墨西哥南部五种松树的广义高径模型

IF 1.8 Q2 FORESTRY
Wenceslao Santiago-García, Antonio Heriberto Jacinto-Salinas, G. Rodríguez-Ortiz, A. Nava-Nava, Elías Santiago-García, G. Ángeles-Pérez, J. R. Enríquez-del Valle
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引用次数: 8

摘要

广义胸高径(D)模型是估算林分木材蓄积量、建立森林生长模型所需的基础信息和制定森林经营计划的基本投入的重要工具。以墨西哥瓦哈卡州Ixtlán de Juárez森林经营下的5种松林为研究对象,建立了基于D和林分变量估算总高度的广义模型。使用的数据来自木材林调查,其中n = 1041个采样样地,每个样地1000平方米,基于分层系统抽样设计。根据相对丰度选择的树种有:松、瓦哈卡松、红松、大山松和松。利用回归技术拟合了5个非线性方程,预测了几种造林制度和森林经营条件下树木的TH。拟合优度的统计标准为:调整后的决定系数(R2adj)、均方根误差(RMSE)和预测的绝对平均偏差(Ē)。同样,考虑了方程预测能力的图形分析。这些树种的D和林分变量(二次平均直径、优势直径和优势高度)解释了TH数据的75 - 83%的变异。应用开发的广义模型来估计树木总高度的预测变量需要较少的采样工作,并且来自传统的森林清查数据,这可以减少实地工作的成本和时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalized height-diameter models for five pine species at Southern Mexico
Abstract Generalized height-diameter at breast height (D) models are essential for the estimation of the timber stocks of a forest stand, as well as in the generation of base information to develop forest growth models, and as basic inputs in the development of forest management plans. Generalized models were developed to estimate total height (TH) based on the D and stand variables, of five Pinus species in forests under forest management of Ixtlán de Juárez, Oaxaca, Mexico. The data used come from a timber forest inventory, where n = 1041 sampling plots of 1000 m2 each were established based on a stratified-systematic sampling design. The species selected according to their relative abundance were: Pinus patula, Pinus oaxacana, Pinus ayacahuite, Pinus teocote and Pinus leiophylla. Five nonlinear equations were fitted using regression techniques to predict the TH of the trees under several silviculture regimes and forest management conditions. The statistical criteria of goodness of fit used were: adjusted coefficient of determination (R2adj), root mean square error (RMSE) and absolute average bias in the prediction (Ē). Likewise, the graphic analysis of the predictive capacity of the equations was considered. The D and the stand variables (quadratic mean diameter, dominant diameter and dominant height) for these species explained between 75 and 83% of the variability of the TH data. The predicting variables to apply the developed generalized models to estimate tree's total height require less sampling effort and are derived from conventional forest inventory data, which allows to reduce costs and time in field work.
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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
0
审稿时长
21 weeks
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