Evidence map›Paper›PMID 39484209›Full record

ArticlePeerJ2024

Evaluation and driving factors of ecological environment quality in the Tarim River basin based on remote sensing ecological index.

Congcang Tang, Qiao Li, Hongfei Tao, Mahemujiang Aihemaiti, Zhenxia Mu, Youwei Jiang

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Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Congcang TangCollege of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, China.
Qiao LiCollege of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, China.
Hongfei TaoCollege of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, China.
Mahemujiang AihemaitiCollege of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, China.
Zhenxia MuCollege of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, China.
Youwei JiangCollege of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Changes in the ecological environment quality (EEQ) in the main inland Tarim River Basin in China substantially impact the regional development. Indeed, comprehensive ecological environment measures have been implemented in the Tarim River Basin since 2000. In this context, the main objective of the present study was to investigate the spatiotemporal evolution of the EEQ and monitor the effectiveness of ecological restoration measures in the Tarim River Basin over the 2000-2020 period using remote sensing data. First, a Remote Sensing Ecological Index (RSEI) was constructed based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Second, the spatial distributions and factors of the RSEI were analyzed by using Moran's Index and Geodetector. The results indicated that the overall RSEI values for the Tarim River Basin increased from 0.22 in 2000 to 0.25 in 2020. Moreover, the values for areas with poor EEQ decreased from 50.7% to 44.73%, while those with moderate EEQ increased from 11.45% to 16.91%. Therefore, the results demonstrated a slight overall improvement in the EEQ of the study area over the 2000-2020 period. On the other hand, the EEQ in the Tarim River Basin exhibited a significant spatial autocorrelation in the 2000-2020 period, with a relatively stable overall spatial distribution. Areas with high-high aggregation were distributed in the high-elevation mountainous areas in the western, northern, and southern parts of the study area. In contrast, areas with low-low aggregation were observed in the central and eastern low-elevation desert areas. The EEQ in the Tarim River Basin was driven by the interactions of several factors, including the normalized difference vegetation index, land surface moisture, land surface temperature, normalized differential build-up and bare soil index, and elevation. In particular, heat was the main driving factor that severely impacted the EEQ in the study area. Indeed, increase in the heat values could directly enhance meltwater runoff from glaciers in the basin, thereby resulting in short-term improvement in the basin EEQ. Furthermore, rapid urbanization from 2015 to 2020 resulted in a decrease in the average RSEI value of the Tarim River Basin by 0.1 over this period, consequently, the EEQ level decreased slightly. Briefly, the EEQ in the Tarim River Basin showed an overall increasing trend from 2000 to 2020, further demonstrating the effectiveness of a series of implemented ecological restoration measures in the Tarim River Basin over this period.

Indexed as

Environmental MonitoringRemote Sensing TechnologyRiversChinaConservation of Natural ResourcesEcosystemGeodetectorMoran’s indexRemote sensing ecological indexTarim river basin

Identifiers

PMID39484209
PMCPMC11526787

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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.