哈萨克斯坦首都努尔苏丹人工林健康评价

doi:10.13866/j.azr.2021.05.30

干旱区研究 ›› 2021, Vol. 38 ›› Issue (5): 1474-1483.doi: 10.13866/j.azr.2021.05.30

哈萨克斯坦首都努尔苏丹人工林健康评价

闫晋升^1,^2,³(),王永东^1,³(),娄泊远^1,^2,³,艾柯代·艾斯凯尔^1,^2,³,徐新文^1,³

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

收稿日期:2021-02-23 修回日期:2021-04-12 出版日期:2021-09-15 发布日期:2021-09-24
通讯作者: 王永东
作者简介: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
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

YAN Jinsheng^1,^2,³(),WANG Yongdong^1,³(),LOU Boyuan^1,^2,³,Akida Askar^1,^2,³,XU Xinwen^1,³

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:2021-02-23 Revised:2021-04-12 Online:2021-09-15 Published:2021-09-24
Contact: Yongdong WANG

摘要/Abstract

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+0.038X₇（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。

关键词: 人工林, 因子分析, 聚类分析, 判别分析, 逐步回归分析, 健康评价

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.

Key words: plantations, factor analysis, cluster analysis, discriminant analysis, stepwise regression analysis, health assessment

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.

YAN Jinsheng,WANG Yongdong,LOU Boyuan,Akida Askar,XU Xinwen. Health assessment of plantations in Nursultan, capital of Kazakhstan[J]. Arid Zone Research, 2021, 38(5): 1474-1483.

图/表 7

表1

样地信息表"

样地	经纬度	海拔/m	树种配置	龄组划分	种植年份	林带走向
M₁	50°59'51.39"N,71°24'13.11"E	364	A-P-A-P-A	幼龄林	2003	N-S
M₂	50°59'42.85"N,71°25'33.48"E	351	U-A-U-A	幼龄林	2003	N-S
M₃	50°55'41.98"N,71°23'39.47"E	346	M-A-P-A-P-M	幼龄林	2010	N-S
M₄	50°55'39.56"N,71°23'52.77"E	361	M-S-Po-S-E	幼龄林	2010	N-S
M₅	50°57'45.52"N,71°24'45.64"E	354	F-E-A-A-E-A	幼龄林	2009	NW-SE
M₆	51°10'41.84"N,71°38'52.43"E	436	P-P-P-C-C	成熟林	1970	NW-SE
M₇	51°11'06.17"N,71°39'12.21"E	429	B(天然林)	中龄林	天然林	天然林
M₈	51°11'09.35"N,71°39'08.50"E	418	Co-P-B-P-H	幼龄林	2015	NW-SE
M₉	51°14'10.48"N,71°42'59.20"E	411	M-M-M-M-M-M	过熟林	1963	N-S
M₁₀	51°14'17.69"N,71°43'14.50"E	414	P-P-P-P-P-P	成熟林	1970	NW-SE
M₁₁	51°14'15.15"N,71°43'27.51"E	427	B-B-B-A-Co	幼龄林	2004	NE-SW
M₁₂	51°14'15.56"N,71°43'20.49"E	419	U-P-U-P-U	幼龄林	2015	NE-SW
M₁₃	51°09'11.42"N,71°40'04.05"E	383	R-B-B-B-R	幼龄林	2003	NE-SW
M₁₄	51°09'11.28"N,71°36'58.95"E	382	B(天然林)	中龄林	天然林	天然林
M₁₅	51°09'11.32"N,71°39'47.07"E	385	P-P-P-P-P	幼龄林	2016	NE-SW
M₁₆	51°03'36.37"N,71°57'03.96"E	378	U-B-B-U-E	幼龄林	2004	NE-SW
M₁₇	51°05'38.27"N,71°48'22.11"E	370	A-A-A-B-B	幼龄林	2005	W-E
M₁₈	51°04'09.34"N,71°50'06.25"E	383	B-B-B-A-R	幼龄林	2005	NE-SW
M₁₉	51°33'05.25"N,71°07'13.07"E	442	B-B-B-B-M	幼龄林	2007	NE-SW
M₂₀	51°32'03.96"N,71°05'33.42"E	437	B-B-B-B-B-Co	幼龄林	2007	NE-SW
M₂₁	51°24'24.88"N,71°18'25.51"E	356	E-M-E-Co	幼龄林	2009	NW-SE
M₂₂	51°14'29.25"N,71°39'21.05"E	400	R-R-E-E-E	幼龄林	2004	NE-SW
M₂₃	51°12'03.02"N,71°41'53.49"E	418	P-P-P-Co-A-R-R	幼龄林	2004	W-E
M₂₄	51°11'31.71"N,71°39'26.49"E	429	E-E-E-A-A	幼龄林	2003	N-S
M₂₅	51°11'03.48"N,71°05'12.39"E	356	Co-A-A-A-E	幼龄林	2008	W-E
M₂₆	51°08'03.07"N,71°01'53.97"E	367	Ri-A-U-U-A-Ri	幼龄林	2008	NE-SW
M₂₇	51°05'59.03"N,71°04'17.72"E	349	Ri-U-U-U-U-A	幼龄林	2008	W-E

表1

图1

表2

表3

表4

图2

表5

参考文献 25

[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. doi: 10.1126/science.349.6250.800 pmid: 26293948
[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. doi: 10.1126/science.aac6674 pmid: 26293956
[4]	Trumbore S, Brando P, Hartmann H. Forest health and global change[J]. Science, 2015, 349(6250): 814. doi: 10.1126/science.aac6759 pmid: 26293952
[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.
	[ 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.
	[ 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. doi: 10.1016/j.foreco.2015.12.042
[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. doi: 10.1016/j.ecolind.2016.06.020
[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.
	[ 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.
	[ 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.
	[ 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.
	[ 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.
	[ 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.
	[ 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.
	[ 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. pmid: 15624801
	[ 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. ] pmid: 15624801
[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. doi: 10.3724/SP.J.1003.2009.09253
	[ Fang Jingyun, Wang Xiangping, Shen Zehao, et al. Methods and protocols for plant community inventory[J]. Biodiversity Science, 2009, 17(6): 533-548. ] doi: 10.3724/SP.J.1003.2009.09253
[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.
	[ 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.
	[ 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.
	[ 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.
	[ 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.
	[ 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.
	[ 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.
	[ 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. ]

因子	初始特征值			旋转后的特征值
因子	特征值	方差百分比/%	累积/%	特征值	方差百分比	累积/%
F₁	5.765	41.181	41.181	4.267	30.482	30.482
F₂	2.468	17.630	58.811	3.412	24.374	54.855
F₃	2.223	15.878	74.689	2.760	19.711	74.566
F₄	1.193	8.523	83.212	1.210	8.646	83.212

指标		成分				指标		成分
指标		1	2	3	4	指标		1	2	3	4
Shannon-Wiener指数	X₁	0.216	0.947	-0.008	-0.038	pH	X₈	-0.653	-0.043	-0.126	0.195
Pielou指数	X₂	0.260	0.909	-0.054	-0.036	质量含水量	X₉	-0.329	0.254	0.657	-0.196
Simpson指数	X₃	0.155	0.966	0.118	0.065	平均株高	X₁₀	0.924	0.256	0.048	0.026
FSI	X₄	0.109	0.633	0.388	0.093	平均胸径	X₁₁	0.911	0.230	-0.034	0.118
有机质	X₅	0.512	0.076	0.791	-0.120	平均枝下高	X₁₂	0.940	0.146	0.058	-0.106
全氮	X₆	0.311	0.043	0.867	0.038	平均冠幅	X₁₃	0.806	0.375	0.157	0.373
全磷	X₇	-0.062	-0.011	0.856	0.226	林下更新得分	X₁₄	-0.040	0.028	0.047	0.942

样地	健康综合得分	因子得分				样地	健康综合得分	因子得分
样地	健康综合得分	1	2	3	4	样地	健康综合得分	1	2	3	4
M₁	0.020	-0.233	-0.383	0.329	1.325	M₁₅	-1.590	-0.576	-3.267	-1.454	-0.745
M₂	0.520	0.302	-0.270	0.873	2.675	M₁₆	0.140	-0.028	0.658	-0.997	1.844
M₃	-0.170	-0.627	0.385	-0.229	-0.032	M₁₇	-0.350	-0.308	0.440	-1.405	-0.275
M₄	-0.260	-0.793	-0.029	0.144	0.014	M₁₈	-0.220	-0.158	0.507	-1.246	-0.106
M₅	-0.180	-0.499	-0.277	0.322	0.101	M₁₉	0.210	0.082	-0.305	1.297	-0.411
M₆	0.760	2.395	-0.387	-0.046	0.053	M₂₀	0.170	-0.251	0.229	1.021	-0.459
M₇	0.620	1.254	0.113	0.768	-0.567	M₂₁	0.050	-0.531	0.509	0.835	-0.969
M₈	-0.210	0.206	-0.228	-0.911	-0.063	M₂₂	-0.060	-1.382	1.398	0.817	-1.465
M₉	0.200	-0.107	0.928	0.159	-0.659	M₂₃	-0.350	-0.456	0.445	-1.161	-0.343
M₁₀	0.940	2.896	0.643	-1.083	-0.496	M₂₄	0.070	-0.357	0.491	-0.636	1.971
M₁₁	-0.070	0.129	0.588	-0.858	-0.824	M₂₅	-0.250	-0.999	-0.284	0.954	-0.240
M₁₂	-1.020	-0.299	-2.704	-0.171	-0.796	M₂₆	0.040	-0.546	-0.479	0.873	1.649
M₁₃	-0.050	-0.095	1.167	-1.325	-0.382	M₂₇	0.000	-0.905	0.492	0.944	-0.343
M₁₄	1.050	1.885	-0.380	2.184	-0.459

聚类编号	健康等级	判别函数
聚类编号	健康等级	Y₁	Y₂	Y₃	Y₄
1	Ⅲ	0.993	-1.077	0.094	0.139
2	Ⅱ	4.828	2.122	0.592	-0.204
3	Ⅳ	-1.817	-0.865	-0.224	-0.196
4	Ⅰ	7.600	3.593	-0.663	0.129
5	Ⅴ	-13.536	3.142	0.106	0.129

哈萨克斯坦首都努尔苏丹人工林健康评价

Health assessment of plantations in Nursultan, capital of Kazakhstan

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 25

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	王思淇, 张建军, 张彦勤, 赵炯昌, 胡亚伟, 李阳, 唐鹏, 卫朝阳. 晋西黄土区不同密度刺槐林下植物群落物种多样性[J]. 干旱区研究, 2023, 40(7): 1141-1151.
[2]	张泽宇, 吴晓静, 梁一鹏, 张晓霞, 查同刚. 乌拉山废弃矿山生态恢复的近自然植被空间配置模式[J]. 干旱区研究, 2023, 40(7): 1164-1171.
[3]	许君利, 韩海东, 王建. 新疆大气PM_2.5来源与潜在贡献源分析[J]. 干旱区研究, 2023, 40(6): 874-884.
[4]	张建华, 张琨, 刘勇, 张红, 张凯权, 周晓阳, 徐龙超. 山西省露天煤矿复垦区典型人工林凋落物持水性能研究[J]. 干旱区研究, 2023, 40(12): 2043-2052.
[5]	吴际,杨光,韩雪莹,温雅琴,杨溢文,李文龙,刘一. 不同人工林对奈曼沙区土壤团聚体的影响[J]. 干旱区研究, 2022, 39(6): 1832-1841.
[6]	李雪宁,徐先英,郑桂恒,马全林,张裕年,刘虎俊,赵鹏,杨雪梅. 石羊河下游人工梭梭林健康评价体系构建及应用研究[J]. 干旱区研究, 2022, 39(3): 872-882.
[7]	陈春波,彭建,李刚勇. 新疆草地生态系统健康评价体系构建[J]. 干旱区研究, 2022, 39(1): 270-281.
[8]	刘琳,熊东红,张宝军,袁勇,张闻多. 拉萨河谷杨树人工林枯落物蓄积特征及持水性能[J]. 干旱区研究, 2021, 38(6): 1674-1682.
[9]	李浩,胡婵娟,赵荣钦,郭雷,满洲. 黄土丘陵区典型人工林的根系分布特征[J]. 干旱区研究, 2021, 38(5): 1420-1428.
[10]	张立恒, 李清雪, 王学全, 贾志清, 李少华. 高寒沙区中间锦鸡儿人工林细根动态及其周转 [J]. 干旱区研究, 2020, 37(1): 212-219.
[11]	张晓梅, 邸利, 史再军, 费俊娥, 王正安. 甘肃泾川中沟小流域不同坡位刺槐人工林土壤水分特征 [J]. 干旱区研究, 2019, 36(5): 1300-1308.
[12]	于德良, 雷泽勇, 张岩松, 于东伟, 周晏平, 姜吉文. 沙地樟子松人工林土壤酶活性及其影响因子 [J]. 干旱区研究, 2019, 36(3): 621-629.
[13]	孔涛, 张莹, 雷泽勇, 王东丽, 刘洋, 于吉, 吴丹. 沙地樟子松人工林土壤氮矿化特征#br#[J]. 干旱区研究, 2019, 36(2): 296-306.
[14]	杨尧军, 袁宏波, 刘淑娟, 张锦春, 唐进年, 丁峰. 库姆塔格沙漠沙生柽柳种群与环境因子分析[J]. 干旱区研究, 2013, 30(5): 827-831.
[15]	陈东强, 王让会. 准噶尔盆地人工林地土壤全盐的高光谱反演[J]. 干旱区研究, 2013, 30(3): 444-448.