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生态学杂志 ›› 2011, Vol. 30 ›› Issue (06): 1295-1303.

• 方法与技术 • 上一篇    下一篇

基于SOM神经网络的白河林业局森林健康分等评价

施明辉1,2,赵翠薇1,郭志华2**,刘世荣3   

  1. 1贵州师范大学地理与环境科学学院, 贵阳 550001; 2中国林业科学研究院湿地研究所, 北京 100091;3中国林业科学研究院, 北京 100091
  • 出版日期:2011-06-08 发布日期:2011-06-08

Forest health assessment based on self-organizing map neural network: A case study in Baihe Forestry Bureau, Jilin Province.

SHI Ming-hui1,2, ZHAO Cui-wei1, GUO Zhi-hua2**, LIU Shi-rong3   

  1. 1College of Geography and Environmental Science, Guizhou Normal University, Guiyang 550001, China;2Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China; 3Chinese Academy of Forestry, Beijing 100091, China
  • Online:2011-06-08 Published:2011-06-08

摘要: 将自组织特征映射(SOM)神经网络引入森林健康评价领域,与地理信息系统技术(GIS)相结合,基于森林经营小班尺度,对长白山白河林业局3个主要森林类型(阔叶混交林、针阔混交林、长白落叶松林)的森林健康状况进行定量评价,并分析了不同平均年龄段、不同平均树高、不同郁闭度森林小班的健康状况。结果表明:SOM神经网络是自动化定量评价森林健康的一个较先进的方法,其用于森林健康分等评价的最大优点是不需要知道分等类别的先验知识,不需要事先人为确定分等评价因素指标的权重,能有效地克服主观因素的干扰,使分等结果更加客观准确;不同森林类型健康等级状况的比例排序为阔叶混交林Ⅲ> Ⅱ>Ⅰ>Ⅳ>Ⅴ,针阔混交林Ⅱ> Ⅳ>Ⅰ>Ⅲ>Ⅴ,长白落叶松林Ⅰ>Ⅱ>Ⅲ>Ⅴ>Ⅳ;相对来说,森林小班平均年龄越大、平均树高越高、郁闭度越高,呈健康状况的小班比例也越高。以上评价结果可为白河林业局的森林可持续经营和多功能利用提供理论支撑。

关键词: 蚯蚓, 土壤碳、氮动态, 作物产量, 秸秆还田, 稻麦轮作

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, broadleaf-conifer 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 subcompartmentsⅡ> 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.

Key words: Earthworm, Dynamics of soil C and N, Crop yield, Crop residue application, Upland rice winter wheat rotation