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Remote Sensing
Volume 17
Issue 8
10.3390/rs17081433
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Article

Estimating the Effects of Natural and Anthropogenic Activities on Vegetation Cover: Analysis of Zhejiang Province, China, from 2000 to 2022

by
Lv Chen
1,2,
Chong Li
1,2,*,
Chunyu Pan
1,3,
Yancun Yan
3,
Jiejie Jiao
4,
Yufeng Zhou
1,2,
Xiaoxian Wang
5 and
Guomo Zhou
1,2
1
Key Laboratory of Carbon Sequestration and Emission Reduction in Agriculture and Forestry of Zhejiang Province, Zhejiang A&F University, Hangzhou 311300, China
2
School of Environmental and Resources Science, Zhejiang A&F University, Hangzhou 311300, China
3
Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada
4
Zhejiang Academy of Forestry, Hangzhou 310023, China
5
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(8), 1433; https://doi.org/10.3390/rs17081433
Submission received: 12 February 2025 / Revised: 21 March 2025 / Accepted: 29 March 2025 / Published: 17 April 2025
(This article belongs to the Special Issue Remote Sensing Based Forest Inventories from Landscape to Global Scale II)
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Abstract

Zhejiang Province, a pivotal economically developed region within China’s Yangtze River Delta, requires systematic investigation of spatiotemporal vegetation dynamics and their drivers to formulate targeted ecological protection policies and optimize vegetation restoration strategies. Utilizing the Google Earth Engine (GEE) platform, this study applied the Kernel Normalized Difference Vegetation Index (kNDVI) to assess vegetation responses to climate variability and human activities in Zhejiang Province from 2000 to 2022. Analytical methods included simple linear regression, Theil Sen trend analysis (Sen), Mann Kendall test (MK), Hurst index, partial correlation analysis, and correlation analysis. The results show: (1) The kNDVI exhibited a significant upward trend (0.001/year), covering 61.5% of the province. The Hurst index analysis revealed that 69.1% of vegetation changes exhibited anti-sustainability characteristics, with future vegetation degradation areas (56.4%) projected to exceed improvement areas (28.1%). (2) Human activities (57.11%) contributed more to kNDVI changes than climate change (42.89%). (3) Against the backdrop of climate change, kNDVI demonstrated a positive partial correlation with temperature (coefficient: 0.44) but exhibited a negative correlation with precipitation (coefficient: −0.056), confirming temperature as the dominant climatic driver. Overall, vegetation dynamics in Zhejiang Province from 2000 to 2022 were jointly driven by climate change and human activities.
Keywords: human activities; climate change; vegetation monitoring; google earth engine; remote sensing human activities; climate change; vegetation monitoring; google earth engine; remote sensing

Share and Cite

MDPI and ACS Style

Chen, L.; Li, C.; Pan, C.; Yan, Y.; Jiao, J.; Zhou, Y.; Wang, X.; Zhou, G. Estimating the Effects of Natural and Anthropogenic Activities on Vegetation Cover: Analysis of Zhejiang Province, China, from 2000 to 2022. Remote Sens. 2025, 17, 1433. https://doi.org/10.3390/rs17081433

AMA Style

Chen L, Li C, Pan C, Yan Y, Jiao J, Zhou Y, Wang X, Zhou G. Estimating the Effects of Natural and Anthropogenic Activities on Vegetation Cover: Analysis of Zhejiang Province, China, from 2000 to 2022. Remote Sensing. 2025; 17(8):1433. https://doi.org/10.3390/rs17081433

Chicago/Turabian Style

Chen, Lv, Chong Li, Chunyu Pan, Yancun Yan, Jiejie Jiao, Yufeng Zhou, Xiaoxian Wang, and Guomo Zhou. 2025. "Estimating the Effects of Natural and Anthropogenic Activities on Vegetation Cover: Analysis of Zhejiang Province, China, from 2000 to 2022" Remote Sensing 17, no. 8: 1433. https://doi.org/10.3390/rs17081433

APA Style

Chen, L., Li, C., Pan, C., Yan, Y., Jiao, J., Zhou, Y., Wang, X., & Zhou, G. (2025). Estimating the Effects of Natural and Anthropogenic Activities on Vegetation Cover: Analysis of Zhejiang Province, China, from 2000 to 2022. Remote Sensing, 17(8), 1433. https://doi.org/10.3390/rs17081433

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