• 论文 •

基于混合效应模型的杉木人工林蓄积联立方程系统

1. 中国林业科学研究院资源信息研究所 北京 100091
• 收稿日期:2011-03-23 修回日期:2011-08-05 出版日期:2012-06-25 发布日期:2012-06-25

The Simultaneous Equation System of Total Volume in Fir Plantation

Li Chunming

1. Research Institute of Forest Resources Information Techniques, CAF Beijing 100091
• Received:2011-03-23 Revised:2011-08-05 Online:2012-06-25 Published:2012-06-25

Abstract:

A simultaneously interdependent model system in which stand characteristics such as dominant height, basal area, and total volume are included is very common in forest growth and yield model. Permanent plots are usually the main source of data. Such data include two basic sources of errors. One is within-plot error, the other is the variation from plot to plot. Within-plot error is usually modeled by a reasonable variance function that accounts for within-plot heteroscedasticity and correlations, between-plot variation can be described by random effects of parameter to be varied from plot to plot in the models. A simultaneous system containing components of dominant height and basal area growth model based on nonlinear mixed effects, a log-linear total volume model is developed for fir plantation. In the end the result of mixed effect model is compared with that of ordinary regression analysis with validation data. The result showed that dominant height was the fundamental component in the three-component system. With random effects for dominant height and basal area and contemporaneous correlation of three components considered, random effects of total volume model were proved to be unnecessary. Dominant height itself dominated the precision of basal area prediction, dominant height and basal area were main source of error for total volume prediction. The fitted effects of simultaneous equation system based on mixed model approach were better than that of based on ordinary regression analysis. The observed components could improve the prediction of the unobserved components by accounting for the contemporaneous correlation among the components in prediction.