Evidence map›Paper›PMID 33893589›Full record

ArticleEnvironmental science and pollution research international2021

Macro analysis of spatiotemporal variations in ecosystems from 1996 to 2016 in Xishuangbanna in Southwest China.

Zhuoya Zhang, Xin Yang, Fuming Xie

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
–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. Sustainable water-energy-environment nexus.Environmental science and pollution research international · 2021
    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

3 authors.

Zhuoya ZhangSchool of Geography and Ecotourism, Southwest Forestry University, Kunming, Yunnan, China.
Xin YangCommunist Youth League Committee, Southwest Forestry University, No. 300, Bailong Road, Kunming, Yunnan, China. yangxin@swfu.edu.cn.ORCID http://orcid.org/0000-0001-7836-8786
Fuming XieInstitute of International River and Eco-security, Yunnan University, Kunming, Yunnan, China.

Funding

Yunnan Provincial Department of Education 2017ZZX210
6 · The paper itself

Abstract

This study used remote sensing images from 1996 to 2016 as the main data source, and selected the average annual ecosystem type net change rate, ecosystem type transfer matrix, and comprehensive index of land development degree, to analyze the macro change of the ecosystem pattern in Xishuangbanna Dai Autonomous Prefecture in the past 20 years. Quantitative analysis was performed on amplitude, rate, type of transition, and degree of disturbance of human activities. The results reveal the spatial and temporal changes of the Xishuangbanna ecosystem and their regional differentiation. The results showed that (1) from 1996 to 2016, Xishuangbanna as a whole was dominated by forest ecosystems and rubber ecosystems, followed by tea, farmland, built-up area, and water ecosystems. (2) During 1996-2016, the ecosystem in Xishuangbanna accounted for more than 99% of the total area has not changed. From 1996 to 2003, the transfer of ecosystem types in Xishuangbanna was mainly between forest and rubber ecosystem. (3) The extent of land development and utilization in Xishuangbanna in the past 20 years is relatively low, slightly lower than the national average, and the overall level of land use is at a medium level of utilization, and over time, the degree of disturbance of human activities has shown an increasing trend.

Indexed as

Conservation of Natural ResourcesEcosystemChinaForestsHuman ActivitiesHumansEcosystemMacro analysisRemote sensingSouthwest ChinaSpatiotemporal variations

Identifiers

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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.