Evidence map›Paper›PMID 38424478›Full record

ArticleBMC ecology and evolution2024

Detecting the effects of opencast mining on ecosystem services value in arid and semi-arid areas based on time-series remote sensing images and Google Earth Engine (GEE).

Qinyu Wu, Liya Yang, Jiaxin Mi

Abstract read
In one paragraph

Article in BMC ecology and evolution, 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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Qinyu WuSchool of Public Policy & Management, China University of Mining and Technology, Xuzhou, 221116, China. qywu@cumt.edu.cn.
Liya YangKey Laboratory of Mine Ecological Effects and Systematic Restoration, Ministry of Natural Resources, Beijing, 100081, China.
Jiaxin MiSchool of Public Policy & Management, China University of Mining and Technology, Xuzhou, 221116, China. jxmi@cumt.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ecosystem Services Value (ESV) are the various beneficial functions and products that natural ecosystems provide to humans, and are important indicators for evaluating ecosystem conditions and human well-being. Opencast mining is one of the human activities that severely damage the surface environment, but its long-term impact on ecosystem services lacks systematic assessment. This study takes the Ordos opencast mining area as an example, and calculates the value of ESV from 1990 to 2020 based on the Google Earth Engine platform. Mann-Kendall Tau-b with Sen's Method (Sen + mk test) and Joinpoint regression model were used to analyzes its spatiotemporal variation characteristics. Further revealed the impacts of opencast mining on ESV as well as the trend of ESV changes. The results show that: (1) The dynamic ESV levels in the study area fluctuated considerably from 1990 to 2020 with an overall decreasing trend of 89.45%. (2) Among nine types ecosystem services, most of them were significantly different (p < 0.001) between mining areas and control areas, with biodiversity protection (BP), climate regulation (CR), gas regulation (GR), soil formation and retention (SFR), water supply (WS) and waste treatment (WT) showed a significant decrease between 1990 and 2020. (3) In the past 30 years, the ESV of the study area showed an overall improvement trend, where the improved area accounted for 48.45% of the total area of the study area. However, the degraded area also accounted for 21.28, and 17.19% of the area belonged to severe degradation. With 67% of the significantly degraded areas distributed within mining concessions. (4) The trend of ESV changes in the mining impact areas and the control area showed significant differences. The ESV of the control area increased continuously, with an average annual percentage change (AAPC) of 0.7(95%CI:0.50 ~ 0.9, P < 0.001) from 1990 to 2020; while the ESV of the mining impact areas first stabilized and then decreased significantly, with an AAPC of - 0.2(95%CI:- 0.3 ~ - 0.1,P < 0.001) from 1990 to 2020. This study provides scientific support for formulating ecosystem management, restoration plans, and payment for ecosystem service policies, which is conducive to achieving regional sustainable development and improving human well-being.

Indexed as

EcosystemRemote Sensing TechnologyConservation of Natural ResourcesHumansSearch EngineSoilSoilEcosystem services valueGoogle earth engine platformJoinpoint regression modelMining impactOrdos opencast mining areasSen + MK test

Identifiers

PMID38424478
PMCPMC10902960

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LicenceCC BY
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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.