Evidence map›Paper›PMID 41253961›Full record

ArticleScientific reports2025

Decoupling anthropogenic and climate impacts on vegetation dynamics in China's Huaihe River Basin using geodetector.

Xinyu Wang, Yanling Li, Shibo Fang, Chao Dong, Li Sun

Abstract read
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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xinyu Wang *College of Information Science and Engineering, Shandong Agricultural University, Taian, 271018, China.
Yanling Li *College of Information Science and Engineering, Shandong Agricultural University, Taian, 271018, China. liyanling@sdau.edu.cn.
Shibo FangState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China. fangshibo@cma.gov.cn.
Chao DongCollege of Information Science and Engineering, Shandong Agricultural University, Taian, 271018, China.
Li SunCollege of Information Science and Engineering, Shandong Agricultural University, Taian, 271018, China.

Funding

Natural Science Foundation of Shandong Province ZR2021MD096the National Natural Science Foundation of China 11701337
6 · The paper itself

Abstract

Understanding the spatiotemporal dynamics of vegetation under the coupled influence of climatic and anthropogenic drivers is crucial for ecological governance at the river basin scale. This study focuses on the Huaihe River Basin (HRB) in China, a climate-sensitive region spanning both northern and southern climatic zones. Previous studies have often overlooked the inherent temporal and spatial autocorrelation in NDVI time series within this region, which may lead to overestimation or underestimation of vegetation trends, as well as misidentification of spurious trends as statistically significant. To address spatial and temporal autocorrelation challenges, we developed a novel hybrid framework combining Univariate Stationary First-order Gaussian Autoregressive (AR1) modeling with spatial autocorrelation analysis (Moran's I index), enabling robust detection of vegetation trends, stability evaluation, and clustering characteristics. Combined with Geodetector, we quantified the impacts of climatic extremes, land use change, and urbanization on NDVI patterns(2000-2022). Key findings reveal: (1) A significant greening trend with mean annual NDVI increase of 0.00152 yr⁻¹ (p < 0.05), demonstrating basin-wide ecological improvement; (2) Spatially divergent patterns where 42.89% of the basin show significant NDVI growth, contrasted by 9.54% degradation areas. Notably, 47.57% exhibits non-significant increases, emphasizing the necessity of temporal autocorrelation correction in trend detection; (3) Geodetector analysis identifies land use type as the dominant spatial heterogeneity driver (q = 0.35-0.42), while extreme climatic events (particularly the 2000-2001 mega-drought) govern temporal anomalies. Urban expansion reduced vegetation cover, except near water bodies where NDVI remained stable. Interaction analysis revealed nonlinear synergies between anthropogenic activities (e.g., GDP, population) and climatic factors, emphasizing the need for adaptive policies in transitional ecosystems. These findings advocate for tiered management strategies incorporating climate-adaptive zoning, emphasizing riparian corridor conservation and dynamic land-use optimization to balance ecological restoration with urban development. This work offers a replicable framework for regional-scale ecological assessments.

Indexed as

Anthropogenic-Climate driversDecouplingGeodetectorHuaihe River BasinSpatiotemporal dynamicsVegetation index

Identifiers

PMID41253961
PMCPMC12627572

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