三江源植被覆盖变化驱动机制及生态脆弱性分析

doi:10.12118/j.issn.1000-6060.2024.324

Abstract

Abstract:

Investigating changes in vegetation cover, the driving mechanisms behind these changes, and the region’s ecological vulnerability in the Three-River Headwater Region (TRHR), Qinghai Province, China is essential for ensuring its ecological sustainability. Normalized difference vegetation index (NDVI) and kernel normalized difference vegetation index (kNDVI) were used, along with Theil-Sen Median trend analysis, Mann-Kendall significance test, and geographic detectors to explore the spatiotemporal changes in vegetation cover and driving forces. The sensitivity-resilience-pressure (SRP) model was used to assess ecological vulnerability. The results revealed the following trends: (1) From 2001 to 2020, both NDVI and kNDVI in the TRHR showed a fluctuating upward trend. Spatially, areas of improvement were mainly in the northeast and west, covering 73.70% and 79.79%, respectively, while areas of decline were primarily in the central and southern regions, covering 23.23% and 18.18%, respectively. (2) Precipitation, elevation, and temperature were the dominant factors influencing vegetation cover, with interactions among these factors led to bifactor or nonlinear enhancement effects. Precipitation between 573-675 mm and elevations of 3447-3850 m were most favorable for vegetation growth. (3) Ecological vulnerability increased from the southeast to the northwest, showing significant spatial variation. The region exhibited high ecological vulnerability, with areas of severe and extreme vulnerability, as indicated by NDVI and kNDVI, covering 35.38% and 36.85% of the total area, respectively.

Key words: NDVI, kNDVI, temporal and spatial changes of vegetation, ecological vulnerability assessment, geographic detector

LI Kangning, LIN Yilin, ZHAO Junsan, WANG Jian, GE Feng. Driving mechanisms of vegetation change and ecological vulnerability in the Three-River Headwater Region[J].Arid Land Geography, 2025, 48(2): 283-295.

Figures/Tables 14

Fig. 1

Tab. 1

Tab. 2

Tab. 3

Tab. 4

Fig. 2

Fig. 3

Tab. 5

Fig. 4

Tab. 6

Tab. 7

Tab. 8

Fig. 5

Tab. 9

References 38

[1]	张元梅, 孙桂丽, 鲁艳, 等. 气候变化和人类活动对环塔里木盆地植被覆盖度的影响[J]. 东北林业大学学报, 2024, 52(5): 75-81.
	[Zhang Yuanmei, Sun Guili, Lu Yan, et al. Effects of climate change and human activities on vegetation coverage in Ring Tarim Basin[J]. Journal of Northeast Forestry University, 2024, 52(5): 75-81. ]
[2]	Zheng K Y, Tan L S, Sun Y W, et al. Impacts of climate change and anthropogenic activities on vegetation change: Evidence from typical areas in China[J]. Ecological Indicators, 2021, 126: 107648, doi: 10.1016/j.ecolind.2021.107648.
[3]	Yuan J, Xu Y P, Xiang J, et al. Spatiotemporal variation of vegetation coverage and its associated influence factor analysis in the Yangtze River Delta, eastern China[J]. Environmental Science and Pollution Research, 2019, 26: 32866-32879. doi: 10.1007/s11356-019-06378-2
[4]	Li X W, Zulkar H, Wang D Y, et al. Changes in vegetation coverage and migration characteristics of center of gravity in the arid desert region of northwest China in 30 recent years[J]. Land, 2022, 11(10): 1688, doi: 10.3390/land11101688.
[5]	Shammi S A, Meng Q M. Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling[J]. Ecological Indicators, 2021, 121: 107124, doi: 10.1016/j.ecolind.2020.107124.
[6]	Zhang J X, Yang T, Deng M, et al. Spatiotemporal variations and its driving factors of NDVI in northwest China during 2000—2021[J]. Environmental Science and Pollution Research, 2023, 30(56): 118782-118800.
[7]	Alencar A Z, Shimbo J, Lenti F, et al. Mapping three decades of changes in the Brazilian savanna native vegetation using landsat data processed in the google earth engine platform[J]. Remote Sensing, 2020, 12(6): 924, doi: 10.3390/rs12060924.
[8]	Camps-Valls G, Campos-Taberner M, Moreno-Martínez Á, et al. A unified vegetation index for quantifying the terrestrial biosphere[J]. Science Advances, 2021, 7(9): eabc7447, doi: 10.1126/sciadv.abc7447.
[9]	Chen Z G, Shen M G, Jiang N, et al. Daytime warming strengthened delaying effect of precipitation on end of the vegetation growing season on the Tibetan Plateau[J]. Science of the Total Environment, 2023, 892: 164382, doi: 10.1016/j.scitotenv.2023.164382.
[10]	Li X, Xu L, Li M X, et al. High-resolution maps of vegetation nitrogen density on the Tibetan Plateau: An intensive field-investigation[J]. Science of the Total Environment, 2023, 904: 167233, doi: 10.1016/j.scitotenv.2023.167233.
[11]	Liu Y, Tian J, Liu R H, et al. Influences of climate change and human activities on NDVI changes in China[J]. Remote Sensing, 2021, 13(21): 4326, doi: 10.3390/rs13214326.
[12]	Dai Q, Cui C F, Wang S. Spatiotemporal variation and sustainability of NDVI in the Yellow River Basin[J]. Irrigation and Drainage, 2022, 71(5): 1304-1318.
[13]	李敏, 张艳. 黄河流域中段植被覆盖时空变化特征及影响因素分析[J]. 贵州师范大学学报(自然科学版), 2023, 41(1): 10-20, 40.
	[Li Min, Zhang Yan. Temporal and spatial variation characteristics and influencing factors of vegetation cover in the middle Yellow River Basin[J]. Journal of Guizhou Normal University (Natural Sciences Edition), 2023, 41(1): 10-20, 40. ]
[14]	赵慧芳, 曹晓云. 三江源国家公园植被覆盖时空变化及其气候驱动因素[J]. 高原气象, 2022, 41(2): 328-337. doi: 10.7522/j.issn.1000-0534.2021.00091
	[Zhao Huifang, Cao Xiaoyun. Vegetation cover changes and its climate driving in Three-River-Source National Park[J]. Plateau Meteorology, 2022, 41(2): 328-337. ] doi: 10.7522/j.issn.1000-0534.2021.00091
[15]	谢绮丽, 杨鑫, 郝利娜. 2001—2020年三江源区植被覆盖时空变化特征及其影响因素[J]. 水土保持通报, 2022, 42(5): 202-212.
	[Xie Qili, Yang Xin, Hao Lina. Spatio-temporal variation of vegetation cover and its driving factors in Three-River Headwaters Region during 2001—2020[J]. Bulletin of Soil and Water Conservation, 2022, 42(5): 202-212. ]
[16]	Wang J, Zhao J S, Zhou P, et al. Study on the spatial and temporal evolution of NDVI and its driving mechanism based on geodetector and hurst indexes: A case study of the Tibet Autonomous Region[J]. Sustainability, 2023, 15(7): 5981, doi: 10.3390/su15075981.
[17]	Chen C, Li T J, Sivakumar B, et al. Attribution of growing season vegetation activity to climate change and human activities in the Three-River Headwaters Region, China[J]. Journal of Hydroinformatics, 2020, 22(1): 186-204.
[18]	王劲峰, 徐成东. 地理探测器: 原理与展望[J]. 地理学报, 2017, 72(1): 116-134. doi: 10.11821/dlxb201701010
	[Wang Jinfeng, Xu Chengdong. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 2017, 72(1): 116-134. ] doi: 10.11821/dlxb201701010
[19]	Dong Y, Yin D Q, Li X, et al. Spatial-temporal evolution of vegetation NDVI in association with climatic, environmental and anthropogenic factors in the Loess Plateau, China during 2000—2015: Quantitative analysis based on geographical detector model[J]. Remote Sensing, 2021, 13(21): 4380, doi: 10.3390/rs13214380.
[20]	Gao S Q, Dong G T, Jiang X H, et al. Quantification of natural and anthropogenic driving forces of vegetation changes in the Three-River Headwater Region during 1982—2015 based on geographical detector model[J]. Remote Sensing, 2021, 13(20): 4175, doi: 10.3390/rs13204175.
[21]	Wu R N, Wang Y, Liu B Y, et al. Spatial-temporal changes of NDVI in the three northeast provinces and its dual response to climate change and human activities[J]. Frontiers in Environmental Science, 2022, 10: 974988, doi: 10.3389/fenvs.2022.974988.
[22]	Jiang B H, Chen W, Dai X A, et al. Change of the spatial and temporal pattern of ecological vulnerability: A case study on Cheng-Yu urban agglomeration, southwest China[J]. Ecological Indicators, 2023, 149: 110161, doi: 10.1016/j.ecolind.2023.110161.
[23]	陆晴, 廖佳婧, 胡慧敏. 基于SRP模型的红壤丘陵区生态脆弱性评价——以江西省赣南地区为例[J]. 上海国土资源, 2023, 44(3): 100-105, 156.
	[Lu Qing, Liao Jiajing, Hu Huimin. Evaluation of ecological vulnerability in the Gannan region of Jiangxi Province[J]. Shanghai Land & Resources, 2023, 44(3): 100-105, 156. ]
[24]	王成军, 罗昕玥. 基于SRP模型榆林市生态脆弱性评价及时空演变研究[J]. 生产力研究, 2023(10): 56-61.
	[Wang Chengjun, Luo Xinyue. A study on ecological vulnerability evaluation and spatial and temporal evolution of Yulin City based on SRP modeling[J]. Productivity Research, 2023(10): 56-61. ]
[25]	常溢华, 蔡海生. 基于SRP模型的多尺度生态脆弱性动态评价——以江西省鄱阳县为例[J]. 江西农业大学学报, 2022, 44(1): 245-260.
	[Chang Yihua, Cai Haisheng. Dynamic assessment of multi-scale eco-environmental vulnerability based on SRP model in Poyang County[J]. Acta Agriculturae Universitatis Jiangxiensis, 2022, 44(1): 245-260. ]
[26]	樊星, 秦圆圆, 高翔. IPCC第六次评估报告第一工作组报告主要结论解读及建议[J]. 环境保护, 2021, 49(增刊2): 44-48.
	[Fan Xing, Qin Yuanyuan, Gao Xiang. Interpretation and suggestions on the main conclusions of the IPCC sixth assessment report by working group I[J]. Environmental Protection, 2021, 49(Suppl. 2): 44-48. ]
[27]	丁文荣, 李孝川, 陈相标. 珠江源区植被变化特征及其影响因素研究[J]. 人民长江, 2024, 55(3): 83-88, 96.
	[Ding Wenrong, Li Xiaochuan, Chen Xiangbiao. Dynamic characteristics and attribution of vegetation in source region of Pearl River[J]. Yangtze River, 2024, 55(3): 83-88, 96. ]
[28]	Mann H B. Nonparametric tests against trend[J]. Econometrica: Journal of the Econometric Society, 1945: 245-259.
[29]	Wang J F, Li X H, Christakos G, et al. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China[J]. International Journal of Geographical Information Science, 2010, 24(1): 107-127.
[30]	李佳, 彭泰来, 刘寅学, 等. 基于SRP模型的广东省林地生态脆弱性评价[J]. 中南林业调查规划, 2024, 43(1): 33-37.
	[Li Jia, Peng Tailai, Liu Yinxue, et al. Forest ecological vulnerability assessment of Guangdong Province based on SRP model[J]. Central South Forest Inventory and Planning, 2024, 43(1): 33-37. ]
[31]	金丽娟, 许泉立. 基于SRP模型的四川省生态脆弱性评价[J]. 生态科学, 2022, 41(2): 156-165.
	[Jin Lijuan, Xu Quanli. Ecological vulnerability assessment of Sichuan Province based on SRP model[J]. Ecological Science, 2022, 41(2): 156-165. ]
[32]	卓静, 胡皓, 何慧娟, 等. 陕北黄土高原生态脆弱性时空变异及驱动因素分析[J]. 干旱区地理, 2023, 46(11): 1768-1777. doi: 10.12118/j.issn.1000-6060.2023.027
	[Zhuo Jing, Hu Hao, He Huijuan, et al. Spatiotemporal variation and driving factors of ecological vulnerability in the Loess Plateau of northern Shaanxi[J]. Arid Land Geography, 2023, 46(11): 1768-1777. ] doi: 10.12118/j.issn.1000-6060.2023.027
[33]	黄越, 程静, 王鹏. 中国北方农牧交错区生态脆弱性时空演变格局与驱动因素——以盐池县为例[J]. 干旱区地理, 2021, 44(4): 1175-1185. doi: 10.12118/j.issn.1000–6060.2021.04.29
	[Huang Yue, Cheng Jing, Wang Peng. Spatiotemporal evolution pattern and driving factors of ecological vulnerability in agro-pastoral region in northern China: A case of Yanchi County in Ningxia[J]. Arid Land Geography, 2021, 44(4): 1175-1185. ] doi: 10.12118/j.issn.1000–6060.2021.04.29
[34]	贾晶晶, 赵军, 王建邦, 等. 基于SRP模型的石羊河流域生态脆弱性评价[J]. 干旱区资源与环境, 2020, 34(1): 34-41.
	[Jia Jingjing, Zhao Jun, Wang Jianbang, et al. Ecological vulnerability assessment of Shiyang River Basin based on SRP model[J]. Journal of Arid Land Resources and Environment, 2020, 34(1): 34-41. ]
[35]	邰苏日嘎拉, 王永亮, 陈国栋, 等. 基于SRP模型的内蒙古鄂伦春地区生态脆弱性评价[J]. 中国地质, 2024, 51(1): 234-247.
	[Tai Surigala, Wang Yongliang, Chen Guodong, et al. Ecological vulnerability assessment of Oroqen region in the Inner Mongolia based on SRP model[J]. Geology in China, 2024, 51(1): 234-247. ]
[36]	饶品增, 王义成, 王芳. 三江源植被覆盖区NDVI变化及影响因素分析[J]. 草地学报, 2021, 29(3): 572-582. doi: 10.11733/j.issn.1007-0435.2021.03.019
	[Rao Pinzeng, Wang Yicheng, Wang Fang. Analysis on the NDVI change and influence factors of vegetation cover in the Three-River Headwaters Region[J]. Acta Agrestia Sinica, 2021, 29(3): 572-582. ] doi: 10.11733/j.issn.1007-0435.2021.03.019
[37]	张青. 三江源地区植被覆盖时空变化特征及其影响因素研究[D]. 郑州: 郑州大学, 2022.
	[Zhang Qing. Spatial-temporal variation of vegetation cover and its influencing factors in the Three-River Headwaters Region[D]. Zhengzhou: Zhengzhou University, 2022. ]
[38]	Feng X J, Tian J, Wang Y X, et al. Spatio-temporal variation and climatic driving factors of vegetation coverage in the Yellow River Basin from 2001 to 2020 based on kNDVI[J]. Forests, 2023, 14(3): 620, doi: 10.3390/f14030620.

判断区间	交互作用
q(X_n∩X_m)<min[q(X_n), q(X_m)]	非线性减弱
min[q(X_n), q(X_m)]<q(X_n∩X_m)<max[q(X_n), q(X_m)]	单因子非线性减弱
q(X_n∩X_m)>max[q(X_n), q(X_m)]	双因子增强
q(X_n∩X_m)=q(X_n)+q(X_m)	相互独立
q(X_n∩X_m)>q(X_n)+q(X_m)	非线性增强

目标层	准则层	指标层	属性
敏感性（0.7089）	地形因子（0.2024）	海拔（0.1797）	正向
敏感性（0.7089）		坡度（0.0227）	正向
	地表因子（0.0496）	土地利用类型（0.0496）	定性
	气象因子（0.4570）	降水量（0.1943）	负向
		气温（0.1630）	负向
		蒸散发（0.0356）	负向
		PM₁₀（0.0641）	正向
恢复力（0.1786）	植被因子（0.1191）	NDVI/kNDVI（0.1191）	负向
恢复力（0.1786）	多样性因子（0.0595）	生物丰富度指数（0.0595）	负向
压力度（0.1125）	社会因子（0.1125）	人口密度（0.0844）	正向
压力度（0.1125）		夜间灯光（0.0281）	正向

生态脆弱性程度	生态脆弱性指数
生态脆弱性程度	NDVI	kNDVI
微度脆弱	0.087~0.237	0.084~0.253
轻度脆弱	0.237~0.299	0.253~0.316
中度脆弱	0.299~0.359	0.316~0.376
重度脆弱	0.359~0.428	0.376~0.444
极度脆弱	0.428~0.555	0.444~0.555

指标		X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀
NDVI	q	0.513	0.449	0.187	0.034	0.007	0.229	0.493	0.007	0.123	0.078
NDVI	P	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
kNDVI	q	0.504	0.438	0.182	0.036	0.007	0.178	0.500	0.008	0.123	0.062
kNDVI	P	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

降水量/mm	降水量/mm
降水量/mm	0~225	225~280	280~330	330~379	379~427	427~472	472~516	516~573	573~675
0~225
225~280	Y
280~330	Y	Y
330~379	Y	Y	Y
379~427	Y	Y	Y	Y
427~472	Y	Y	Y	Y	Y
472~516	Y	Y	Y	Y	Y	Y
516~573	Y	Y	Y	Y	Y	Y	N
573~675	Y	Y	Y	Y	Y	Y	Y	Y
NDVI	0.143	0.196	0.253	0.302	0.351	0.416	0.445	0.451	0.558
kNDVI	0.045	0.061	0.091	0.117	0.144	0.186	0.204	0.209	0.270

Driving mechanisms of vegetation change and ecological vulnerability in the Three-River Headwater Region

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 38

Related Articles 15

Metrics

Comments

Recommended 0

海拔/m	海拔/m
海拔/m	1956~2979	2979~3447	3447~3850	3850~4176	4176~4436	4436~4665	4665~4894	4894~5181	5181~6824
1956~2979
2979~3447	Y
3447~3850	Y	Y
3850~4176	Y	Y	Y
4176~4436	Y	Y	Y	Y
4436~4665	Y	Y	Y	Y	Y
4665~4894	Y	Y	Y	Y	Y	Y
4894~5181	Y	Y	Y	Y	Y	Y	Y
5181~6824	Y	Y	Y	Y	Y	Y	Y	Y
NDVI	0.262	0.378	0.564	0.536	0.405	0.322	0.277	0.207	0.149
kNDVI	0.096	0.164	0.278	0.257	0.178	0.130	0.105	0.069	0.051

因子类型		NDVI/kNDVI 适宜范围或类型	NDVI	kNDVI
自然因子	降水量	573~675 mm	0.558	0.270
	气温	-5~21 ℃	0.529	0.256
	蒸散发	168~408 mm·月^-1	0.524	0.253
	海拔	3447~3850 m	0.564	0.278
	坡向	西、西北方向	0.349	0.149
	坡度	20°~24°	0.425	0.191
	土壤类型	黄壤	0.580	0.287
人为因子	人口密度	7~47 人·km^-2	0.491	0.231
	夜间灯光	9~16 lm·m^-3	0.449	0.191
	土地利用	森林	0.581	0.292

生态脆弱性等级	NDVI		kNDVI
生态脆弱性等级	面积/km²	比例/%	面积/km²	比例/%
微度脆弱	40895	18.01	37780	16.64
轻度脆弱	51917	22.86	49569	21.83
中度脆弱	53950	23.75	56026	24.68
重度脆弱	52507	23.12	55232	24.33
极度脆弱	27856	12.26	28435	12.52

[1]	ZHANG Haidong, LI Chongbo, MENG Liqi, Adilai SAITINIYAZI, JU Xifeng. Evolutionary characteristics of NDVI in the Kashi Delta and its response to the climate [J]. Arid Land Geography, 2025, 48(2): 296-307.
[2]	CHANG Wenjing, CONG Shixiang, WANG Rongrong, DING Xudong, YU Hailong, HUANG Juying. Quantitative analysis of NDVI changes in Mu Us Sandy Land by climate change and human activities [J]. Arid Land Geography, 2025, 48(1): 63-74.
[3]	LI Junjia, ZHAO Meifeng. Spatial evolution and influencing mechanism of high-quality development in ethnic minority areas of China [J]. Arid Land Geography, 2024, 47(3): 496-505.
[4]	MU Shilei, YANG Yuhuan, Wuritaoketaohu . Spatial differentiation pattern and influencing factors of national desert (rocky desert) parks [J]. Arid Land Geography, 2024, 47(2): 356-368.
[5]	WANG Ziyan, NIU Liqin, CHENG Zhanhong. Temporal and spatial evolution of ecological welfare performance and its influencing factors: A case of Shanxi Province [J]. Arid Land Geography, 2024, 47(12): 2152-2163.
[6]	YANG Xiaoling, DING Wenkui, ZHOU Hua, LI Yanying, CHEN Haibei. Normalized difference vegetation index change and its driving factors in Shiyang River Basin [J]. Arid Land Geography, 2024, 47(10): 1735-1744.
[7]	LI Jianhui, CHEN Lin, DANG Zheng. Spatial pattern and influencing factors of patriotic education bases in the Yellow River Basin [J]. Arid Land Geography, 2023, 46(9): 1536-1544.
[8]	ZHANG Gangdong, BAO Gang, HUANG Xiaojun, YUAN Zhihui, WEN Durina. Asymmetrical warming in winter and spring and its effect on start of growing season and spring NDVI in Mongolia [J]. Arid Land Geography, 2023, 46(8): 1238-1249.
[9]	AI Liya, WANG Yongfang, GUO Enliang, YIN Shan, GU Xiling. NDVI change and its influencing factors of Daqingshan National Nature Reserve based on GEE [J]. Arid Land Geography, 2023, 46(8): 1279-1290.
[10]	ZHANG Hao, HAN Zenglin, QIAO Guorong, WANG Hui, WANG Hongye, DUAN Ye. Patterns and influencing factors of tourism economic linkages between cities in the Yellow River Basin [J]. Arid Land Geography, 2023, 46(8): 1344-1354.
[11]	KANG Ligang, CAO Shengkui, CAO Guangchao, YAN Li, CHEN Lianxuan, LI Wenbin, ZHAO Haoran. Spatiotemporal variation of land surface temperature in Qinghai Lake Basin [J]. Arid Land Geography, 2023, 46(7): 1084-1097.
[12]	LUO Jiayan, ZHANG Jing, XU Mengran, MO Yu, TONG Liga. Vegetation dynamic and its driving force and multi-scenario prediction in Otindag Sandy Land [J]. Arid Land Geography, 2023, 46(4): 614-624.
[13]	ZHU Lei, LI Yannan, HU Jing, TIAN Xiaobo, XU Jiahui, QING Qi. Multi-scale characteristics and influencing mechanism of spatial pattern on research and practice bases in China [J]. Arid Land Geography, 2023, 46(4): 625-635.
[14]	CHENG Jing,WANG Peng,CHEN Hongxiang,HAN Yonggui. Spatiotemporal evolution of habitat quality in the Weihe River Basin and its topographic gradient effects and influencing factors [J]. Arid Land Geography, 2023, 46(3): 481-491.
[15]	LU Chenyu,HUANG Ping,ZHANG Tong,LIU Xiaowan,CHENG Wei. Spatiotemporal evolution and driving factors of the green development efficiency in Gansu Province [J]. Arid Land Geography, 2023, 46(2): 305-315.