| 基于单木生长神经网络模型的林分生长预测 |
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| 引用本文： | 黄家荣,孟宪宇,关毓秀.基于单木生长神经网络模型的林分生长预测[J].贵州大学学报(农业与生物科学版),2005,24(6):477-482. |
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| 作者姓名： | 黄家荣 孟宪宇 关毓秀 |
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| 作者单位： | 1. 河南农业大学,林学园艺学院,河南,郑州,450002
2. 北京林业大学,资源与环境学院,北京,100083 |
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| 基金项目： | 贵州省基金资助项目（933036）,致谢：本文属第1作者博士论文《马尾松人工林生长的人工神经网络模型研究》的一部分,得到温佐吾教授主持的省攻关课题的资助;得到贵州龙里林场的大力支持;得到罗军的帮助;贵州大学林学院1993～2000届林学专业毕业班的部分学生参加外业调查,特致谢意. |
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| 摘 要： | 以马尾松Piuns massoniana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。
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| 关 键 词： | 神经网络 马尾松 人工林 林分生长预测 |
| 文章编号： | 1008-0457（2005）06-0477-06 |
| 收稿时间： | 2005-09-13 |
| 修稿时间： | 2005年9月13日 |
Prediction of stand growth on base of individual tree growth neural network model |
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| HUANG Jia-rong,MENG Xian-yu,GUAN Yu-xiu.Prediction of stand growth on base of individual tree growth neural network model[J].Journal of Mountain Agriculture & Biology,2005,24(6):477-482. |
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| Authors: | HUANG Jia-rong MENG Xian-yu GUAN Yu-xiu |
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| Institution: | 1. College of Forestry and Horticulture, Henan Agricultural University, Henan Zhengzhou 450002, China; 2. College of Resources and Environment, Beijing Forestry University, Beijing 100083, China |
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| Abstract: | 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. |
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| Keywords: | neural network Pinus massoniana planted forest prediction of stand growth |
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