Spatiotemporal changes of vegetation greenness and their influencing factors in the Qinghai Lake Basin

doi:10.7522/j.issn.1000-694X.2025.00126

Journal of Desert Research ›› 2025, Vol. 45 ›› Issue (5): 289-300.DOI: 10.7522/j.issn.1000-694X.2025.00126

Previous Articles Next Articles

Spatiotemporal changes of vegetation greenness and their influencing factors in the Qinghai Lake Basin

Mingyu Wang¹(), Chengyong Wu²^,³, Kelong Chen¹^,²()

^1.School of Geographical Sciences / Key Laboratory of Physical Geography and Environmental Processes of Qinghai Province / Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation，Ministry of Education，Qinghai Normal University，Xining 810008，China
^2.Qinghai Qinghai Lake Wetland Ecosystem National Positioning Observation and Research Station，Haibei 812300，Qinghai，China
^3.School of Resources and Environmental Engineering，Tianshui Normal University，Tianshui 741001，Gansu，China

Received:2025-04-14 Revised:2025-06-16 Online:2025-09-20 Published:2025-09-27
Contact: Kelong Chen

Abstract

Abstract:

To investigate the spatiotemporal changes in vegetation greenness in the Qinghai Lake Basin and their responses to climate change and human activities， this study analyzed MODIS Normalized Difference Vegetation Index （NDVI） data and meteorological data from 2000 to 2023. Methods such as Sen+Mann-Kendall trend analysis， Hurst index， correlation analysis， and residual analysis were employed to examine the spatiotemporal patterns and future trends of vegetation greenness in the basin. The degree of influence of climate change and human activities on vegetation greenness was also assessed. The results show：（1） From 2000 to 2023， the annual average NDVI in the Qinghai Lake Basin was 0.45， with a significant increasing trend over time at a rate of 0.0022 per year. Areas showing improvement accounted for 86.2% of the study region， exhibiting a significant upward trend and distinct spatial heterogeneity. （2） Vegetation greenness generally remained relatively stable， with a coefficient of variation ranging from 0 to 1.7 and an average of 0.08， indicating a positive development trend. （3） Vegetation greenness in the Qinghai Lake Basin was positively correlated with both precipitation （r=0.196， P<0.05） and temperature （r=0.07， P<0.05）， with precipitation having a stronger influence than temperature. Precipitation was the dominant driver in 85.5% of the area. （4） Changes in vegetation greenness in 63.6% of the Qinghai Lake Basin were jointly influenced by climate change and human activities， with human activities accounting for a relative contribution rate of 85.02%.

Key words: vegetation greenness, spatiotemporal changes, climate change, human activities, Qinghai Lake Basin

CLC Number:

Q948.15+6

Mingyu Wang, Chengyong Wu, Kelong Chen. Spatiotemporal changes of vegetation greenness and their influencing factors in the Qinghai Lake Basin[J]. Journal of Desert Research, 2025, 45(5): 289-300.

Figures/Tables 16

Fig.1 Schematic diagram of the study area

Table 1 Criteria for determining the coefficient of variation of vegetation greenness

变异系数	C_v<0.05	0.05≤C_v<0.10	0.10≤C_v<0.15	0.15≤C_v<0.20	C_v≥0.20
稳定性	低波动	中低波动	中波动	中高波动	高波动

Table 2 Criteria and contribution rates of the driving factors for vegetation greenness

Slope(NDVI_obs)	驱动因素	驱动因素判定标准		驱动因素的贡献率/%
Slope(NDVI_obs)	驱动因素	Slope(NDVI_CC)	Slope(NDVI_HA)	气候变化	人类活动
>0	CC&HA	>0	>0	$S l o p e (N D V I C C) S l o p e (N D V I o b s)$	$S l o p e (N D V I H A) S l o p e (N D V I o b s)$
	CC	>0	<0	100	0
	HA	<0	>0	0	100
<0	CC&HA	<0	<0	$S l o p e (N D V I C C) S l o p e (N D V I o b s)$	$S l o p e (N D V I H A) S l o p e (N D V I o b s)$
	CC	<0	>0	100	0
	HA	>0	<0	0	100

Table 2 Criteria and contribution rates of the driving factors for vegetation greenness

Slope(NDVI_obs)	驱动因素	驱动因素判定标准		驱动因素的贡献率/%
Slope(NDVI_obs)	驱动因素	Slope(NDVI_CC)	Slope(NDVI_HA)	气候变化	人类活动
>0	CC&HA	>0	>0	$S l o p e (N D V I C C) S l o p e (N D V I o b s)$	$S l o p e (N D V I H A) S l o p e (N D V I o b s)$
	CC	>0	<0	100	0
	HA	<0	>0	0	100
<0	CC&HA	<0	<0	$S l o p e (N D V I C C) S l o p e (N D V I o b s)$	$S l o p e (N D V I H A) S l o p e (N D V I o b s)$
	CC	<0	>0	100	0
	HA	>0	<0	0	100

Fig.2 The interannual variation trend of vegetation greenness in the Qinghai Lake Basin from 2000 to 2023

Fig.3 Spatial distribution of the multi-year average vegetation greenness in the Qinghai Lake Basin from 2000 to 2023

Fig.4 Spatial distribution of the vegetation greenness change trend and its significance test in the Qinghai Lake Basin from 2000 to 2023

Table 3 Significance test of the vegetation greenness change trend in the Qinghai Lake Basin from 2000 to 2023

变化趋势Slope	显著性水平P	变化趋势	像元数	比例/%
<-0.0005	<0.01	极显著减少	3 957	0.97
	0.01~0.05	显著减少	55	0.01
	≥0.05	不显著减少	11 622	3
-0.005~0.0005	≥0.05	无变化	39 226	9.8
>0.0005	≥0.05	不显著增加	120 304	30
	0.01~0.05	显著增加	900	0.22
	<0.01	极显著增加	224 374	56

Fig.5 Vegetation greenness stability and sustainability in the Qinghai Lake Basin from 2000 to 2023

Table 4 Persistence of vegetation greenness changes in the Qinghai Lake Basin from 2000 to 2023

变化趋势值slope	Hurst指数	未来变化趋势	像元数	比例/%
<-0.0005	0.5~1	持续性退化	4 018	1
	0~0.5	反持续性退化（改善）	285 864	71.34
-0.0005~0.0005	0~1	基本不变	40 182	10
>0.0005	0~0.5	反持续性改善（退化）	11 563	2.98
	0.5~1	持续性改善	58 811	14.68

Fig.6 Results of correlation and significance test between vegetation greenness and precipitation

Fig.7 Results of correlation and significance test between vegetation greenness and air temperature

Fig.8 Spatial distribution of the residual variation trend of vegetation greenness in the Qinghai Lake Basin

Table 5 Residual trend statistics

残差趋势值Sr	人类活动影响	像元数	比例/%
<-0.0005	抑制	22 401	5.8
-0.0005~0.0005	无影响	45 342	11.4
>0.0005	促进	332 665	82.8

Fig.9 The contribution rates of climate change and human activities to vegetation greenness change

Fig.10 Spatial distribution of driving factors for vegetation greenness changes in the Qinghai Lake Basin from 2000 to 2023

Table 6 Statistics of driving factors for vegetation greenness changes in the Qinghai Lake Basin from 2000 to 2023

Slope (NDVI_obs)	驱动因素	像元数	比例/%
>0	气候变化和人类活动	253 592	63.6
	气候变化	6 320	1.5
	人类活动	111 006	27.8
<0	气候变化和人类活动	14 169	3.4
	气候变化	3 317	0.7
	人类活动	12 034	3.0

References 59

[1]	Qiu B， Yan X， Chen C，et al.The impact of indicator selection on assessment of global greening［J］.GIScience & Remote Sensing，2021，58（3）：1-14.
[2]	Zhang Y， Song C， Band L E，et al.Reanalysis of global terrestrial vegetation trends from MODIS products：browning or greening？［J］.Remote Sensing of Environment，2017，191：145-155.
[3]	Cong N， Wang T， Nan H，et al.Changes in satellite‐derived spring vegetation green‐up date and its linkage to climate in China from 1982 to 2010：a multimethod analysis［J］.Global Change Biology，2013，19（3）：881-891.
[4]	曹永强，周姝含，杨雪婷.近20年辽宁省植被动态特征及其对气候变化的响应［J］.生态学报，2022，42（14）：5966-5979.
[5]	刘爽，宫鹏.2000-2010年中国地表植被绿度变化［J］.科学通报，2012，57（16）：1423-1434.
[6]	梁顺林，白瑞，陈晓娜，等.2019年中国陆表定量遥感发展综述［J］.遥感学报，2020，24（6）：618-671.
[7]	章钊华，赵书河，丛佃敏，等.基于遥感的泰山地区植被绿度趋势变化研究［J］.地理空间信息，2018，16（7）：65-68.
[8]	刘海，刘凤，郑粮.气候变化及人类活动对黄河流域植被覆盖变化的影响［J］.水土保持学报，2021，35（4）：143-151.
[9]	张志强，刘欢，左其亭，等.2000-2019年黄河流域植被覆盖度时空变化［J］.资源科学，2021，43（4）：849-858.
[10]	杨达，易桂花，张廷斌，等.青藏高原植被生长季NDVI时空变化与影响因素［J］.应用生态学报，2021，32（4）：1361-1372.
[11]	金凯.中国植被覆盖时空变化及其与气候和人类活动的关系［D］.杨凌：西北农林科技大学，2019.
[12]	余志巍，刘强，张宇阳，等.内蒙古不同草地NDVI变化及其驱动要素［J］.生态学报，2024，44（22）：10068-10082.
[13]	李双双，段生勇，胡佳岚，等.黄土高原植被变化主导空间模态及其影响因素［J］.地理学报，2024，79（7）：1768-1786.
[14]	张江，袁旻舒，张婧，等.近30年来青藏高原高寒草地NDVI动态变化对自然及人为因子的响应［J］.生态学报，2020，40（18）：6269-6281.
[15]	钟诗瑶，李传华，乔鹏飞.2000-2020年干旱梯度下西北干旱半干旱区植被突变及归因［J］.中国沙漠，2025，45（2）：275-283.
[16]	任涵玉，温仲明，刘洋洋，等.我国北方草地净初级生产力时空动态特征及其与水热因子的关系［J］.草地学报，2021，29（8）：1779-1792.
[17]	涂又，姜亮亮，刘睿，等.1982-2015年中国植被NDVI时空变化特征及其驱动分析［J］.农业工程学报，2021，37（22）：75-84.
[18]	刘慧丽，陈浩，董廷旭，等.川渝地区NDVI动态特征及其对气候变化的响应［J］.生态学报，2023，43（16）：6743-6757.
[19]	Liu Y， Li Y， Li S，et al.Spatial and temporal patterns of global NDVI trends：correlations with climate and human factors［J］.Remote Sensing，2015，7（10）：13233-13250.
[20]	路建兵，鞠珂，廖伟斌.2000-2020年甘肃省植被覆盖特征及其对气候变化和人类活动的响应［J］.中国沙漠，2023，43（4）：118-127.
[21]	燕丹妮，武心悦，王博恒，等.1982-2015年黄土高原植被变化特征及归因［J］.生态学报，2023，43（23）：9794-9804.
[22]	朱士华，方霞，杭鑫，等.中亚草地植被指数（NDVI）对气候变化及人类活动的响应［J］.中国沙漠，2022，42（4）：229-241.
[23]	张倩，曹广超，张乐乐，等.祁连山南坡植被绿度时空变化及其对气候变化和人类活动的响应［J］.干旱区研究，2024，41（12）：2143-2153.
[24]	Sun Y， Yang Y， Zhang L，et al.The relative roles of climate variations and human activities in vegetation change in North China［J］.Physics and Chemistry of the Earth，2015，87-88：12.
[25]	陶洁怡，章锦河，郭丽佳，等.三江源国家公园水源涵养服务时空演变特征及其驱动因素［J］.生态学报，2025，45（3）：1226-1238.
[26]	徐晓梅，王军农.青海湖流域水文要素初步分析［J］.科学技术与工程，2012，12（29）：7669-7673.
[27]	Chen H， Zhu Q， Peng C，et al.The impacts of climate change and human activities on biogeochemical cycles on the Qinghai‐Tibetan Plateau［J］.Global Change Biology，2013，19（10）：2940-2955.
[28]	杨元合，朴世龙.青藏高原草地植被覆盖变化及其与气候因子的关系［J］.植物生态学报，2006，30（1）：1-8.
[29]	吴恒飞，陈克龙，张乐乐，等.增温对青海湖流域高寒沼泽草甸主要温室气体通量的影响［J］.草原与草坪，2021，41（4）：1-9.
[30]	李星玥，杜岩功，陈克龙，等.青海湖流域南岸温性草原CO₂排放特征对模拟降雨变化的响应［J］.草地学报，2023，31（8）：2496-2504.
[31]	韩艳莉，陈克龙，于德永.土地利用变化对青海湖流域生境质量的影响［J］.生态环境学报，2019，28（10）：2035-2044.
[32]	陈克龙.基于生态健康的青海湖流域植被生态补偿标准研究［D］.西宁：青海师范大学，2016.
[33]	拓进宝.2000-2016年青海湖流域NDVI时空变化及其与环境因子的关系［D］.西宁：青海师范大学，2019.
[34]	张乐乐，高黎明，陈克龙.高分辨率遥感降水资料在青海湖流域及周边区域的适用性评价［J］.水文，2020，40（5）：15-21.
[35]	刘芙梅，吴成永，陈克龙.青海湖流域植被覆盖时空变化特征及驱动力分析［J］.草原与草坪，2024，44（1）：1-12.
[36]	陈亚荣，李星玥，陈克龙.布哈河流域不同水体稳定同位素特征分析［J］.水文，2025，45（1）：83-89.
[37]	高小红，王一谋，冯毓荪，等.基于遥感与GIS的青海湖地区土地利用变化及其对生态环境影响的研究［J］.遥感技术与应用，2002（6）：304-309.
[38]	Sen P K.Estimates of the regression coefficient based on Kendall's Tau［J］.Journal of the American Statistical Association，1968，63：324.
[39]	刘智源，李继红.2000-2020年黑龙江省植被时空变化对气候因子响应［J］.森林工程，2024，40（1）：85-97.
[40]	孙锐，陈少辉，苏红波.2000-2016年黄土高原不同土地覆盖类型植被NDVI时空变化［J］.地理科学进展，2019，38（8）：1248-1258.
[41]	牟禹恒，黄义忠，梁睿，等.基于MODIS NDVI的文山州植被覆盖度及景观格局分析［J］.西北林学院学报，2023，38（1）：174-180.
[42]	秦格霞，芦倩，孟治元，等.1982-2015年中国北方草地NDVI时空动态及其对气候变化的响应［J］.水土保持研究，2021，28（1）：101-108.
[43]	Jiang W， Yuan L， Wang W，et al.Spatio‐temporal analysis of vegetation variation in the Yellow River Basin［J］.Ecological Indicators，2015，51：117-126.
[44]	陈怡洁，陈瑜.黄河流域甘肃段NDVI时空变化及其影响因素分析［J］.测绘与空间地理信息，2024，47（3）：89-92.
[45]	岳萌，耿广坡，王涛，等.2000-2019年黄河流域陕西段植被NDVI时空变化及其驱动因素分析［J］.水土保持研究，2023，30（2）：238-246.
[46]	程兀杰，孟妮娜，蔡昕楠，等.陕西省NDVI时空变化及其对气候和人类活动的响应［J］.人民黄河，2023，45（4）：28-34.
[47]	刘芙梅.青海湖流域地表绿度变化特征及其驱动力分析［D］.西宁：青海师范大学，2023.
[48]	陈昀琳.基于Landsat和MODIS NDVI时序数据的青海湖流域植被覆盖度提取及其变化分析［D］.北京：中国地质大学，2019.
[49]	杨亮，刘丽男，孙少波.1982-2015年青藏高原植被变化的主导环境因子［J］.生态学报，2023，43（2）：744-755.
[50]	韩艳莉.气候与景观格局变化对青海湖流域生态系统服务的影响［D］.西宁：青海师范大学，2021.
[51]	陈克龙，韩艳丽，曹生奎.青海湖流域植被生态补偿标准初探［J］.青海师范大学学报（自然科学版），2012，28（1）：21-24.
[52]	王逸玮.青藏高原沱沱河源多年冻土区NDVI时空变化及影响因素分析［D］.南京：南京信息工程大学，2024.
[53]	卓嘎，陈思蓉，周兵.青藏高原植被覆盖时空变化及其对气候因子的响应［J］.生态学报，2018，38（9）：3208-3218.
[54]	潘虹，顾海敏，史建桥，等.基于RS和GIS的青海湖流域植被覆盖度变化与驱动因子研究［J］.资源开发与市场，2016，32（7）：827-831.
[55]	李焱.2000-2020年藏西南高原植被覆盖时空变化及其影响因素［D］.兰州：兰州大学，2022.
[56]	李永格，李宗省，冯起，等.基于生态红线划定的祁连山生态保护性开发研究［J］.生态学报，2019，39（7）：2343-2352.
[57]	杜凯，康宇坤，张德罡，等.放牧模式对祁连山东缘高寒草甸植被特征的影响［J］.草原与草坪，2021，41（3）：9-18.
[58]	马昊翔，陈长成，宋英强，等.青海省近10年草地植被覆盖动态变化及其驱动因素分析［J］.水土保持研究，2018，25（6）：137-145.
[59]	田海静.非气候因素引起的中国植被变化遥感诊断［D］.北京：中国科学院遥感与数字地球研究所，2017.

Spatiotemporal changes of vegetation greenness and their influencing factors in the Qinghai Lake Basin

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 59

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Haojiang Bai, Yongqing Luo, Li Cheng, Zhengjiaoyi Wang. The phenological responses of the dominant shrubs Artemisia halodendron and Caragana microphylla in the Horqin Sandy Land to climate change [J]. Journal of Desert Research, 2025, 45(4): 305-313.
[2]	Ying Zhai, Jiangli Pang, Chunchang Huang, Xiaochun Zha, Yali Zhou, Yuqin Li, Yuzhu Zhang, Xueqing Sun, Xiaokang Zhao. Particle endmembers characteristics of Amiola-South profile in the Zoige Basin and recorded climate change since 15 ka BP [J]. Journal of Desert Research, 2025, 45(2): 111-118.
[3]	Shiyao Zhong, Chuanhua Li, Pengfei Qiao. Vegetation abrupt changes and attribution in the arid and semi-arid regions of Northwest China under aridity gradients from 2000 to 2020 [J]. Journal of Desert Research, 2025, 45(2): 275-283.
[4]	Xingyu Cheng, Zhiwei Xu, Yan Yu, Xiaoxiao Zhang. Changes in frequency and possible causes of dust occurrence in northern China and Mongolia since 2001 revealed by remote sensing [J]. Journal of Desert Research, 2025, 45(2): 47-60.
[5]	Zhenliang Yin, Rui Zhu, Chunshuang Fang, Huaqing Yang, Zexia Chen. Attribution analysis of runoff variations in the Changma River Basin based on the Budyko hypothesis [J]. Journal of Desert Research, 2024, 44(6): 110-121.
[6]	Sen Li, Zongying Yang, Hongyan Zhao, Narentuya, Guixiang An, Jiali Xie, Xiaopeng Jia, Changzhen Yan. Spatio-temporal changes of aeolian desertification in the Jiziwan of the Yellow River from 1975 to 2020 [J]. Journal of Desert Research, 2024, 44(5): 13-22.
[7]	Lianxuan Chen, Shengkui Cao, Guangchao Cao, Yizhen Lei, Haoran Zhao, Wenbin Li. Temporal and spatial response characteristics of evapotranspiration to soil water content in Qinghai Lake Basin [J]. Journal of Desert Research, 2024, 44(3): 202-212.
[8]	Zhenzi He, Bingxin Xu, Wenjing Liu, Yigang Hu. Carbon exchange of desert soil crust in response to simulated warming and changes of precipitation [J]. Journal of Desert Research, 2024, 44(3): 269-278.
[9]	Jianhui Ge, Bing Liu, Yujie Xu, Aijun Sun, Keqi Wang, Dongxue Li, hui Zhao. Interrelationships between climate change and surface processes in the First Meander of the Yellow River since the Last Deglaciation [J]. Journal of Desert Research, 2024, 44(2): 121-132.
[10]	Yang Chen, Ping Lv, Min Cao, Zishu Xia, Fang Ma, Junlin Yu. Microclimate characteristics of Tengger Desert lakes and its response to climate change [J]. Journal of Desert Research, 2024, 44(2): 231-238.
[11]	Qing Li, Na Zhou, Sheng Wang, Tongzhou Li, Rende Wang, Jinfeng Wang. Quantitative assessment the impacts of climate change and human actives on wind erosion： a case study of Inner Mongolia Autonomous Region [J]. Journal of Desert Research, 2024, 44(1): 178-188.
[12]	Chengwei Zheng, Xiaohong Deng, Zongxing Li, Jian Xue, Libang Ma, Juan He, Shuxiang Lu, Jianxiong Shao, Shiyu Cai, Peiyi Zhao. Analysis of the spatial and temporal evolution of water resources conservation and human activity intensity in the Hexi region of Gansu Province [J]. Journal of Desert Research, 2024, 44(1): 189-200.
[13]	Xiao Feng, Jianjun Qu, Xinhui Ding, Qin Tian, Qingbin Fan. Temporal and spatial pattern of NPP in Yulin and its influencing factors during the desertification reversal [J]. Journal of Desert Research, 2024, 44(1): 22-32.
[14]	Yanzhuo Zhao, Yuanyun Xie, Chunguo Kang, Yunping Chi, Lei Sun, Peng Wu, Zhenyu Wei. Holocene climate change recorded by paleosoil profile in Hulun Buir Sandy Land [J]. Journal of Desert Research, 2023, 43(5): 85-96.
[15]	Jianbing Lu, Ke Ju, Weibin Liao. Variation in NDVI and its response to climate change and human activities in Gansu Province during 2000-2020 [J]. Journal of Desert Research, 2023, 43(4): 118-127.