摘要
以马尾松P iunsm asson iana人工林间伐试验林为研究对象,用单木生长神经网络模型与林分表法的转移概率矩阵模型构建了林分直径分布的动态转移模型,再与径阶材积向量或材种材积向量构成林分生长与收获预测模型。预测检验结果显示,高、中、低密度的林分断面积预测精度依次为94%、95%、97%,蓄积量预测精度依次为92%、94%、96%,表明不计枯损(或采伐)的转移概率矩阵模型对低密度林分的预测比对高密度林分的预测效果好。
A dynamic transfer model of diameter distribution is constructed by using individual tree growth neural network model and transfer matrix of stand table, in Masson pine thinning experiment forest. And a stand growth and yield prediction model is constructed by using volume of diameter grade vector and volume of timber assortment vector. The prediction accuracy of high, medium and low forest density in basal area of forest stands by using the model is 94% , 95% and 97% respectively. The prediction accuracy of volume is 92% , 94% and 96% respectively. The results indicate the prediction accuracy of the transfer matrix in lower density forest is better than higher density forest, in the case of exclusive of mortality or thinning.
出处
《山地农业生物学报》
2005年第6期477-482,共6页
Journal of Mountain Agriculture and Biology
基金
贵州省基金资助项目(933036)
关键词
神经网络
马尾松
人工林
林分生长预测
neural network
Pinus massoniana
planted forest
prediction of stand growth
