Evidence map›Paper›PMID 36388493›Full record

ArticleFrontiers in plant science2022

Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021.

Huihui Wang, Jinyan Zhan, Chao Wang, Wei Liu, Zheng Yang, Huizi Liu, Chunyue Bai

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

7 authors.

Huihui WangState Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China.
Jinyan ZhanState Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China.
Chao WangSchool of Labor Economics, Capital University of Economics and Business, Beijing, China.
Wei LiuCollege of Geography and Environment, Shandong Normal University, Jinan, China.
Zheng YangState Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China.
Huizi LiuState Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China.
Chunyue BaiState Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vegetation greenness is one of the main indicators to characterize changes in terrestrial ecosystems. China has implemented a few large-scale ecological restoration programs on the Qinghai-Tibetan Plateau (QTP) to reverse the trend of ecosystem degradation. Although the effectiveness of these programs is beginning to show, the mechanisms of vegetation degradation under climate change and human activities are still controversial. Existing studies have mostly focused on changes in overall vegetation change, with less attention on the drivers of change in different vegetation types. In this study, earth satellite observation records were used to robustly map changes in vegetation greenness on the QTP from 2000 to 2021. The random forest (RF) algorithm was further used to detect the drivers of greenness browning on the QTP as a whole and in seven different vegetation types. The results show that an overall trend of greening in all seven vegetation types on the QTP over a 21-year period. The area of greening was 46.54×10

Indexed as

browningclimate changegreeningMODIS NDVIQinghai-Tibetan Plateau (QTP)restoration

Identifiers

PMID36388493
PMCPMC9643839

What OpenQuestion holds

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LicenceCC BY
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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.