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Analysis of the Spatial and Temporal Changes of NDVI and Its Driving Factors in the Wei and Jing River Basins

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  • Chenlu Huang

    (College of Tourist (Institute of Human Geography), Xi’an International Studies University, Xi’an 710127, China)

  • Qinke Yang

    (College of Urban and Environment Sciences, Northwest University, Xi’an 710127, China)

  • Weidong Huang

    (Hydrology and Water Resources Bureau of Gansu Province, Lanzhou 730000, China)

Abstract

This study aimed to explore the long-term vegetation cover change and its driving factors in the typical watershed of the Yellow River Basin. This research was based on the Google Earth Engine (GEE), a remote sensing cloud platform, and used the Landsat surface reflectance datasets and the Pearson correlation method to analyze the vegetation conditions in the areas above Xianyang on the Wei River and above Zhangjiashan on the Jing River. Random forest and decision tree models were used to analyze the effects of various climatic factors (precipitation, temperature, soil moisture, evapotranspiration, and drought index) on NDVI (normalized difference vegetation index). Then, based on the residual analysis method, the effects of human activities on NDVI were explored. The results showed that: (1) From 1987 to 2018, the NDVI of the two watersheds showed an increasing trend; in particular, after 2008, the average increase rate of NDVI in the growing season (April to September) increased from 0.0032/a and 0.003/a in the base period (1987–2008) to 0.0172/a and 0.01/a in the measurement period (2008–2018), for the Wei and Jing basins, respectively. In addition, the NDVI significantly increased from 21.78% and 31.32% in the baseline period (1987–2008) to 83.76% and 92.40% in the measurement period (2008–2018), respectively. (2) The random forest and classification and regression tree model (CART) can assess the contribution and sensitivity of various climate factors to NDVI. Precipitation, soil moisture, and temperature were found to be the three main factors that affect the NDVI of the study area, and their contributions were 37.05%, 26.42%, and 15.72%, respectively. The changes in precipitation and soil moisture in the entire Jing River Basin and the upper and middle reaches of the Wei River above Xianyang caused significant changes in NDVI. Furthermore, changes in precipitation and temperature led to significant changes in NDVI in the lower reaches of the Wei River. (3) The impact of human activities in the Wei and Jing basins on NDVI has gradually changed from negative to positive, which is mainly due to the implementation of soil and water conservation measures. The proportions of areas with positive effects of human activities were 80.88% and 81.95%, of which the proportions of areas with significant positive effects were 11.63% and 7.76%, respectively. These are mainly distributed in the upper reaches of the Wei River and the western and eastern regions of the Jing River. These areas are the key areas where soil and water conservation measures have been implemented in recent years, and the corresponding land use has transformed from cultivated land to forest and grassland. The negative effects accounted for 1.66% and 0.10% of the area, respectively, and were mainly caused by urban expansion and coal mining.

Suggested Citation

  • Chenlu Huang & Qinke Yang & Weidong Huang, 2021. "Analysis of the Spatial and Temporal Changes of NDVI and Its Driving Factors in the Wei and Jing River Basins," IJERPH, MDPI, vol. 18(22), pages 1-16, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:11863-:d:677477
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    References listed on IDEAS

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    1. Almut Arneth, 2015. "Uncertain future for vegetation cover," Nature, Nature, vol. 524(7563), pages 44-45, August.
    2. Jean-François Pekel & Andrew Cottam & Noel Gorelick & Alan S. Belward, 2016. "High-resolution mapping of global surface water and its long-term changes," Nature, Nature, vol. 540(7633), pages 418-422, December.
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    2. Quansheng Li & Feiyue Li & Junting Guo & Li Guo & Shanshan Wang & Yaping Zhang & Mengyuan Li & Chengye Zhang, 2023. "The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    3. Xiaoxin Li & Ruichen Mao & Jinxi Song & Junqing Gao & Aying Shi & Wei Xiang & Haotian Sun, 2024. "Response of Runoff Change to Soil and Water Conservation Measures in the Jing River Catchment of China," Land, MDPI, vol. 13(4), pages 1-21, March.
    4. Weimin Liu & Dengming Yan & Zhilei Yu & Zening Wu & Huiliang Wang & Jie Yang & Simin Liu & Tianye Wang, 2024. "Analysis of Dynamic Changes in Vegetation Net Primary Productivity and Its Driving Factors in the Two Regions North and South of the Hu Huanyong Line in China," Land, MDPI, vol. 13(6), pages 1-23, May.
    5. Rongyao Wang & Junsan Zhao & Guoping Chen & Yilin Lin & Anran Yang & Jiaqi Cheng, 2022. "Coupling PLUS–InVEST Model for Ecosystem Service Research in Yunnan Province, China," Sustainability, MDPI, vol. 15(1), pages 1-19, December.
    6. Jian Wang & Junsan Zhao & Peng Zhou & Kangning Li & Zhaoxiang Cao & Haoran Zhang & Yang Han & Yuanyuan Luo & Xinru Yuan, 2023. "Study on the Spatial and Temporal Evolution of NDVI and Its Driving Mechanism Based on Geodetector and Hurst Indexes: A Case Study of the Tibet Autonomous Region," Sustainability, MDPI, vol. 15(7), pages 1-24, March.

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