Evidence map›Paper›PMID 41754333›Full record

ArticlePlants (Basel, Switzerland)2026

Analyzing the Driving Forces of Vegetation Change in the Yellow River Basin: Comprehensive Assessment of Natural Social and Economic Factors.

Fei Gao, Ying Yang, Weijie Yuan, Xiuxiu Deng, Lina Wang, Shuai Zhang

Abstract read
In one paragraph

Article in Plants (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Fei GaoExperimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, China.
Ying YangExperimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, China.
Weijie YuanExperimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, China.ORCID 0009-0006-6519-8443
Xiuxiu DengExperimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, China.
Lina WangExperimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, China.
Shuai ZhangExperimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, China.

Funding

Sub-project of the National Key Research and Development Program of China 2024YFD2201104-4the Fundamental Research Funds for the Central Non-profit Re-search Institution of CAF CAFYBB2024QF037、CAFYBB2022MA009
6 · The paper itself

Abstract

Leaf area index (LAI) is a key vegetation structural parameter widely used to quantify vegetation dynamics. A thorough understanding of its spatiotemporal characteristics and driving mechanisms is essential for sustainable ecosystem management. This study combines LAI and climate remote sensing data with socioeconomic statistics, and uses the Geographically and Temporally Weighted Regression (GTWR) model and geographic detector to identify the key drivers of LAI changes and their spatial differentiation characteristics. The results indicate a significant upward trend in the LAI across the basin, with a markedly higher growth rate after 2000 (0.0123/year) compared to the period before 2000 (0.0028/year). Spatially, before 2000, 57% of the regions showed an increasing trend in LAI, while after 2000, 69% of the regions exhibited an increasing trend in LAI. In terms of temporal LAI dynamics, in the eastern region, the positive promotion effect of the Grain for Green policy (GRGR) was the most significant factor affecting LAI changes. In the central region, the employed population ratio (EPR) emerged as the dominant factor driving LAI increase. In the western region, the negative effect of the urban-rural income ratio (IR) served as the primary driver of LAI change. Regarding the spatial distribution of LAI, both natural and policy factors were statistically significant. Mean precipitation (MP) and mean slope (MS) exerted the most pronounced influences, followed by mean temperature (MT) and GRGR, whereas mean elevation (MD) showed the weakest effect. Notably, socioeconomic factors did not demonstrate statistically significant impacts on the spatial distribution of LAI. This study provides a theoretical foundation for understanding the driving mechanisms of LAI dynamics in the Yellow River Basin and offers scientific support for ecological restoration and sustainable management in the region.

Indexed as

driving mechanismsecological restorationGeographically and Temporally Weighted Regressionleaf area index

Identifiers

PMID41754333
PMCPMC12944586

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.