利用增长量分配模型的杉木林分生长预测建模

以Richards方程为林分生长预测方程, 结合林分的少量实测和经验数据, 采用最小二乘法求得Richards方程的各个参数重构方程, 根据重构的Richards方程预测林分的整体生长, 然后根据预测量得到平均增长量, 基于"强者越强, 弱者越弱"原则的增长量分配模型, 将增长量合理地分配到每株林木上。在预测林分生长时, 提出一种全林分生长模型到单木生长模型转换的新算法, 即全林分生长模型采用Richards方程预测林分某个生长因子整体的平均增长量, 然后根据增长量分配模型将整体增长量合理分派到每株林木, 实现由整体到个体的转换。采用湖南省攸县黄丰桥林场1块杉木Cunninghamia lanceolata林分样地的实验数据并对其模拟建模。结果表明:该方法可使林分预测建模简便快速, 且建立的生长模型真实度较高。

Stand growth prediction based on a growth distribution model

To predict growth of an entire forest, the least squares method was used with experimental and empirical data of a stand to reconstruct each parameter in Richards's equation. Then the average growth quantity was obtained through the predicted equation, and the increment was reasonably distributed to each tree according to an increment distribution model based on the principle of "the strong stronger, the weak weaker". For predicted stand growth, a new algorithm portraying the whole stand growth model developed from the single tree growth model was presented. For this, the whole stand growth model, from Richards's equation, was used to predict the average growth of a stand as a growth factor in the overall volume, then the overall growth increment was reasonably assigned to each tree by an incremental distribution model working from the whole to the individual. To prove this method was correct, an experiment on stand growth modeling was carried out using experimental data from Chinese fir (Cunninghamia lanceolata) stands in Hunan Province's Youxian Huangfengqiao Forest Farm. Results showed that this new model avoided problems with a competition model when studying complex issues among trees and with stand growth when used as a general law for growth in a forest.