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Article

Anthropogenic Activities Dominate Vegetation Improvement in Arid Areas of China

1
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
College of Chemical Engineering and Environment, China University of Petroleum, Beijing 102249, China
3
Institute of Farmland Water Conservancy and Soil-Fertilizer, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China
4
Key Laboratory of Northwest Oasis Water-Saving Agriculture, Ministry of Agriculture and Rural Affairs, Shihezi 832000, China
5
University of Chinese Academy of Sciences, Beijing 100049, China
6
Beijing Forestry University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(15), 2634; https://doi.org/10.3390/rs17152634
Submission received: 16 June 2025 / Revised: 12 July 2025 / Accepted: 23 July 2025 / Published: 29 July 2025

Abstract

Arid regions, while providing essential ecosystem services, are among the most ecologically vulnerable worldwide. Understanding and monitoring their long-term vegetation dynamics is essential for accurate environmental assessment and climate adaptation strategies. This study examined the spatiotemporal variations and driving forces of the vegetation dynamics in arid Northwestern China during 2000 to 2020, using the annual peak fractional vegetation cover (FVC) as the primary indicator. The Sen’s slope estimator with the Mann–Kendall test and the coefficient of variation were employed to assess the spatiotemporal variations in FVC, while the Pearson correlation, geographic detector model and random forest model were applied to identify the dominant driving factors for FVC. The results indicated that (1) overall vegetation cover was low (averaged peak FVC = 0.191), showing a spatial pattern of higher values in the northwest and lower values in the southeast; high FVC values were primarily observed in mountainous areas and river corridors; (2) the annual peak FVC increased significantly at a rate of 0.0508 yr−1, with 33.72% of the region showing significant improvements and 5.49% degradation; (3) the spatial pattern of FVC was shaped by the distribution of land use types (59.46%), while the temporal dynamics of FVC were driven by land use changes (16.37%) and the land use intensity (37.56%); (4) both the spatial pattern and the temporal dynamics were limited by the environmental conditions. These findings highlight the critical role of anthropogenic activities in shaping the spatiotemporal variations in FVC, particularly emphasizing the distinct contributions of changes in land use types and land use intensity. This study could provide a scientific basis for sustainable land management and restoration strategies in arid regions facing global changes.
Keywords: spatiotemporal variation in vegetation; land use change; land use intensity; climatic changes spatiotemporal variation in vegetation; land use change; land use intensity; climatic changes

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MDPI and ACS Style

Guo, Y.; Wang, X.; Cao, H.; Peng, Q.; Dong, Y.; Qi, Y.; Liu, J.; Lv, N.; Yin, F.; Yuan, X.; et al. Anthropogenic Activities Dominate Vegetation Improvement in Arid Areas of China. Remote Sens. 2025, 17, 2634. https://doi.org/10.3390/rs17152634

AMA Style

Guo Y, Wang X, Cao H, Peng Q, Dong Y, Qi Y, Liu J, Lv N, Yin F, Yuan X, et al. Anthropogenic Activities Dominate Vegetation Improvement in Arid Areas of China. Remote Sensing. 2025; 17(15):2634. https://doi.org/10.3390/rs17152634

Chicago/Turabian Style

Guo, Yu, Xinwei Wang, Hongying Cao, Qin Peng, Yunshe Dong, Yunchun Qi, Jian Liu, Ning Lv, Feihu Yin, Xiujin Yuan, and et al. 2025. "Anthropogenic Activities Dominate Vegetation Improvement in Arid Areas of China" Remote Sensing 17, no. 15: 2634. https://doi.org/10.3390/rs17152634

APA Style

Guo, Y., Wang, X., Cao, H., Peng, Q., Dong, Y., Qi, Y., Liu, J., Lv, N., Yin, F., Yuan, X., & Zeng, M. (2025). Anthropogenic Activities Dominate Vegetation Improvement in Arid Areas of China. Remote Sensing, 17(15), 2634. https://doi.org/10.3390/rs17152634

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