Spatiotemporal variations and driving mechanisms of vegetation coverage in the Wumeng Mountainous Area, China

被引:24
作者
Tao, Shuai [1 ,2 ]
Peng, Wenfu [1 ,2 ]
Xiang, Jiayao [1 ,2 ]
机构
[1] Sichuan Normal Univ, Inst Geog & Resources Sci, Chengdu 610068, Peoples R China
[2] Sichuan Normal Univ, Key Lab Land Resources Evaluat & Monitoring Southw, Minist Educ, Chengdu 610068, Peoples R China
基金
中国国家自然科学基金;
关键词
Enhanced vegetation index; Fractional vegetation cover; Spatiotemporal variation; Geographical detector; Wumeng Mountainous Area; CLIMATE-CHANGE; KARST REGION; SPATIAL HETEROGENEITY; NDVI CHANGES; RESTORATION; DYNAMICS; PATTERNS; SENSITIVITY; FORCES; RIVER;
D O I
10.1016/j.ecoinf.2022.101737
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Quantifying the influence of factors on changes in fractional vegetation cover (FVC) is critical for assessing regional environmental changes and consequent ecological protection. However, accurately identifying the factors responsible for vegetation changes remains a challenge. This study focuses on the Wumeng Mountain Area, China (WM), where the ecological environment is extremely fragile and the social economy underdeveloped. Using the enhanced vegetation index to calculate FVC, Sen's slope trend analysis, Mann-Kendall test with the trend-free prewhitening procedure, Pettitt change-point test, and Hurst exponent, we analyzed the spatiotemporal variations in vegetation from 2000 to 2019 and projected future variations. The geographical detector model was used to analyze the spatial differentiation driving mechanism of changes in vegetation cover in the WM. We observed that the spatiotemporal variation of vegetation in the WM was significant between 2000 and 2019. The areas of the WM with extremely significant growth and significant growth accounted for 32.57% (p < 0.01) and 15.28% (0.01 < p < 0.05), respectively. The mutation years of the significantly changed vegetation were concentrated between 2007 and 2011. However, 36.09% of vegetation growth exhibited strong unsustainable characteristics and based on the past 20 years, a potential decreasing trend that has great uncertainty in the future. The geographical detector model indicated that temperature and soil type were the primary driving forces for spatial differentiation of vegetation changes in the WM, with q values of 0.131 and 0.101, respectively. Interactions between climate, topography, and human activities promote vegetation growth in a nonlinear fashion
引用
收藏
页数:15
相关论文
共 79 条
  • [1] Vegetation dynamics and ecosystem service values changes at national and provincial scales in Nepal from 2000 to 2017
    Baniya, Binod
    Tang, Qiuhong
    Pokhrel, Yadu
    Xu, Ximeng
    [J]. ENVIRONMENTAL DEVELOPMENT, 2019, 32
  • [2] A Real-Time MODIS Vegetation Product for Land Surface and Numerical Weather Prediction Models
    Case, Jonathan L.
    LaFontaine, Frank J.
    Bell, Jordan R.
    Jedlovec, Gary J.
    Kumar, Sujay V.
    Peters-Lidard, Christa D.
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (03): : 1772 - 1786
  • [3] Past and future carbon sequestration benefits of China's grain for green program
    Deng, Lei
    Liu, Shuguang
    Kim, Dong Gill
    Peng, Changhui
    Sweeney, Sandra
    Shangguan, Zhouping
    [J]. GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS, 2017, 47 : 13 - 20
  • [4] Quantifying influences of physiographic factors on temperate dryland vegetation, Northwest China
    Du, Ziqiang
    Zhang, Xiaoyu
    Xu, Xiaoming
    Zhang, Hong
    Wu, Zhitao
    Pang, Jing
    [J]. SCIENTIFIC REPORTS, 2017, 7
  • [5] Long-term spatiotemporal variations in satellite-based soil moisture and vegetation indices over Iran
    Fakharizadehshirazi, Elham
    Sabziparvar, Ali Akbar
    Sodoudi, Sahar
    [J]. ENVIRONMENTAL EARTH SCIENCES, 2019, 78 (12)
  • [6] Examining Vegetation Change and Associated Spatial Patterns in Wuyishan National Park at Different Protection Levels
    Fan, Mengzhuo
    Liao, Kuo
    Lu, Dengsheng
    Li, Dengqiu
    [J]. REMOTE SENSING, 2022, 14 (07)
  • [7] Evaluation of Earth Observation based global long term vegetation trends - Comparing GIMMS and MODIS global NDVI time series
    Fensholt, Rasmus
    Proud, Simon R.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2012, 119 : 131 - 147
  • [8] Chinese ecosystem research network: Progress and perspectives
    Fu, Bojie
    Li, Shenggong
    Yu, Xiubo
    Yang, Ping
    Yu, Guirui
    Feng, Renguo
    Zhuang, Xuliang
    [J]. ECOLOGICAL COMPLEXITY, 2010, 7 (02) : 225 - 233
  • [9] NDVI-based vegetation dynamics and their responses to climate change and human activities from 1982 to 2020: A case study in the Mu Us Sandy Land, China
    Gao, Wande
    Zheng, Ce
    Liu, Xiuhua
    Lu, Yudong
    Chen, Yunfei
    Wei, Yan
    Ma, Yandong
    [J]. ECOLOGICAL INDICATORS, 2022, 137
  • [10] Gella G., 2018, Environmental Systems Research, V7, P1, DOI [10.1186/s40068-018-0105-1, DOI 10.1186/S40068-018-0105-1]