哈萨克斯坦首都努尔苏丹人工林健康评价
收稿日期: 2021-02-23
修回日期: 2021-04-12
网络出版日期: 2021-09-24
基金资助
中国科学院战略性先导科技专项（A类）(XDA20030102);中国科学院关键技术人才项目(“一带一路”荒漠化防治技术模式研究)
Health assessment of plantations in Nursultan, capital of Kazakhstan
Received date: 2021-02-23
Revised date: 2021-04-12
Online published: 2021-09-24
通过分析努尔苏丹不同人工林,筛选健康综合评价指标,建立健康综合评价模型,为努尔苏丹及其周边人工林提供健康综合评价理论基础。调查努尔苏丹25块人工林样地和2块天然林样地,选取林下植物Shannon-Wiener指数（X1）、Pielou指数（X2）、Simpson指数（X3）、林分空间综合结构综合指数（X4）、土壤有机质（X5）、全氮（X6）、全磷（X7）、pH（X8）、质量含水量（X9）、林木平均株高（X10）、平均胸径（X11）、平均枝下高（X12）、平均冠幅（X13）和林下更新（X14）共14个指标,采用因子分析、聚类分析、判别分析和逐步回归分析等多元统计分析法,对努尔苏丹人工林健康状况开展综合评价。通过因子分析将14个单项指标转换为4个相互独立的综合指标,其贡献率分别为30.482%、24.374%、19.711%和8.646%,代表了全部数据83.212%的信息量。结合因子得分系数矩阵与各因子权重得到健康综合得分值。对健康综合得分值进行聚类分析,将选择的样地划分为5类,优质健康（Ⅰ）、良好健康（Ⅱ）、一般健康（Ⅲ）、亚健康（Ⅳ）和不健康（Ⅴ）。使用判别分析验证聚类分析的效果,其自身验证与交叉验证的准确率分别为100%、85.185%。采取逐步回归分析建立努尔苏丹人工林健康评价最优数学模型,H=0+0.293X13+0.186X5+0.079X3+0.100X2+0.038X7（R2=0.987）,筛选出5个判断人工林健康状况的指标,分别为平均冠幅、土壤有机质、Simpson指数、Pielou指数和土壤全磷。平均冠幅、土壤有机质、Simpson指数、Pielou指数和土壤全磷可作为判断努尔苏丹人工林健康状况的指标,可在相同条件下测定这5项指标,计算健康综合评价值并预测其健康状况。
闫晋升,王永东,娄泊远,艾柯代·艾斯凯尔,徐新文 . 哈萨克斯坦首都努尔苏丹人工林健康评价[J]. 干旱区研究, 2021 , 38(5) : 1474 -1483 . DOI: 10.13866/j.azr.2021.05.30
The study was performed in Nursultan and its surroundings. Its principal aims are to explore the methods of plantation health assessment, analyze different plantations, screen suitable assessment indexes of plantation health, and establish an evaluation model of plantation health, which would provide theoretical support for health assessment in the region. Twenty-five plantation plots and two natural forest plots were analyzed. Shannon-Wiener index (X1), Pielou index (X2), Simpson index (X3), stand spatial structure optimization object function(X4), soil organic matter content (X5), soil total nitrogen content (X6), soil total phosphorus content (X7), soil pH (X8), soil moisture (X9), mean tree height (X10), mean breast diameter (X11), mean height under branches (X12), mean canopy (X13), and forest regeneration (X14) were evaluated. Factor analysis, cluster analysis, discriminant analysis, and stepwise regression analysis were used to comprehensively assess the plantations. Fourteen single indicators were converted into four independent indicators through factor analysis. The contribution rates of the first four factors were 30.482%, 24.374%, 19.711%, and 8.646%, representing 83.212% of the original data variance. The health score value was calculated through the factor score coefficient matrix and the weight of each factor. Cluster analysis was performed on comprehensive health scores, and plots were divided into five categories: (Ⅰ) a high-quality type, (Ⅱ) a satisfied type, (Ⅲ) a moderate type, (Ⅳ) a vulnerable type, and (Ⅴ) an unhealthy type. The results of discriminant analysis and cluster analysis were similar. The accuracy of the self-verification and cross-validation were 100% and 85.185%. The optimal mathematical model for plantation health assessment was established as H=0+0.293X13+0.186X5+0.079X3+0.100X2+0.038X7(R2=0.987). Five indexes for plantation health assessment were selected: Mean canopy, soil organic matter content, Simpson index, Pielou index and soil total phosphorus content. Mean canopy, soil organic matter content, Simpson index, Pielou index, and soil total phosphorus content could be used to assess the health of plantations in the region. The comprehensive assessment value of the five indexes could be calculated to predict healthy conditions by measuring the five indexes under the same conditions.
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