Spatiotemporal variations and its driving factors of NDVI in Northwest China during 2000-2021

被引:7
|
作者
Zhang, Jiaxin [1 ,2 ]
Yang, Tao [1 ,2 ]
Deng, Mingjiang [1 ,3 ]
Huang, Huiping [4 ]
Han, Yuping [4 ]
Xu, Huanhuan [4 ]
机构
[1] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul Eng, Nanjing 210098, Peoples R China
[2] Hohai Univ, Natl Key Lab Water Disaster Prevent, Nanjing 210098, Jiangsu, Peoples R China
[3] Xinjiang Ertix River Basin Dev & Construct Managem, Urumqi 830000, Peoples R China
[4] North China Univ Water Resources & Elect Power, Coll Water Resources, Zhengzhou 450046, Peoples R China
关键词
Northwest China; NDVI; Driving factors; Geodetector; Contribution rate; CLIMATE-CHANGE; LOESS PLATEAU; RIVER-BASIN; VEGETATION;
D O I
10.1007/s11356-023-30250-z
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Northwest China (WTL) is an essential ecological barrier zone of China, an important node of the "Silk Road Economic Belt," and a crucial bearing area for China's execution of the "One Road and One Belt" and "Going Global" strategies. However, its ecology is exceedingly fragile and particularly vulnerable to climate change and human interference. This study explored the spatiotemporal evolution characteristics of vegetation in WTL using NDVI data and investigated its drive mechanisms by geodetector, partial correlation analysis, and residual trend analysis methods. As well as forecasting the trend for vegetation changes. The findings demonstrated that (1) the change in NDVI manifested an overall improvement trend and the distribution in space of NDVI rose from the center to the periphery. 57.07% of the area had a sparse cover of vegetation (NDVI between 0 and 0.2). In addition, about 49% of regions had deterioration tendencies, which were mainly aggregated in HX, QCXDB, QCXDN, and the eastern of QCXQN and QCXXB. (2) The NDVI's shifting trend was unsustainability, and the region of uncertain future accounted for 57.45% of the total, with apparent unsustainability features. (3) The key parameters influencing NDVI spatial distribution were Pre (precipitation), vegetation type, land use type, and soil type. The interaction between two factors enhanced the influence of any single element, which appeared as bivariate and nonlinear enhancements. (4) Both climate variations and human activities have been recognized as key variables affecting NDVI growth. NDVI variance in 73.02% of areas was influenced by the combined effects of climate variations and human activities. However, human activities were the most influential element in NDVI growth, with the relative contributions of 80.28% (19.72% of which was caused by climate variations). These results can be conducive to deepening insights into the local vegetation status, identifying the mechanisms driving vegetation change, and providing scientific recommendations for WTL's ecosystem restoration measures based on actual situations.
引用
收藏
页码:118782 / 118800
页数:19
相关论文
共 50 条
  • [21] Continuous Karakoram Glacier Anomaly and Its Response to Climate Change during 2000-2021
    Lhakpa, Drolma
    Fan, Yubin
    Cai, Yu
    REMOTE SENSING, 2022, 14 (24)
  • [22] Climate change drives NDVI variations at multiple spatiotemporal levels rather than human disturbance in Northwest China
    Shang, Jiaxin
    Zhang, Yang
    Peng, Yu
    Huang, Yihang
    Zhu, Lu
    Wu, Zhuoyi
    Wang, Jing
    Cui, Yixin
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (10) : 13782 - 13796
  • [23] 2000-2021年泾河流域植被NDVI变化及影响因素
    封建民
    郭玲霞
    刘宇峰
    文琦
    水土保持研究, 2025, 32 (01) : 249 - 256
  • [24] Climate change drives NDVI variations at multiple spatiotemporal levels rather than human disturbance in Northwest China
    Jiaxin Shang
    Yang Zhang
    Yu Peng
    Yihang Huang
    Lu Zhu
    Zhuoyi Wu
    Jing Wang
    Yixin Cui
    Environmental Science and Pollution Research, 2022, 29 : 13782 - 13796
  • [25] Spatiotemporal Dynamics and Driving Factors of Ecosystem Services Value in the Hexi Regions, Northwest China
    Li, Yongge
    Liu, Wei
    Feng, Qi
    Zhu, Meng
    Zhang, Jutao
    Yang, Linshan
    Yin, Xinwei
    SUSTAINABILITY, 2022, 14 (21)
  • [26] Spatiotemporal variations and driving factors of crop productivity in China from 2001 to 2020
    Zhang, Haitao
    Xu, Yingjun
    Lu, Yifan
    Hasi, Eerdun
    Zhang, Hua
    Zhang, Shengnan
    Wang, Weifeng
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 371
  • [27] Carbohydrate-based drugs launched during 2000-2021
    Xin Cao
    Xiaojing Du
    Heng Jiao
    Quanlin An
    Ruoxue Chen
    Pengfei Fang
    Jing Wang
    Biao Yu
    ActaPharmaceuticaSinicaB, 2022, 12 (10) : 3783 - 3821
  • [28] Spatiotemporal Variations in Evapotranspiration and Their Driving Factors in Southwest China between 2003 and 2020
    Zhang, Ji
    Zhou, Xu
    Yang, Shengtian
    Ao, Yang
    REMOTE SENSING, 2023, 15 (18)
  • [29] Spatiotemporal variations and driving factors of China's ecosystem water use efficiency
    Ji, Yongyue
    Zeng, Sidong
    Tang, QingQing
    Yan, Lingyun
    Wu, Shengjun
    Fan, Yuanchao
    Chen, Jilong
    ECOLOGICAL INDICATORS, 2023, 148
  • [30] NDVI-derived forest area change and its driving factors in China
    Liang, Lizhuang
    Chen, Feng
    Shi, Lei
    Niu, Shukui
    PLOS ONE, 2018, 13 (10):