Evidence map›Paper›PMID 33813700›Full record

ArticleEnvironmental science and pollution research international2021

Dynamic characteristics and driving factors of vegetation greenness under changing environments in Xinjiang, China.

Panxing He, Zongjiu Sun, Zhiming Han, Yiqiang Dong, Huixia Liu, Xiaoyu Meng, Jun Ma

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2021. 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
5.9field-weighted citation impact, top 3% of its field
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, 41 citations in OpenAlex.

  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 at 4 institutions in 1 country.

Panxing HeMinistry of Education Key Laboratory for Western Arid Region Grassland Resources and Ecology, College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, 830052, China.
Zongjiu SunMinistry of Education Key Laboratory for Western Arid Region Grassland Resources and Ecology, College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, 830052, China. nmszj@21cn.com.
Zhiming HanState Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi'an University of Technology, Xi'an, 710000, China.
Yiqiang DongMinistry of Education Key Laboratory for Western Arid Region Grassland Resources and Ecology, College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, 830052, China.
Huixia LiuMinistry of Education Key Laboratory for Western Arid Region Grassland Resources and Ecology, College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, 830052, China.
Xiaoyu MengState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China.
Jun MaMinistry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, Shanghai, 200438, China.
Xinjiang Agricultural University · CNChinese Academy of Sciences · CNFudan University · CNXi'an University of Technology · CN

Funding

Natural Science Foundation of Jilin Province 41601181Science and Technology Basic Resources Investigation Program of China 2017FY100200
6 · The paper itself

Abstract

Global environment changes rapidly alter regional hydrothermal conditions, which undoubtedly affects the spatiotemporal dynamics of vegetation, especially in arid and semi-arid areas. However, identifying and quantifying the dynamic evolution and driving factors of vegetation greenness under the changing environment are still a challenge. In this study, gradual trend analysis was applied to calculate the overall spatiotemporal trend of the normalized difference vegetation index (NDVI) time series of Xinjiang province in China, the abrupt change analysis was used to detect the timing of breakpoint and trend shift, and two machine learning methods (boosted regression tree and random forest) were used to quantify the key factors of vegetation change and their relative contribution rate. The results have shown that vegetation has experienced overall recovery over the past 20 years in Xinjiang, and greenness increased at a rate of 17.83 10

Indexed as

EcosystemChinaSeasonsAbrupt changeEnvironmental factorsGradual changeLand-use managementVegetation greennessXinjiang

Identifiers

PMID33813700
OpenAlexW3151944980

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.