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GEE-Based Ecological Environment Variation Analysis under Human Projects in Typical China Loess Plateau Region

Tang, Jingya 1; Sui, Lichun ; Ma, Ting; Dan, Yang; Yang, Qian; Zhao, Ruofan; Qiang, Xinhuan
1 Institut für Geographie und Geoökologie (IFGG), Karlsruher Institut für Technologie (KIT)

Abstract:

The China Loess Plateau (CLP) is a unique geomorphological unit with abundant coal resources but a fragile ecological environment. Since the implementation of the Western Development plan in 2000, the Grain for Green Project (GGP), coal mining, and urbanization have been extensively promoted by the government in the CLP. However, research on the influence of these human projects on the ecological environment (EE) is still lacking. In this study, we investigated the spatial–temporal variation of EE in a typical CLP region using a Remote Sensing Ecological Index (RSEI) based on the Google Earth Engine (GEE). We obtained a long RSEI time series from 2002–2022, and used trend analysis and rescaled range analysis to predict changing trends in EE. Finally, we used Geodetector to verify the influence of three human projects (GGP, coal mining, and urbanization). Our results show that GGP was the major driving factor of ecological changes in the typical CLP region, while coal mining and urbanization had significant local effects on EE. Our research provides valuable support for ecological protection and sustainable social development in the relatively underdeveloped region of northwest China.


Verlagsausgabe §
DOI: 10.5445/IR/1000160951
Veröffentlicht am 26.07.2023
Originalveröffentlichung
DOI: 10.3390/app13084663
Web of Science
Zitationen: 6
Dimensions
Zitationen: 8
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Geographie und Geoökologie (IFGG)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 2076-3417
KITopen-ID: 1000160951
Erschienen in Applied Sciences
Verlag MDPI
Band 13
Heft 8
Seiten Art.-Nr. 4663
Vorab online veröffentlicht am 07.04.2023
Nachgewiesen in Dimensions
Web of Science
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