树木位置空间模式建模与预测(英文)

Modeling and Predicting Spatial Patterns of Trees

传统的林分生长与收获模型不能为林分和生态系统管理提供准确的空间信息。采用3种配对势函数的Gibbs点过程模型对美国东北部50块云冷杉样地里树木的空间分布模式进行模拟。该Gibbs模型能够较好地模拟这50块样地中的82%～84%,但其对完全随机分布和规则分布的样地模拟比对聚集分布的样地模拟效果要好。使用常用的林分变量如林分密度、公顷断面积、林分平均胸径、平均树高、平均冠幅和冠长建立经验回归模型对Gibbs模型的2个参数进行预测。结果表明这些回归模型对81%的样地可以得到满意的模拟效果,其中,100%的完全随机分布样地、71%的规则分布样地和56%的聚集分布样地模拟效果较好。选择3块样地对树木的模拟空间位置和实际观测位置的相似性进行对比和说明。

Traditional forest growth and yield models have been criticized for their inability to provide precise spatial information for forest and ecosystem management. In this study the spatial patterns of trees within 50 spruce-fir plots in the Northeast, USA were modeled by a Gibbs point process model with three pair potential functions. In general, 82%-84% of these 50 plots were modeled well by the Gibbs model. However, the complete spatial random （CSR） and regular spatial patterns were modeled better than the clustered plots. Further, empirical regression models were developed to predict the two parameters of the Gibbs model using the available stand variables as predictors such as stand density, basal area, mean tree diameter, mean tree height, mean crown length, and mean crown width. The simulation results showed that 81% of the 50 plots were satisfactorily predicted by the empirical regression models. Among them, 100% of the CSR plots, 71% of the regular plots, and 56% of the clustered plots were predicted well by the empirical regression models. Three example plots were selected to illustrate the similarity between simulated tree locations and observed ones.