生态学杂志2011，Vol.30Issue(6)：1295-1303,9.
基于SOM神经网络的白河林业局森林健康分等评价
Forest health assessment based on self-organizing map neural network:A case study in Baihe Forestry Bureau,Jilin Province
摘要
Abstract
Through introducing self-organizing map ( SOM) neural network into forest health assessment, and combining with geographic information system ( GIS) , a quantitative assessment was conducted on the health status of different forest types ( broadleaved mixed forest, broadleafconifer mixed forest, and Larix olgensis forest) at subcompartment scale in the Baihe Forestry Bureau in Changbai Mountains, and a comparison was made on the health status of the sub-compartments with different average age, different average tree height, and different canopy density. The results showed that SOM neural network would be a more advanced approach for the automated and quantitative assessment of forest health. Its greatest advantage in the assessment of forest health was no need to know the priori knowledge about classification categories, and no need of the assessment indicators ’ weights beforehand determined. As a result, SOM neural network could effectively overcome the interference of subjective factors, and let the classification results become more objective and accurate. The health level of test forest types was in the order of broadleaved mixed forest subcompartments Ⅲ＞ Ⅱ ＞ Ⅰ ＞ Ⅳ＞ Ⅴ , broadleaf-conifer mixed forest subcompartments Ⅱ ＞ Ⅳ＞ Ⅰ ＞ Ⅲ＞ Ⅴ , and Lalix olgensis forest subcompartments Ⅰ ＞ Ⅱ ＞ Ⅲ＞＞ Ⅴ ＞ Ⅳ. Relatively, the forest subcompartments that had greater average age and higher average tree height and canopy density would have higher level forest health. This study could provide theoretical support for the sustainable management and multifunctional use of the forests in Baihe Forestry Bureau.关键词
SOM神经网络/森林健康评价/白河林业局/GISKey words
SOM neural network/ forest health assessment/ Baihe Forestry Bureau/ GIS.分类
农业科技引用本文复制引用
施明辉,赵翠薇,郭志华,刘世荣..基于SOM神经网络的白河林业局森林健康分等评价[J].生态学杂志,2011,30(6):1295-1303,9.基金项目
林业公益性行业专项重大项目(200804001)和林业公益性行业专项(201104072)和国家"十一五"科技支撑计划项目(2006BAD03A0406)资助. （200804001）
