Evidence map›Paper›PMID 39420016›Full record

ArticleScientific data2024

A 30-m annual grassland dataset from 1991 to 2020 for Inner Mongolia, China.

Min Liu, Yue Qu, Jie Wang, Yixin Liao, Guotai Zheng, Yubin Guo, Luo Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Min LiuGuangdong Province Key Laboratory for Agricultural Resources Utilization, South China Agricultural University, Guangzhou, 510642, China.
Yue QuGuangdong Province Key Laboratory for Agricultural Resources Utilization, South China Agricultural University, Guangzhou, 510642, China.
Jie WangCollege of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China.ORCID http://orcid.org/0000-0003-1866-3999
Yixin LiaoGuangdong Province Key Laboratory for Agricultural Resources Utilization, South China Agricultural University, Guangzhou, 510642, China.
Guotai ZhengCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China.
Yubin GuoCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China. guoyubin@scau.edu.cn.
Luo LiuGuangdong Province Key Laboratory for Agricultural Resources Utilization, South China Agricultural University, Guangzhou, 510642, China. liuluo@scau.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 42101355
6 · The paper itself

Abstract

Grassland is a widely distributed land use type that provides essential resources for livestock production and serves as a crucial ecosystem component. Over the past decades, grassland has significantly degraded and shrunk due to climate change and human activities, with ongoing changes in its area. This study utilized the Google Earth Engine (GEE) and the Res-UNet++ model to analyze the phenological and spectral characteristics of grasslands in Landsat images for long-term annual monitoring. Additionally, the LandTrendr algorithm was utilized to correct long-term time series data for grasslands, yielding a map of grassland distribution in Inner Mongolia from 1991 to 2020. The results indicate that the overall spatial accuracy from 1991 to 2020 exceeded 96%, with a Kappa coefficient over 0.92, demonstrating high monitoring accuracy. In particular, the 2019 grassland monitoring area showed high consistency with data from the Third National Land Survey (TNLS), with a coefficient of determination (R^2) reaching 0.97, reflecting the high accuracy and reliability.

Identifiers

PMID39420016
PMCPMC11486956

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.