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基于哑变量的湖南栎类天然林林分断面积生长模型(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2018年02期
Page:
155-162
Column:
专题报道
publishdate:
2018-03-20

Article Info:/Info

Title:
Basal area growth model for oak natural forest in Hunan Province based on dummy variable
Article ID:
1000-2006(2018)02-0155-08
Author(s):
ZHU Guangyu12 HU Song1 FU Liyong2*
1. Forestry College,Central South University of Forest & Technology, Changsha 410004,China; 2. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Keywords:
Keywords:oak natural forest stand basal area forest type site type dummy variable
Classification number :
S758
DOI:
10.3969/j.issn.1000-2006.201704059
Document Code:
A
Abstract:
【Objective】This paper established a basal area growth model containing a forest type or site type dummy variable for oak populations in Hunan Province. The purpose of this study was to provide a theoretical reference for basal area-based harvest and growth forecasts.【Method】In total, 51 plots of natural oak mixed forest in five locations in Hunan Province were used to fit six basal area growth models with biological significance, which included the independent variables of stand age, mean dominant height, and stand density index, by applying the Forstat package. The effects and performances of different equations and density indices on the model simulation were then compared.Then, the model with the best fit was chosen as the basis for building dummy variable models.In terms of forest types and site types partitioned by considering the differences in site types and dominant tree species, the dummy variable models were constructed and their simulation performances were accordingly evaluated.【Result】 The basal area growth models of the stand density index had determinant coefficients ranging from 0.85 to 0.92 and a prediction accuracy greater than 95%,which were much better than the tree density models with the determinant coefficients ranging from 0.47 to 0.51 and a prediction accuracy less than 93%. Schumacher model had the best simulation result, with the highest R2(0.924 2). Schumacher model was used as the basic model to establish the dummy variable model. The result showed that the site type model(R2=0.997 6)was better than the forest type(R2=0.979 8)and basic models(R2=0.924 2).【Conclusion】The models with dummy variables can effectively solve the influence of dominant tree species distribution and site type differences,improve the modeling accuracy, and provide a reference and basis for oak natural forest growth and harvest management.

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Last Update: 2018-06-12