基于主成分分析的林型数量分类研究——以吉林省长白山科学院实验基地为例

Research on Quantitative Classification of Forest Types Based on Principal Component Analysis:Taking Jilin Changbai Mountain Academy of Experimental Base as an Example

基于主成分分析方法,对吉林省长白山科学院实验基地的4种林型杂木林、白桦林、红松阔叶林和长白落叶松林进行数量分类研究。通过选取并测量9种分类指标,对不同林型样地的指标数据进行主成分分析,计算得出林型综合分类指标的数值范围,从而建立起方便快捷的林型数量分类方法。研究表明:所选取的9种分类指标,多数指标之间的相关性显著,相关系数矩阵中绝对值大于0.3的系数(P<0.01)占66.67%;通过主成分分析的降维处理,提取出3个主成分因子,进而得出长白山科学院实验基地林型综合分类指标的数值范围为:杂木林[-1.456,-1.128]、白桦林[0.303,0.796]、红松阔叶林[1.286,1.745]、长白落叶松林[-0.256,0.156];对该林型数量分类方法进行可行性验证,准确度达90%。

Based on the principal component analysis method,quantitative classification studies were conducted on four forest types of mixed forest,birch forest,korean pine broad-leaved forest,and Larix olgensis forest at the experimental base of the Changbai Mountain Academy.Through selecting and measuring 9 kinds of classification indexes,the principal component analysis of the index data of different forest type plots was carried out,and the numerical range of forest type comprehensive classification index was calculated,thus establishing a convenient and rapid forest type number classification method.The results showed that among the nine categories of selected indexes,the correlation between the majority of indexes was significant,and the coefficient with an absolute value greater than 0.3(P<0.01)in the correlation coefficient matrix accounted 66.67%;three principal component factors were extracted through dimension reduction of principal component analysis,and the numerical range of comprehensive forest classification index for the experimental base of the Changbai Mountain Academy was:Hybrid forest[-1.456,-1.128],Birch forest[0.303,0.796],Korean pine broad-leaved forest[1.286,1.745],Larix olgensis forest[-0.256,0.156];we verified the feasibility of the quantitative classification method for forest types with an accuracy of 90%.