{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T21:51:10Z","timestamp":1770328270471,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2016,7,8]],"date-time":"2016-07-08T00:00:00Z","timestamp":1467936000000},"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 Defense Meteorological Satellite Program (DMSP)\u2019s Operational Line-scan System (OLS) stable nighttime light (NTL) imagery offers a good opportunity for characterizing the extent and dynamics of urban development at the global and regional scales. However, their ability to characterize intra-urban variation is limited due to saturation and blooming of the data values. In this study, we adopted the methods of Mann-Kendall and linear regression to analyze urban dynamics from time series Vegetation Adjusted NTL Urban Index (VANUI) data from 1992 to 2013 in the Southeast United States of America (U.S.A.), which is one of the fastest growing regions in the nation. The newly built urban areas were effectively detected based on the trend analysis. In addition, the VANUI-derived urban areas with an optimal threshold method were found highly consistent with the Landsat-derived National Land Cover Database. The total urbanized areas in large metropolitan areas in southeastern U.S.A. increased from 8524 km2 in 1992 to 14,684 km2 in 2010, accounting for 5% and 9% of the total area, respectively. The results further showed that urban expansion in the region cannot be purely explained by population growth. Our results suggested that the VANUI time series provided an effective method for characterizing the spatiotemporal dynamics of urban extent at the regional scale.<\/jats:p>","DOI":"10.3390\/rs8070578","type":"journal-article","created":{"date-parts":[[2016,7,8]],"date-time":"2016-07-08T09:46:07Z","timestamp":1467971167000},"page":"578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Monitoring Urban Dynamics in the Southeast U.S.A. Using Time-Series DMSP\/OLS Nightlight Imagery"],"prefix":"10.3390","volume":"8","author":[{"given":"Qingting","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1647-1950","authenticated-orcid":false,"given":"Linlin","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2498-0934","authenticated-orcid":false,"given":"Qihao","family":"Weng","sequence":"additional","affiliation":[{"name":"Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanhua","family":"Xie","sequence":"additional","affiliation":[{"name":"Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huadong","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1126\/science.1111772","article-title":"Global consequences of land use","volume":"309","author":"Foley","year":"2005","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0034-4257(98)00098-4","article-title":"Radiance calibration of DMSP-OLS low light imaging data of human settlements","volume":"68","author":"Elvidge","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1080\/014311697218485","article-title":"Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption","volume":"18","author":"Elvidge","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"545","DOI":"10.14358\/PERS.69.5.545","article-title":"Building and evaluating models to estimate ambient population density","volume":"69","author":"Sutton","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1579\/0044-7447-29.3.157","article-title":"Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions","volume":"29","author":"Doll","year":"2000","journal-title":"Ambio"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/15481603.2015.1124488","article-title":"World energy consumption pattern as revealed by DMSP-OLS nighttime light imagery","volume":"53","author":"Xie","year":"2016","journal-title":"GISci. Remote Sens."},{"key":"ref_7","first-page":"727","article-title":"Mapping city lights with nighttime data from the DMSP Operational Linescan System","volume":"63","author":"Elvidge","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S0034-4257(97)00046-1","article-title":"A technique for using composite DMSP-OLS \u201cCity Lights\u201d satellite data to map urban area","volume":"61","author":"Imhoff","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_9","first-page":"1303","article-title":"A comparison of nighttime satellite imagery and population density for the continental united states","volume":"63","author":"Sutton","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1080\/01431160010007015","article-title":"Census from heaven: An estimate of the global human population using night-time satellite imagery","volume":"22","author":"Sutton","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2005.02.002","article-title":"Spatial analysis of global urban extent from DMSP-OLS night lights","volume":"96","author":"Small","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/10106040608542382","article-title":"Mapping \u201cExurbia\u201d in the conterminous United States using nighttime satellite imagery","volume":"21","author":"Sutton","year":"2006","journal-title":"Geocarto Int."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1080\/01431160304982","article-title":"Validation of urban boundaries derived from global night-time satellite imagery","volume":"24","author":"Henderson","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/S0034-4257(03)00081-6","article-title":"Assessing the impact of urban land development on net primary productivity in the southeastern United States","volume":"86","author":"Milesi","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.landurbplan.2012.02.013","article-title":"Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008","volume":"106","author":"Liu","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1007\/s11434-006-2006-3","article-title":"Restoring urbanization process in China in the 1990s by using non-radiance-calibrated DMSP\/OLS nighttime light imagery and statistical data","volume":"51","author":"He","year":"2006","journal-title":"Chin. Sci. Bull."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1016\/j.rse.2011.04.032","article-title":"Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP\/OLS nighttime light data","volume":"115","author":"Zhang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2012.10.022","article-title":"The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity","volume":"129","author":"Zhang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.landurbplan.2014.06.009","article-title":"Quantifying spatiotemporal patterns of urban impervious surfaces in China: An improved assessment using nighttime light data","volume":"130","author":"Ma","year":"2014","journal-title":"Landsc. Urban Plan."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9359","DOI":"10.3390\/rs6109359","article-title":"The integrated use of DMSP-OLS Nighttime Light and MODIS data for monitoring large-scale impervious surface dynamics: A case study in the Yangtze River Delta","volume":"6","author":"Shao","year":"2014","journal-title":"Remote Sens."},{"key":"ref_21","unstructured":"United States Census 2010, Available online: http:\/\/www.census.gov\/2010census\/."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.landurbplan.2012.10.015","article-title":"The changing urban landscape of the continental United States","volume":"110","author":"Kaza","year":"2013","journal-title":"Landsc. Urban Plan."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Terando, A.J., Costanza, J., Belyea, C., Dunn, R.R., McKerrow, A., and Collazo, J.A. (2014). The southern megalopolis: Using the past to predict the future of urban sprawl in the Southeast U.S. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0102261"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"595","DOI":"10.3390\/en20300595","article-title":"A fifteen year record of global natural gas flaring derived from satellite data","volume":"2","author":"Elvidge","year":"2009","journal-title":"Energies"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysic al performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_26","first-page":"345","article-title":"Completion of the 2011 National Land Cover Database for the conterminous United States-representing a decade of land cover change information","volume":"81","author":"Homer","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_27","first-page":"758","article-title":"The change of impervious surface area between 2001 and 2006 in the conterminous United States","volume":"77","author":"Xian","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_28","first-page":"1","article-title":"Towards operational radiometric calibration of NOAA AVHRR imagery in the visible and near-infrared channels","volume":"20","author":"Teillet","year":"1994","journal-title":"Can. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2003.08.010","article-title":"Intercalibration of vegetation indices from different sensor systems","volume":"88","author":"Steven","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_30","first-page":"68","article-title":"A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States","volume":"10","author":"Weng","year":"2008","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3668","DOI":"10.1016\/j.rse.2008.05.009","article-title":"Regional mapping of human settlements in southeastern China with multisensor remotely sensed data","volume":"112","author":"Lu","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1029\/WR020i006p00727","article-title":"A nonparametric trend test for seasonal data with serial dependence","volume":"20","author":"Hirsch","year":"1984","journal-title":"Water Resour. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.rse.2010.10.011","article-title":"Analysis of monotonic greening and browning trends from global NDVI time-series","volume":"115","author":"Schaepman","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.rse.2012.01.017","article-title":"Greenness in semi-arid areas across the globe 1981\u20132007\u2014An Earth Observing Satellite based analysis of trends and drivers","volume":"121","author":"Fensholt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1016\/j.rse.2009.06.001","article-title":"A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data","volume":"113","author":"Cao","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_36","first-page":"49","article-title":"Monitoring urbanization dynamics in India using DMSP\/OLS night time lights and SPOT-VGT data","volume":"23","author":"Pandey","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.apgeog.2015.06.016","article-title":"Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data","volume":"63","author":"Zhou","year":"2015","journal-title":"Appl. Geogr."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1177\/016001700761012710","article-title":"Urban sprawl: Diagnosis and remedies","volume":"23","author":"Brueckner","year":"2000","journal-title":"Int. Reg. Sci. Rev."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2014.03.004","article-title":"A cluster-based method to map urban area from DMSP\/OLS nightlights","volume":"147","author":"Zhou","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_40","first-page":"4095","article-title":"Detecting China\u2019s urban expansion over the past three decades using nighttime light data","volume":"7","author":"Xiao","year":"2014","journal-title":"IEEE J. Sel. Top. Appl."},{"key":"ref_41","first-page":"62","article-title":"Why VIIRS data are superior to DMSP for mapping nighttime lights","volume":"35","author":"Elvidge","year":"2013","journal-title":"Proc. Asia Pac. Adv. Netw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1007\/s13280-013-0468-5","article-title":"A thermodynamic geography: Night-time satellite imagery as a proxy measure of emergy","volume":"43","author":"Coscieme","year":"2014","journal-title":"Ambio"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.rse.2015.12.042","article-title":"Mapping sub-pixel urban expansion in China using MODIS and DMSP\/OLS nighttime lights","volume":"175","author":"Huang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"024004","DOI":"10.1088\/1748-9326\/8\/2\/024004","article-title":"A global fingerprint of macro-scale changes in urban structure from 1999 to 2009","volume":"8","author":"Frolking","year":"2013","journal-title":"Environ. Res. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/7\/578\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:25:44Z","timestamp":1760210744000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/7\/578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,8]]},"references-count":44,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2016,7]]}},"alternative-id":["rs8070578"],"URL":"https:\/\/doi.org\/10.3390\/rs8070578","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,7,8]]}}}