Evidence map›Paper›PMID 41225944›Full record

ArticlePlants (Basel, Switzerland)2025

Monitoring Long-Term Vegetation Dynamics in the Hulun Lake Basin of Northeastern China Through Greening and Browning Speeds from 1982 to 2015.

Nan Shan, Tie Wang, Qian Zhang, Jinqi Gong, Mingzhu He, Xiaokang Zhang, Xuehe Lu, Feng Qiu

Abstract read
In one paragraph

Article in Plants (Basel, Switzerland), 2025. 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

8 authors.

Nan ShanNanjing lnstitute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing 210042, China.
Tie WangNanjing lnstitute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing 210042, China.
Qian ZhangCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.ORCID 0000-0002-0860-4023
Jinqi GongSchool of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China.
Mingzhu HeSchool of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China.
Xiaokang ZhangCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
Xuehe LuSchool of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215000, China.ORCID 0000-0001-5919-0097
Feng QiuNanjing lnstitute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing 210042, China.ORCID 0000-0002-0422-3493

Funding

Ecological Security Investigation and Assessment Project of Hulun Lake HSZCS-G-F-210059National Natural Science Foundation of China 42071050Taishan Scholar Project TSQN202306210
6 · The paper itself

Abstract

Vegetation dynamics in the Hulun Lake Basin (HLB), a vulnerable grassland-wetland-forest transition zone in Northeastern Inner Mongolia, North China, are sensitive to climate change, but traditional greenness metrics like the normalized difference vegetation index (NDVI) lack process-level insights. Using the GIMMS NDVI3g dataset (1982-2015) and meteorological data, this study analyzed the spatiotemporal dynamics of the NDVI and vegetation NDVI change rate (VNDVI)-a metric quantifying greening and browning speeds via NDVI temporal variation-employing linear regression and partial correlation analyses. The NDVI exhibited an overall significant upward trend of +0.0028 yr

Indexed as

greening and browning speedprocess-level changesvegetation dynamicsVNDVI

Identifiers

PMID41225944
PMCPMC12609123

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Registered trials

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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.