Quantifying the direct and indirect effects of terrain, climate and human activity on the spatial pattern of kNDVI-based vegetation growth: A case study from the Minjiang River Basin, Southeast China

被引:26
作者
Gu, Zipeng [1 ]
Chen, Xingwei [1 ,2 ]
Ruan, Weifang [3 ]
Zheng, Meiling [1 ]
Gen, Kaili [1 ]
Li, Xiaochen [3 ]
Deng, Haijun [1 ,2 ]
Chen, Ying [1 ,2 ]
Liu, Meibing [1 ,2 ]
机构
[1] Fujian Normal Univ, Coll Geog Sci, Fuzhou 350117, Peoples R China
[2] Minist Educ, Key Lab Humid Subtrop Ecogeog Proc, Fuzhou 350117, Peoples R China
[3] Fujian Inst Water Resources & Hydropower Res, Fuzhou 350001, Peoples R China
基金
中国国家自然科学基金;
关键词
kNDVI; Spatial pattern; Drivers; OPGD; PLS-SEM; GEE; Minjiang River basin; NDVI;
D O I
10.1016/j.ecoinf.2024.102493
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
In the context of global change, it is vital to comprehensively understand the spatial pattern and driving mechanism of vegetation growth to maintain the stability of watershed ecosystems. Previous research has focused mainly on identifying the main drivers of vegetation growth, while the direct and indirect effects of climate, terrain, and human activity on vegetation growth have rarely been explored. This study used the Minjiang River Basin (MRB), an important ecological barrier and the largest watershed in southeastern China, as an example. The kernel normalized difference vegetation index (kNDVI) was calculated on the Google Earth Engine (GEE) platform to examine the spatial pattern and evolution characteristics of vegetation growth. The optimal parameter -based geographical detector (OPGD) and partial least squares structural equation modeling (PLS-SEM) were used to analyze how terrain, climate, and human activity influenced the spatial pattern of the kNDVI. (1) From 2001 to 2020, vegetation growth in the MRB was predominantly rated as excellent or good, and 88.93% of the area showed an increasing trend of vegetation growth. (2) The OPGD revealed that the primary drivers influencing the spatial distribution of the kNDVI in the MRB included population density, nighttime light, elevation and temperature, which explained >40% of the variation in the kNDVI. The interaction of all paired drivers enhanced the explanatory power of the kNDVI, among which the strongest interaction was between population density and elevation, and the second interaction was between population density and temperature. (3) PLS-SEM revealed that human activity had a direct negative effect on the kNDVI, while terrain and climate had direct and indirect positive effects on the kNDVI. Overall, the total effects of terrain, climate and human activity on the kNDVI were 0.594, 0.233 and - 0.495, respectively, indicating that the positive effect of terrain outweighed the negative effect of human activity on vegetation growth in the MRB. These findings not only provide scientific evidence for ecological conservation and management in the MRB but also offer a useful reference for other regions exploring the complex causes of spatial patterns of vegetation growth.
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页数:17
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共 83 条
  • [1] Vegetation-based climate mitigation in a warmer and greener World
    Alkama, Ramdane
    Forzieri, Giovanni
    Duveiller, Gregory
    Grassi, Giacomo
    Liang, Shunlin
    Cescatti, Alessandro
    [J]. NATURE COMMUNICATIONS, 2022, 13 (01)
  • [2] Improving NDVI by removing cirrus clouds with optical remote sensing data from Landsat-8 - A case study in Quito, Ecuador
    Alvarez-Mendoza, Cesar, I
    Teodoro, Ana
    Ramirez-Cando, Lenin
    [J]. REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2019, 13 : 257 - 274
  • [3] Analysis of vegetation dynamics in the Qinling-Daba Mountains region from MODIS time series data
    Bai, Yan
    [J]. ECOLOGICAL INDICATORS, 2021, 129
  • [4] Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle
    Bao, Tiantian
    Wang, Ruifan
    Song, Linghan
    Liu, Xiaojie
    Zhong, Shuangwen
    Liu, Jian
    Yu, Kunyong
    Wang, Fan
    [J]. REMOTE SENSING, 2022, 14 (21)
  • [5] A unified vegetation index for quantifying the terrestrial biosphere
    Camps-Valls, Gustau
    Campos-Taberner, Manuel
    Moreno-Martinez, Alvaro
    Walther, Sophia
    Duveiller, Gregory
    Cescatti, Alessandro
    Mahecha, Miguel D.
    Munoz-Mari, Jordi
    Javier Garcia-Haro, Francisco
    Guanter, Luis
    Jung, Martin
    Gamon, John A.
    Reichstein, Markus
    Running, Steven W.
    [J]. SCIENCE ADVANCES, 2021, 7 (09):
  • [6] Multiple Global Population Datasets: Differences and Spatial Distribution Characteristics
    Chen, Ruxia
    Yan, Huimin
    Liu, Fang
    Du, Wenpeng
    Yang, Yanzhao
    [J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2020, 9 (11)
  • [7] Quantifying the Influences of Natural Factors and Human Activities on NDVI Changes in the Hanjiang River Basin, China
    Chen, Ting
    Xia, Jun
    Zou, Lei
    Hong, Si
    [J]. REMOTE SENSING, 2020, 12 (22) : 1 - 21
  • [8] [陈文裕 Chen Wenyu], 2022, [生态环境学报, Ecology and Environmental Sciences], V31, P1306
  • [9] NDVI-Based Assessment of Land Degradation Trends in Balochistan, Pakistan, and Analysis of the Drivers
    Chen, Xiaoxin
    Wang, Yongdong
    Chen, Yusen
    Fu, Shilin
    Zhou, Na
    [J]. REMOTE SENSING, 2023, 15 (09)
  • [10] CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting
    Dash, Ganesh
    Paul, Justin
    [J]. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2021, 173