AccScience Publishing / AJWEP / Online First / DOI: 10.36922/AJWEP025350269
Submit to AJWEP
Cite this article
2
Download
45
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ORIGINAL RESEARCH ARTICLE

Spatiotemporal dynamics of fractional vegetation cover and its relationship with climatic factors in the Yarkand River Basin

Guoqiang Qin1,2* ,  Kai Yuan1,2 ,  Guoliang Ding3 ,  Qiang Guo1,2 ,  Runbo Wang1,2
Show Less
1 Department of Hydraulic Engineering Management, College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang, China
2 Xinjiang Key Laboratory of Water Engineering Safety and Disaster Mitigation, College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang, China
3 Department of Environmental Protection Engineering, XPCC Surveying and Designing Institute Group Co., Ltd., Urumqi, Xinjiang, China
AJWEP, 025350269 https://doi.org/10.36922/AJWEP025350269
Received: 25 August 2025 | Revised: 23 September 2025 | Accepted: 28 September 2025 | Published online: 24 October 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
XML
Cite
Abstract

The Yarkand River Basin, an ecologically fragile zone in arid northwest China, is critical for regional ecological management due to its sensitivity to environmental changes. This study examines the spatiotemporal dynamics of fractional vegetation cover (FVC) from 2000 to 2023 and its correlation with climatic factors, using the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index data and climate observations (temperature and precipitation). FVC was estimated using the pixel dichotomy method, with Sen+Mann–Kendall trend analyses, and Pearson correlation was applied to assess temporal trends and climate-vegetation relationships. MODIS land use data were reclassified to evaluate FVC variations across forestland, grassland, farmland, bare land, and other ecological types. Results revealed significant spatiotemporal heterogeneity in FVC. Spatially, Yecheng County exhibited higher FVC than Bachu County, driven by favorable topography. Temporally, FVC showed a significant upward trend post-2000, particularly in grasslands and croplands, stabilizing between 2010 and 2023. Climate analysis indicated divergent responses: farmland and forest FVC were negatively correlated with temperature (ranging from 8°C to over 9°C). In contrast, grassland and forest FVC were positively associated with precipitation (increasing by ~14 mm). A 1–2-month lag effect was observed in precipitation’s impact on FVC. The Hurst index suggested a sustained FVC growth in most regions. These findings highlight the role of climate change in driving FVC dynamics, providing a scientific basis for ecological conservation and sustainable water resource management in arid regions.

Keywords
Yarkand River Basin
Fractional vegetation cover
Land use types
Climatic factors
Ecological management
Funding
This work was supported by the Sub-project of the Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region, China (Grant Number 2023A02012-1-3), titled “Research on the System for Optimal Allocation of Water Resources at the Watershed Scale.”
Conflict of interest
The authors declare that they have no conflicts of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
References
  1. Tucker CJ, Pinzon JE, Brown ME, et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens. 2005;26(20):4485-4498. doi: 10.1080/01431160500168686

 

  1. Fensholt R, Langanke T, Rasmussen K, et al. Greenness in semi-arid areas across the globe 1981–2007 - an Earth Observing Satellite based analysis of trends and drivers. Remote Sens Environ. 2012;121:144-158. doi: 10.1016/j.rse.2012.01.017

 

  1. Piao S, Wang X, Park T, et al. Characteristics, drivers and feedbacks of global greening. Nat Rev Earth Environ. 2019;1(1):14-27. doi: 10.1038/s43017-019-0001-x

 

  1. Xu M, Wu H, Kang S, Chen X, Wang Y. Climate change decreased the effect of meltwater on cotton production in the Yarkant river basin of arid northwest China. Irrig Sci. 2024;42(1):99-114. doi: 10.1007/s00271-023-00862-x

 

  1. Zhan C, Liang C, Zhao L, et al. Vegetation dynamics and its response to climate change in the yellow river Basin, China. Front Environ Sci. 2022;10:892747. doi: 10.3389/fenvs.2022.892747

 

  1. Komissarov A, Komissarov M, Safin K, Ishbulatov M, Kovshov Y. Long-term irrigation effect on soil and vegetation cover of floodplain estuaries in the Southern Urals. Asian J Water Environ Pollut. 2020;17(1):83-90. doi: 10.3233/AJW200009

 

  1. Chen Y, Li Z, Fan Y, Wang H, Deng H. Progress and prospects of climate change impacts on hydrology in the arid region of northwest China. Environ Res. 2015;139:11-19. doi: 10.1016/j.envres.2014.12.029

 

  1. Li Z, Chen Y, Li W, Deng H, Fang G. Potential impacts of climate change on vegetation dynamics in Central Asia. JGR Atmos. 2015;120(24):12345-12356. doi: 10.1002/2015JD023618

 

  1. Lioubimtseva E, Cole R, Adams JM, Kapustin G. Impacts of climate and land-cover changes in arid lands of Central Asia. J Arid Environ. 2005;62(2):285-308. doi: 10.1016/j.jaridenv.2004.11.005

 

  1. Xiao J, Jin Z, Wang J. Assessment of the hydrogeochemistry and groundwater quality of the Tarim river basin in an extreme arid region, NW China. Environ Manag. 2014;53(1):135-146. doi: 10.1007/s00267-013-0198-2

 

  1. Deng H, Chen Y, Wang H, Zhang S. Climate change with elevation and its potential impact on water resources in the Tianshan Mountains, Central Asia. Global Planet Change. 2015;135:28-37. doi: 10.1016/j.gloplacha.2015.09.015

 

  1. Chen Z, Chen Y, Li B. Quantifying the effects of climate variability and human activities on runoff for Kaidu River Basin in arid region of northwest China. Theor Appl Climatol. 2013;111(3-4):537-545. doi: 10.1007/s00704-012-0680-4

 

  1. Zhang Q, Gu X, Singh VP, Shi P, Sun P. More frequent flooding? Changes in flood frequency in the Pearl River basin, China, since 1951 and over the past 1000 years. Hydrol Earth Syst Sci. 2018;22(5):2637-2653. doi: 10.5194/hess-22-2637-2018

 

  1. Amuti T, Luo G. Analysis of land cover change and its driving forces in a desert oasis landscape of Xinjiang, northwest China. Solid Earth. 2014;5(2):1071-1085. doi: 10.5194/se-5-1071-2014

 

  1. Zhao Y, Cao B, Sha L, et al. Land use and cover change and influencing factor analysis in the Shiyang River Basin, China. J Arid Land. 2024;16(2):246-265. doi: 10.1007/s40333-024-0071-6

 

  1. Xu J, Chen Y, Li W, Nie Q, Hong Y, Yang Y. The nonlinear hydro-climatic process in the Yarkand River, northwestern China. Stoch Environ Res Risk Assess. 2013;27(2):389-399. doi: 10.1007/s00477-012-0606-9

 

  1. Bhambri R, Bolch T, Chaujar RK, Kulshreshtha SC. Glacier changes in the Garhwal Himalaya, India, from 1968 to 2006 based on remote sensing. J Glaciol. 2011;57(203):543-556. doi: 10.3189/002214311796905604

 

  1. Fu A, Li W, Wang Y, Bai Y. Basic and target eco-environment water requirements of a dry inland river under typical flow frequencies in China. PeerJ. 2020;8:e8285. doi: 10.7717/peerj.8285

 

  1. Hou Y, Chen Y, Ding J, Li Z, Li Y, Sun F. Ecological impacts of land use change in the arid Tarim river Basin of China. Remote Sens. 2022;14(8):1894. doi: 10.3390/rs14081894

 

  1. Yi Y, Liu S, Zhu Y, Wu K, Xie F, Saifullah M. Spatiotemporal heterogeneity of snow cover in the central and western Karakoram Mountains based on a refined MODIS product during 2002–2018. Atmos Res. 2021;250:105402. doi: 10.1016/j.atmosres.2020.105402

 

  1. Sulla-Menashe D, Friedl MA. User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product. Available from: https://lpdaac.usgs.gov/ documents/101/MCD12_User_Guide_V6.pdf [Last accessed on 2025 Apr 13]. doi: 10.5067/MODIS/MCD12Q1.006

 

  1. Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ. 2017;202:18-27. doi: 10.1016/j.rse.2017.06.031

 

  1. Kaur A, Ghosh S, Das SK. Satellite image-based land use/land cover dynamics and forest cover change analysis (1996-2016) in Odisha, India. Asian J Water Environ Pollut. 2019;16(1):25-39. doi: 10.3233/AJW190004

 

  1. Li F, Chen W, Zeng Y, Zhao Q, Wu B. Improving estimates of grassland fractional vegetation cover based on a pixel dichotomy model: A case study in inner Mongolia, China. Remote Sens. 2014;6(6):4705-4722. doi: 10.3390/rs6064705

 

  1. Jiapaer G, Chen X, Bao A. A comparison of methods for estimating fractional vegetation cover in arid regions. Agric Forest Meteorol. 2011;151(12):1698-1710. doi: 10.1016/j.agrformet.2011.07.004

 

  1. Shi S, Yu J, Wang F, Wang P, Zhang Y, Jin K. Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau. Sci Total Environ. 2021;755:142419. doi: 10.1016/j.scitotenv.2020.142419

 

  1. Li X, Liu Y, Wang L. Change in fractional vegetation cover and its prediction during the growing season based on machine learning in southwest China. Remote Sens. 2024;16(19):3623. doi: 10.3390/rs16193623

 

  1. Fu B, Yang W, Yao H, et al. Evaluation of spatio-temporal variations of FVC and its relationship with climate change using GEE and Landsat images in Ganjiang River Basin. Geocarto Int. 2022;37(26):13658-13688. doi: 10.1080/10106049.2022.2082551

 

  1. Zeng B, Wen B, Zhang X, et al. Analysis on spatiotemporal variation in soil drought and its influencing factors in Hebei province from 2001 to 2020. Agriculture. 2025;15(10):1109. doi: 10.3390/agriculture15101109

 

  1. Yang R, Li X, Mao D, Wang Z, Tian Y, Dong Y. Examining fractional vegetation cover dynamics in response to climate from 1982 to 2015 in the Amur River Basin for SDG 13. Sustainability. 2020;12(14):5866. doi: 10.3390/su12145866

 

  1. Mao L, Pei X, He C, et al. Spatiotemporal changes in vegetation cover and soil moisture in the lower reaches of the Heihe river under climate change. Forests. 2024;15(11):1921. doi: 10.3390/f15111921

 

  1. Huang J, Li Y, Fu C, et al. Dryland climate change: Recent progress and challenges. Rev Geophys. 2017;55(3):719-778. doi: 10.1002/2016RG000550

 

  1. Yao T, Bolch T, Chen D, et al. The imbalance of the Asian water tower. Nat Rev Earth Environ. 2022;3(10):618-632. doi: 10.1038/s43017-022-00299-4

 

  1. Immerzeel WW, Lutz AF, Andrade M, et al. Importance and vulnerability of the world’s water towers. Nature. 2020;577(7790):364-369. doi: 10.1038/s41586-019-1822-y

 

  1. Duveiller G, Hooker J, Cescatti A. The mark of vegetation change on Earth’s surface energy balance. Nat Commun. 2018;9(1):679. doi: 10.1038/s41467-017-02810-8

 

  1. Zhu Z, Piao S, Myneni RB, et al. Greening of the Earth and its drivers. Nat Clim Change. 2016;6(8):791-795. doi: 10.1038/nclimate3004

 

  1. Zhou Q, Chen W, Wang H, Wang D. Spatiotemporal evolution and driving factors analysis of fractional vegetation coverage in the arid region of northwest China. Sci Total Environ. 2024;954:176271. doi: 10.1016/j.scitotenv.2024.176271

 

  1. Zhang K, Kimball JS, Nemani RR, Running SW. A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006. Water Resour Res. 2010;46(9):2009WR008800. doi: 10.1029/2009WR008800

 

  1. Deng Y, Wang X, Wang K, et al. Responses of vegetation greenness and carbon cycle to extreme droughts in China. Agric Forest Meteorol. 2021;298-299:108307. doi: 10.1016/j.agrformet.2020.108307

 

  1. Vicente-Serrano SM, Gouveia C, Camarero JJ, et al. Response of vegetation to drought time-scales across global land biomes. Proc Natl Acad Sci USA. 2013;110(1):52-57. doi: 10.1073/pnas.1207068110

 

  1. Dimitriadis P, Koutsoyiannis D. Climacogram versus autocovariance and power spectrum in stochastic modelling for Markovian and Hurst– Kolmogorov processes. Stoch Environ Res Risk Assess. 2015;29(6):1649-1669. doi: 10.1007/s00477-015-1023-7

 

  1. Dimitriadis P, Koutsoyiannis D, Iliopoulou T, Papanicolaou P. A Global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes. Hydrology. 2021;8(2):59. doi: 10.3390/hydrology8020059

 

  1. Wei S, Li X, Wang K, Wang T, Piao S. Two decades of persistent greening in China despite 2023 climate extremes. Sci China Earth Sci. 2025;68(4):1064-1073. doi: 10.1007/s11430-024-1530-7

 

  1. Cao S, Zhang L, He Y, et al. Effects and contributions of meteorological drought on agricultural drought under different climatic zones and vegetation types in Northwest China. Sci Total Environ. 2022;821:153270. doi: 10.1016/j.scitotenv.2022.153270

 

  1. Wang H, Guan H, Liu B, Chen X. Impacts of climate extremes on vegetation dynamics in a transect along the Hu Line of China. Ecol Indic. 2023;155:111043. doi: 10.1016/j.ecolind.2023.111043

 

  1. Qi X, Jia J, Liu H, Lin Z. Relative importance of climate change and human activities for vegetation changes on China’s silk road economic belt over multiple timescales. CATENA. 2019;180:224-237. doi: 10.1016/j.catena.2019.04.027

 

  1. Urban MC, Alberti M, De Meester L, et al. Interactions between climate change and urbanization will shape the future of biodiversity. Nat Clim Chang. 2024;14(5):436-447. doi: 10.1038/s41558-024-01996-2

 

  1. Gao X, Wen R, Lo K, Li J, Yan A. Heterogeneity and non-linearity of ecosystem responses to climate change in the Qilian Mountains National Park, China. J Arid Land. 2023;15(5):508-522. doi: 10.1007/s40333-023-0101-9

 

  1. Yan F, Guo X, Zhang Y, et al. Analysis of the multiple drivers of vegetation cover evolution in the Taihangshan- Yanshan region. Sci Rep. 2024;14(1):15306. doi: 10.1038/s41598-024-66053-6

 

  1. Qiao B, Cao X, Yang H, et al. Nonlinear threshold effects of environmental drivers on vegetation cover in mountain ecosystems: From constraint mechanisms to adaptive management. Ecol Indic. 2025;173:113328. doi: 10.1016/j.ecolind.2025.113328

 

  1. Shi S, Wang X, Hu Z, et al. Geographic detector-based quantitative assessment enhances attribution analysis of climate and topography factors to vegetation variation for spatial heterogeneity and coupling. Glob Ecol Conserv. 2023;42:e02398. doi: 10.1016/j.gecco.2023.e02398

 

  1. Liu Y, Huang T, Qiu Z, Guan Z, Ma X. Effects of precipitation changes on fractional vegetation cover in the Jinghe River basin from 1998 to 2019. Ecol Inform. 2024;80:102505. doi: 10.1016/j.ecoinf.2024.102505

 

  1. Tian X, Tao Z, Xie Y, Shao W, Zhang S. Spatiotemporal evolution and driving mechanism of fractional vegetation coverage in the Yangtze river delta. IEEE J Sel Top Appl Earth Observ Remote Sens. 2024;17:10979-10997. doi: 10.1109/JSTARS.2024.3407727

 

  1. Marincowitz S, Pham NQ, Wingfield BD, Roets F, Wingfield MJ. Microfungi associated with dying Euphorbia mauritanica in South Africa and their relative pathogenicity. Fungal Syst Evol. 2023;12(1):59-72. doi: 10.3114/fuse.2023.12.04

 

  1. Litvinenko V. Advances in Raw Material Industries for Sustainable Development Goals. 1st ed. Boca Raton: CRC Press; 2020. doi: 10.1201/9781003164395

 

  1. Serinaldi F, Chebana F, Kilsby CG. Dissecting innovative trend analysis. Stoch Environ Res Risk Assess. 2020;34(5):733-754. doi: 10.1007/s00477-020-01797-x

 

  1. Hamed KH. Trend detection in hydrologic data: The Mann-Kendall trend test under the scaling hypothesis. J Hydrol. 2008;349(3-4):350-363. doi: 10.1016/j.jhydrol.2007.11.009
Share
Back to top
Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept