Article(id=1314918444869448665, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918356222829028, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743523200000, receivedDateStr=2025-04-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430587259, onlineDateStr=2026-10-08, pubDate=1763568000000, pubDateStr=2025-11-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430587259, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430587259, creator=13701087609, updateTime=1791430587259, updator=13701087609, issue=Issue{id=1314918356222829028, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='11', pageStart='5921', pageEnd='6512', issueExtLink='null', onlineDate='null', pubDate='1763568000000', pubDateStr='2025-11-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430566125, creator='13701087609', updateTime=1791431596967, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314922679942078535, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918356222829028, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314922679942078536, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918356222829028, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=6353, endPage=6365, ext={EN=ArticleExt(id=1314918445112718298, articleId=1314918444869448665, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Multi-Scale impacts of street view-based green view index on urban thermal environment within the third ring road of nanchang, columnId=1234106388083954308, journalTitle=China Environmental Science, columnName=Environmental Ecology, runingTitle=null, highlight=null, articleAbstract=

This study was conducted within the area enclosed by the Third Ring Road of Nanchang, China. The Green View Index (GVI) was calculated using semantic segmentation of panoramic street view imagery, while Land Surface Temperature (LST) was retrieved through the Google Earth Engine (GEE) platform. Hotspot analysis, profile analysis, and Geographically Weighted Regression (GWR) model were employed to investigate the spatiotemporal patterns of GVI and LST and explore the scale-dependent impact of GVI on LST. The main conclusions are as follows: (1) From 2017 to 2021, the entire study area showed a declining trend, with 60.9% of the area experiencing a reduction in GVI. However, the local GVI had been enhanced and had formed a continuous green advantage zone, and the proportion of statistically significant hotspots increased from 31.6% to 41.1%. (2) During the same period, the overall LST had increased, and the Urban Heat Island (UHI) effect had intensified. The proportion of high and sub-high temperature areas rose sharply from 6.5% to 25.0%, and the proportion of strong and extremely strong UHI areas expanded from 46.0% to 50.8%. (3) The relationship between GVI and LST was foundto be spatial non-stationarity, with R² values varyingacross different grid scales. At the 500m5500m resolution, the GWR model achieved the best performance, with R² values of 0.58, 0.65, and 0.70 in 2017, 2019, and 2021, respectively. (4) A significant scale effect was identified in the impact of GVI on LST, and the 500m×500m grid was identified as the optimal analysis scale within the study area. The researchresults reveal the spatiotemporal variations of urban GVI and its multi-scale impacts on LST. This not only helps to understandthe impactof urban greening on LST from the perspective of three-dimensional greenvolume, but also providestheoretical references for urban planning.

, authors=Bei-jie GAO^{1, 2, 3}, Qiu-lin XIONG^{1, 2, 3, *}, Wen-bo CHEN^{1, 2, 3}, Hai-qing HE^{1, 2, 3}, Yi-rong HUANG^{1, 2, 3}, Qing-wen HONG^{1, 2, 3}, Ke-ke YANG^{1, 2, 3}, Xin-yue ZHAO^{1, 2, 3}, authorsList=Bei-jie GAO, Qiu-lin XIONG, Wen-bo CHEN, Hai-qing HE, Yi-rong HUANG, Qing-wen HONG, Ke-ke YANG, Xin-yue ZHAO, authorCompany=null, correspAuthors=Qiu-lin XIONG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314918445335016411, articleId=1314918444869448665, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于街景数据的南昌市三环内绿视率对城市热环境的多尺度影响分析, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

选取南昌市三环内为研究区,利用语义分割技术计算街景图像的绿视率（Green View Index,GVI）,借助Google Earth Engine（GEE）平台反演LST.采用热点分析、剖面分析和地理加权回归模型（Geographically Weighted Regression,GWR）,系统分析了GVI和LST的时空格局,并深入解析了不同网格尺度下GVI对LST的影响差异.结果表明:2017~2021年,南昌市三环内整体GVI水平呈现下降趋势,研究区60.9%的区域GVI下降;但局部GVI提升并形成持续绿化优势带,极显著热点占比由31.6%增至41.1%.同期,整体LST升高,热岛效应加剧;南昌市三环内高温及次高温面积占比从6.5%升至25.0%,强热岛和极强热岛区域面积从46.0%增至50.8%.三环内GVI对LST的影响表现出空间非平稳性,不同尺度R²不同;其中500m×500m尺度下R²最高,2017年、2019年、2021年分别为0.58、0.65、0.70.GVI对LST的影响存在空间尺度效应,南昌市三环线内的最佳网格尺度为500m×500m.研究结果揭示了城市GVI的时空变化及其对LST的多尺度影响,不仅有助于从三维绿量视角理解城市绿化对LST的影响,而且可为城市规划提供理论参考.

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高蓓杰（2000-），女，陕西韩城人，东华理工大学硕士研究生，主要从事生态环境遥感研究．gbj5544@163.com.

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高蓓杰（2000-），女，陕西韩城人，东华理工大学硕士研究生，主要从事生态环境遥感研究．gbj5544@163.com.

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