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基于冠幅及植株高度的檵木生物量回归模型(PDF)

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

Issue:
2006年04期
Page:
101-104
Column:
研究论文
publishdate:
2006-04-20

Article Info:/Info

Title:
The Regression Model of Loropetalum chinense Biomass Based on Canopy Diameter and Plant Height
Article ID:
1000-2006(2006)04-0101-04
Author(s):
ZENG Hui-qing123 LIU Qi-jing2 MA Ze-qing2 ZENG Zhen-ying3
1. Research Center for Eco-Environmental Sciences CAS, Beiiing 100085, China; 2. Institute of Geographic Sciences and Natural Resources Research CAS, Beijing 100101, China; 3. Environmental Science and Engineering College of Nanchang University, Nanchang 330029, China
Keywords:
Loropetalum chinense Shrub Biomass Regression model
Classification number :
S718; Q948.2
DOI:
10.3969/j.jssn.1000-2006.2006.04.024
Document Code:
A
Abstract:
Shrub biomass is an important part of forest biomass. Quantification of shrub biomass is important to understand the fixation, depletion, distribution, accumulation and translation of materials and energy in the whole forest ecosystem. Total harvesting is generally impractical or inappropriate in forest studies. So allometric methods have been developed to estimate biomass form nondestructive surrogate measurements such as canopy diameter and plant height. The objective of this paper is to find a simple, convenient and accurate method to estimate Loropetalum chinense biomass since L. chinense is one of common shrub species in subtropical forest in China. Statistics methods were used to choose the best models with canopy diameter (C) and plant height (H) as variables. The results showed the relationship coefficient between biomass and CH (canopy diameter multiply plant height) variable is the biggest and W=ha (CH) b1 is the best model for estimating biomass. The best estimate models for branch biomass, leaves biomass and upper biomass are W1=0.000796 (CH) 1.1878, W2=0.0114 (CH) 0.7581, W3=0.0024 (CH) 1.0973 respectively. Compared with the models with D2H (basal diameter square multiply plant height), the power curve models with CH as variable have the same estimation precision. With the convenience in real forest investigation, the models with CH as variable are better than the ones with D2H as variable. The high precision of the model with CH as variable indicated that L. chinense has columniform morphology approximately. The results should be validated whether they fit trees from a particular stand.

References

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Last Update: 2013-05-20