生态与环境

哈萨克斯坦首都努尔苏丹人工林健康评价

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  • 1. 中国科学院新疆生态与地理研究所,新疆 乌鲁木齐 830011
    2. 中国科学院大学,北京 101408
    3. 国家荒漠-绿洲生态建设工程技术研究中心,新疆 乌鲁木齐 830011
闫晋升(1996-),男,硕士研究生,主要从事生态屏障建设研究. E-mail: 761478224@qq.com

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

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  • 1. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
    2. University of Chinese Academy of Sciences, Beijing 101408, China
    3. National Engineering Technology Research Center for Desert-Oasis Ecological Construction, Urumqi 830011, Xinjiang, China

Received date: 2021-02-23

  Revised date: 2021-04-12

  Online published: 2021-09-24

摘要

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+0.038X7R2=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。

本文引用格式

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

Abstract

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.

参考文献

[1] Boyd I L, Freer-Smith P H, Gilligan C A, et al. The consequence of tree pests and diseases for ecosystem services[J]. Science, 2013, 342(6160): 823-830.
[2] Andrew, Sugden, Julia, et al. Forest health. Forest health in a changing world. Introduction[J]. Science, 2015, 349(6250), 800-801.
[3] Wingfield M J, Brockerhoff E, Wingfield B D, et al. Planted forest health: The need for a global strategy[J]. Science, 2015, 349(6250): 832-836.
[4] Trumbore S, Brando P, Hartmann H. Forest health and global change[J]. Science, 2015, 349(6250): 814.
[5] 王秋燕, 陈鹏飞, 李学东, 等. 森林健康评价方法综述[J]. 南京林业大学学报(自然科学版), 2018, 42(2): 177-183.
[5] [ Wang Qiuyan, Chen Pengfei, Li Xuedong, et al. Review of forest health assessment methods[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2018, 42(2): 177-183. ]
[6] Teale S, Castello J. Forest Health: An Integrated Perspective[M]. Cambridge: Cambridge University Press, 2011: 3-16.
[7] 高志亮, 余新晓, 陈国亮, 等. 北京市八达岭林场森林健康评价研究[J]. 林业资源管理, 2008, 37(4): 77-82.
[7] [ Gao Zhiliang, Yu Xinxiao, Chen Guoliang, et al. Forest health assessment in Badaling forest farm of Beijing[J]. Forest Resources Management, 2008, 37(4): 77-82. ]
[8] Loehle Craig, Idso Craig, Bently Wigley T. Physiological and ecological factors influencing recent trends in United States forest health responses to climate change[J]. Forest Ecology and Management, 2016, 363: 179-189.
[9] Cale J A, Klutsch J G, Erbilgin N, et al. Using structural sustainability for forest health monitoring and triage: Case study of a mountain pine beetle (Dendroctonus ponderosae)-impacted landscape[J]. Ecological Indicators, 2016, 70(11): 451-459.
[10] 刘晓农, 宋亚斌, 邢元军. 基于SOM神经网络的新化县森林健康评价[J]. 中南林业科技大学学报, 2017, 37(4): 21-26.
[10] [ Liu Xiaonong, Song Yabin, Xing Yuanjun. Forest health assessment of Xinhua county based on SOM neural network[J]. Journal of Central South University of Forestry and Technology, 2017, 37(4): 21-26. ]
[11] 李显良, 张贵, 李建军. 基于熵权-云模型的环洞庭湖森林健康评价[J]. 中南林业科技大学学报, 2020, 40(11): 119-128.
[11] [ Li Xianliang, Zhang Gui, Li Jianjun. Assessment of forest health around Dongting lake based on entropy weight-cloud model[J]. Journal of Central South University of Forestry and Technology, 2020, 40(11): 119-128. ]
[12] 中国林业科学研究林业研究所. 结构化森林经营数据调查技术规程( LY/T 2811-2017)[S]. 国家林业局, 2017.
[12] [ Research Institute of Forestry Chinese Academy of Forestry.Survey of Data Technical Specification of Structure-based Forest Management(LY/T 2811-2017)[S]. The State Forestry Administration of the People’s Republic of China, 2017. ]
[13] 周红敏, 惠刚盈, 赵中华, 等. 林分空间结构分析中样地边界木的处理方法[J]. 林业科学, 2009, 45(2): 1-5.
[13] [ Zhou Hongmin, Hui Gangying, Zhao Zhonghua, et al. Treatment methods of plot boundary trees in spatial forest structure analysis[J]. Scientia Silvae Sinicae, 2009, 45(2): 1-5. ]
[14] 王方. 金沟岭林场落叶松人工林健康评价与经营研究[D]. 北京: 北京林业大学, 2012.
[14] [ Wang Fang. Health Assessment and Management Model of Larch Plantation in Jingouling Forest Farm[D]. Beijing: Beijing Forestry University, 2012. ]
[15] 许俊丽. 基于群落结构、更新能力及土壤质量的上海城市森林健康评价研究[D]. 上海: 华东师范大学, 2018.
[15] [ Xu Junli. Study on Urban Forest Health Evaluation Based on Community Structure, Regeneration Capacity and Soil Quality in Shanghai[D]. Shanghai: East China Normal University, 2018. ]
[16] 马志林, 陈丽华, 于显威, 等. 森林生态系统健康评估模式分析与构建[J]. 内蒙古农业大学学报(自然科学版), 2009, 30(3): 264-270.
[16] [ Ma Zhilin, Chen Lihua, Yu Xianwei, et al. Construct and analysis for healthy assessment mode of forest ecological system[J]. Journal of Inner Mongolia Agricultural University (Natural Science Edition), 2009, 30(3): 264-270. ]
[17] 陈高, 代力民, 姬兰柱, 等. 森林生态系统健康评估Ⅰ. 模式、计算方法和指标体系[J]. 应用生态学报, 2004, 15(10): 1743-1749.
[17] [ Chen Gao, Dai Limin, Ji Lanzhu, et al. Assessing forest ecosystem health I. model, method, and index system[J]. Chinese Journal of Applied Ecology, 2004, 15(10): 1743-1749. ]
[18] 方精云, 王襄平, 沈泽昊, 等. 植物群落清查的主要内容、方法和技术规范[J]. 生物多样性, 2009, 17(6): 533-548.
[18] [ Fang Jingyun, Wang Xiangping, Shen Zehao, et al. Methods and protocols for plant community inventory[J]. Biodiversity Science, 2009, 17(6): 533-548. ]
[19] 国家林业和草原局调查规划设计院.森林资源连续清查技术规程(GB/T 38590-2020)[S]. 国家林业和草原局, 2020.
[19] [ Investigation, Planning and Design Institute of State Forestry and Grassland Administration.Technical Regulations for Continuous Forest Inventory(GB/T 38590-2020)[S]. National Forestry and Grassland Administration, 2020. ]
[20] 惠刚盈, 胡艳波, 刘瑞红. 森林观察研究中的林分空间优势度分析方法[J]. 温带林业研究, 2019, 2(1): 1-6, 12.
[20] [ Hui Gangying, Hu Yanbo, Liu Ruhong. Methods of analyzing stand spatial dominance in forest observational studies[J]. Journal of Temperate Forestry Research, 2019, 2(1): 1-6, 12. ]
[21] 惠刚盈, 张连金, 胡艳波, 等. 林分拥挤度及其应用[J]. 北京林业大学学报, 2016, 38(10): 1-6.
[21] [ Hui Gangying, Zhang Lianjin, Hu Yanbo, et al. Stand crowding degree and its application[J]. Journal of Beijing Forestry University, 2016, 38(10): 1-6. ]
[22] 徐来仙, 姚兰, 郭秋菊, 等. 鄂西南利中盆地马尾松天然次生林森林健康评价[J]. 西南林业大学学报(自然科学), 2021, 41(3): 69-77.
[22] [ Xu Laixian, Yao Lan, Guo Qiuju, et al. Forest health assessment of Pinus massoniana natural secondary forest in Lizhong basin in southwestern Hubei[J]. Journal of Southwest Forestry University(Natural Sciences), 2021, 41(3): 69-77. ]
[23] 谷鑫鑫, 司剑华. 基于层次分析法的西宁市油松人工林健康评价[J]. 青海大学学报, 2020, 38(3): 34-43.
[23] [ Gu Xinxin, Si Jianhua. Health evaluation of Pinus tabuliformis Carr. plantation in Xining City based on analytic hierarchy process[J]. Journal of Qinghai University, 2020, 38(3): 34-43. ]
[24] 赵勇钧, 谢阳生, 王建军, 等. 基于多元统计分析的马尾松人工林健康评价研究——以广西热带林业实验中心为例[J]. 中南林业科技大学学报, 2019, 39(7): 100-107.
[24] [ Zhao Yongjun, Xie Yangsheng, Wang Jianjun, et al. Health assessment of Pinus massoniana plantation on multivariate statistical analysis: A case study of Guangxi tropical forestry experimental center[J]. Journal of Central South University of Forestry and Technology, 2019, 39(7): 100-107. ]
[25] 郑学良, 陈丽华, 李洪洋, 等. 基于水源涵养功能的辽东防护林体系健康评价[J]. 中国水土保持科学, 2020, 18(2): 102-110.
[25] [ Zheng Xueliang, Chen Lihua, Li Hongyang, et al. Health assessment of Liaodong shelterbelt system based on water conservation[J]. Science of Soil and Water Conservation, 2020, 18(2): 102-110. ]
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