{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:44:03Z","timestamp":1780087443015,"version":"3.54.0"},"reference-count":62,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,9,28]],"date-time":"2017-09-28T00:00:00Z","timestamp":1506556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The urban clusters in the Middle Reaches of the Yangtze River (MRYR) in China include the Chang-Zhu-Tan urban agglomeration, the Wuhan metropolitan area, and the Poyang Lake urban agglomeration. While previous studies of urban expansion in China focused mainly on the coastal regions, this study aimed to investigate urban expansion patterns and factors in the MRYR, which are crucial for urban development in Central China. A neighborhood statistics analysis (NSA) method and a local-optimized threshold method were used to detect urban changes during 1992\u20132011 from the time-series Defense Meteorological Satellite Program\u2019s Operational Linescan System (DMSP\/OLS) nighttime light (NTL) images. The evolution of urban expansion intensity and landscape metrics were analyzed at multiple spatial scales, including the whole region, urban agglomeration, and city scales. Finally, the expanded STochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was built to explore the factors that controlled NTL intensity. The results revealed that urban areas extracted from the NTL data were consistent with those extracted from the Landsat Thematic Mapper data, with an overall accuracy of 81.74% and a Kappa of 0.40. A relatively slow urbanization pace was observed from 1992 to 2002 in the MRYR region, which then accelerated in the period of 2002 to 2007 and then slowed down between 2007 and 2011. Additionally, urban expansion exhibited a radial pattern. The results further indicated that major factors controlling NTL intensity were gross domestic product, followed by total investment in fixed assets, tertiary industry, urban construction area, non-agricultural population, and industrial output in the city clusters. The study provides important insights for further studies on the urbanization processes in the MRYR region.<\/jats:p>","DOI":"10.3390\/rs9101007","type":"journal-article","created":{"date-parts":[[2017,9,28]],"date-time":"2017-09-28T11:22:44Z","timestamp":1506597764000},"page":"1007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Monitoring Urban Clusters Expansion in the Middle Reaches of the Yangtze River, China, Using Time-Series Nighttime Light Images"],"prefix":"10.3390","volume":"9","author":[{"given":"Yanhong","family":"Zou","sequence":"first","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiquan","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Changsha Urban Planning Information Service Center, Changsha 410000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuanda","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhua","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Earth & Environmental Systems, Center for Urban and Environmental Change, Indiana State University, Terre Haute, IN 47809, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2498-0934","authenticated-orcid":false,"given":"Qihao","family":"Weng","sequence":"additional","affiliation":[{"name":"Department of Earth & Environmental Systems, Center for Urban and Environmental Change, Indiana State University, Terre Haute, IN 47809, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.rse.2003.10.015","article-title":"The consequences of urban land transformation on net primary productivity in the United States","volume":"89","author":"Imhoff","year":"2004","journal-title":"Remote Sens. 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