typeD,typeE and typeF based on Richards model using 4 inventory data of permanent sample plots in natural Quercus spp.broadleaved mixed stands.The 4 different base algebraic difference approaches were comparatively analyzed to get the optimal data structure type.Algebraic difference approach of diversity indices was constructed based on the optimal data structure type using 5 different stand density indices,including tree number(N),stand basal area(BA),stand density index(SDIr),additive stand density index(SDIa)and canopy density(CD),and the 5 different diameter diversity indices including Shannon evenness index(ShaI),Simpson index(SimI),McIntosh evenness index(MceI),Gini coefficient(GinI)and Berger-Parker index(BerI),and the 4 different species diversity indices including ShaI,SimI,MceI and BerI.The algebraic difference approach of diversity indices was comparatively analyzed to obtain the optimize algebraic difference approaches,i.e.the optimize stand mean height and mean DBH relationship.[Result]Model fitting effects of calibration data in different data structure types were sorted from best to worst,and the ranking was:typeD>typeC>typeF>typeE.Except for typeC,model coefficients b and r of the other three data structure types were significant(P<0.01),indicating that the model fitting effects of typeD were the best.Model fitting effects of SDIr were the best.Model coefficients b0,r and cSD were significant(P<0.01),regardless of which stand density index was used,indicating that model fitting effects of the 5 different stand density indices were reasonable.Model fitting effect of ShaI was the
作者
娄明华
张会儒
雷相东
白超
杨同辉
Lou Minghua;Zhang Huiru;Lei Xiangdong;Bai Chao;Yang Tonghui(Ningbo Academy of Agricultural Sciences,Ningbo 315040,Zhejiang,China;Research Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing 100091,China;Ningbo Institute of Surveying and Mapping&Remote Sensing,Ningbo 315042,Zhejiang,China)
出处
《北京林业大学学报》
CAS
CSCD
北大核心
2020年第9期37-50,共14页
Journal of Beijing Forestry University
基金
国家自然科学基金项目(31800539、31700563)
宁波市科学技术局公益性计划项目(2019C10084)。
关键词
林分平均高
数据结构类型
林分密度
直径多样性
树种多样性
代数差分方程
stand mean height
data structure type
stand density
diameter diversity
tree species diversity
algebraic difference approach
