Unstable changes in ecological quality of the four major sandy lands in northern China based on Google Earth Engine

IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Ecological Indicators Pub Date : 2025-02-01 DOI:10.1016/j.ecolind.2025.113195
Haowen Ma , Yongfang Wang , Enliang Guo , Shan Yin , Yao Kang , Yanli Wang , Jiapei Zhao , Jisiguleng Wu , Quanfei Mu , Delong Zhou
{"title":"Unstable changes in ecological quality of the four major sandy lands in northern China based on Google Earth Engine","authors":"Haowen Ma ,&nbsp;Yongfang Wang ,&nbsp;Enliang Guo ,&nbsp;Shan Yin ,&nbsp;Yao Kang ,&nbsp;Yanli Wang ,&nbsp;Jiapei Zhao ,&nbsp;Jisiguleng Wu ,&nbsp;Quanfei Mu ,&nbsp;Delong Zhou","doi":"10.1016/j.ecolind.2025.113195","DOIUrl":null,"url":null,"abstract":"<div><div>Based on the Google Earth Engine (GEE) platform, this study constructed a Remote Sensing-based Ecological Index (RSEI) model for four major sandy lands in northern China from 2000 to 2022. RESI incorporates a Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) to mine abrupt change years and key information within the time series data. Moreover, it combines Geographical Detector (GD) to conduct a detailed quantitative study on the spatiotemporal variation and driving mechanisms of the Ecological Quality (EQ) in these sandy lands at different times and discusses possible causes for abrupt changes in the EQ. The results indicated that from 2000 to 2022, the EQ of all four major sandy lands improved to varying degrees. However, the introduction of BEAST revealed that the RSEI of all four sandy lands experienced abrupt changes in the latter half of the study period, with the EQ in the Horqin and Hulun Buir showing potential for continued improvement post-change. By contrast, the EQ of the Mu Us and Otindag showed a downward trend within certain intervals, indicating instability under adverse natural conditions. Regarding the driving mechanisms, the spatial distribution of the EQ in the four sandy lands was influenced by natural and anthropogenic factors, displaying complex interactions of driving factors within each sandy land. Among them, in the Mu Us, Otindag, and Hulun Buir, natural factors played a dominant role, whereas in the Horqin, anthropogenic factors played a dominant role.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113195"},"PeriodicalIF":7.0000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Indicators","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1470160X25001244","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Based on the Google Earth Engine (GEE) platform, this study constructed a Remote Sensing-based Ecological Index (RSEI) model for four major sandy lands in northern China from 2000 to 2022. RESI incorporates a Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) to mine abrupt change years and key information within the time series data. Moreover, it combines Geographical Detector (GD) to conduct a detailed quantitative study on the spatiotemporal variation and driving mechanisms of the Ecological Quality (EQ) in these sandy lands at different times and discusses possible causes for abrupt changes in the EQ. The results indicated that from 2000 to 2022, the EQ of all four major sandy lands improved to varying degrees. However, the introduction of BEAST revealed that the RSEI of all four sandy lands experienced abrupt changes in the latter half of the study period, with the EQ in the Horqin and Hulun Buir showing potential for continued improvement post-change. By contrast, the EQ of the Mu Us and Otindag showed a downward trend within certain intervals, indicating instability under adverse natural conditions. Regarding the driving mechanisms, the spatial distribution of the EQ in the four sandy lands was influenced by natural and anthropogenic factors, displaying complex interactions of driving factors within each sandy land. Among them, in the Mu Us, Otindag, and Hulun Buir, natural factors played a dominant role, whereas in the Horqin, anthropogenic factors played a dominant role.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
Ecological Indicators
Ecological Indicators 环境科学-环境科学
CiteScore
11.80
自引率
8.70%
发文量
1163
审稿时长
78 days
期刊介绍: The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published. • All aspects of ecological and environmental indicators and indices. • New indicators, and new approaches and methods for indicator development, testing and use. • Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources. • Analysis and research of resource, system- and scale-specific indicators. • Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs. • How research indicators can be transformed into direct application for management purposes. • Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators. • Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.
期刊最新文献
Unravelling the spatiotemporal trade-offs and synergies among hydrological ecosystem services in a large floodplain lake Exploring the spatiotemporal variation of carbon storage on Hainan Island and its driving factors: Insights from InVEST, FLUS models, and machine learning The need for advancing algal bloom forecasting using remote sensing and modeling: Progress and future directions Effectiveness and driving mechanisms of ecological conservation and restoration in Sichuan Province, China Cooling the land surface: Ecosystem health and water availability drive the landscape capacity to mitigate climate change
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1