基于因子分析与聚类分析的森林质量评价——以北京市京西林场为例

Forest Quality Evaluation Based on Factor Analysis and Cluster Analysis: A Case Study of the Western Beijing Forest Farm

中文关键词: 因子分析  聚类分析  森林质量评价  京西林场

英文关键词: factor analysis  cluster analysis  forest quality evaluation  the Western Beijing Forest Farm

中文摘要:

      以北京市京西林场北港沟、曹家铺和斋堂山林区典型植被群落为研究对象，采用因子分析和聚类分析方法开展森林质量评价。结果表明，通过因子分析提取3个主成分，累积方差贡献率为75.621%，较全面反映森林质量状况。3个准则层权重值为:林木生长因子>林分结构因子>林下植被因子，说明林木生长因子影响最大。运用K-Means聚类法对森林质量指数进行质量等级划分，各等级样地数量占比为:优 17%、良6%、中57%、差17%；不同海拔森林质量分化明显，其中斋堂山以“优”为主，三岔口、八二〇、北港沟质量以“中”为主，曹家铺以“差”为主。研究结果对掌握京西林场森林质量状况，为后续开展森林质量精准提升奠定基础。

英文摘要:

      In order to understand the forest quality status of the Western Beijing Forest Farm , this study takes the typical vegetation communities at different altitudes in Beiganggou, Caojiapu and Zhaitangshan forest areas of the Western Beijing Forest Farm as the research objects, and carries out the forest quality evaluation by using factor analysis and cluster analysis. The research results show that: Three principal components are extracted through factor analysis, with a cumulative variance contribution rate of 75.621%, which can comprehensively reflect the forest quality status; The weight values of the three criterion layers are as follows: tree growth factor > stand structure factor > understory vegetation factor, indicating that the tree growth factor has the greatest influence on the forest quality evaluation index system; The K-Means clustering method is applied to classify the quality grades of the forest quality index. The proportions of the number of sample plots at each grade are: excellent (17%), good (6%), medium (57%), and poor (17%); The forest quality at different altitudes is obviously differentiated. Among them, the forest in Zhaitangshan is mainly "excellent", the forest quality at Sanchakou, Ba'erling, and Beiganggou is mainly "medium", and the forest quality in Caojiapu is mainly "poor". The research results are helpful for mastering the forest quality status of the Western Beijing Forest Farm, thus laying a foundation for the subsequent precise improvement of forest quality.