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Article: Mapping Global Urban Areas from 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products

TitleMapping Global Urban Areas from 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products
Authors
KeywordsMarkov random field (MRF)
nighttime light (NTL) data
support vector machine (SVM)
urban area
Issue Date2019
Citation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, v. 12, n. 4, p. 1143-1153 How to Cite?
AbstractMapping urban dynamics at the global scale becomes a pressing task with the increasing pace of urbanization and its important environmental and ecological impacts. In this study, we proposed a new approach to mapping global urban areas from 2000 to 2012 by applying a region-growing support vector machine classifier and a bidirectional Markov random field model to time-series nighttime light data. In this approach, both spectrum and spatial-temporal contextual information are employed for an improved urban area mapping. Our results indicate that at the global level, the urban area increased from 625,000 to 1,039,000 km2 during 2000-2012. Most urban areas are concentrated in the region between 30°N and 60°N latitudes. The latitudinal distribution of urban areas from this study is consistent with three land-cover products, including European Space Agency Climate Change Initiative Land Cover dataset, Finer Resolution Observation and Monitoring Global Land Cover, and 30-m Global Land Cover dataset. We found that for several major cities, such as Shanghai, urban areas from our study contain some nonurban land-cover types with intensive human activities. The validation using Landsat 7 ETM+ imagery indicates that the overall accuracies of the mapped urban areas for 2000, 2005, 2008, and 2010 are 86.0%, 88.6%, 89.8%, and 88.7%, respectively, and the Kappa coefficients are 0.72, 0.77, 0.79, and 0.78, respectively. This study also demonstrates that the integration of the spatial-temporal contextual information and the use of bidirectional Markov random field model are effective in improving the accuracy and temporal consistency of urban area mapping using time-series nighttime light data.
Persistent Identifierhttp://hdl.handle.net/10722/329560
ISSN
2023 Impact Factor: 4.7
2023 SCImago Journal Rankings: 1.434
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, Zuoqi-
dc.contributor.authorYu, Bailang-
dc.contributor.authorZhou, Yuyu-
dc.contributor.authorLiu, Hongxing-
dc.contributor.authorYang, Chengshu-
dc.contributor.authorShi, Kaifang-
dc.contributor.authorWu, Jianping-
dc.date.accessioned2023-08-09T03:33:41Z-
dc.date.available2023-08-09T03:33:41Z-
dc.date.issued2019-
dc.identifier.citationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, v. 12, n. 4, p. 1143-1153-
dc.identifier.issn1939-1404-
dc.identifier.urihttp://hdl.handle.net/10722/329560-
dc.description.abstractMapping urban dynamics at the global scale becomes a pressing task with the increasing pace of urbanization and its important environmental and ecological impacts. In this study, we proposed a new approach to mapping global urban areas from 2000 to 2012 by applying a region-growing support vector machine classifier and a bidirectional Markov random field model to time-series nighttime light data. In this approach, both spectrum and spatial-temporal contextual information are employed for an improved urban area mapping. Our results indicate that at the global level, the urban area increased from 625,000 to 1,039,000 km2 during 2000-2012. Most urban areas are concentrated in the region between 30°N and 60°N latitudes. The latitudinal distribution of urban areas from this study is consistent with three land-cover products, including European Space Agency Climate Change Initiative Land Cover dataset, Finer Resolution Observation and Monitoring Global Land Cover, and 30-m Global Land Cover dataset. We found that for several major cities, such as Shanghai, urban areas from our study contain some nonurban land-cover types with intensive human activities. The validation using Landsat 7 ETM+ imagery indicates that the overall accuracies of the mapped urban areas for 2000, 2005, 2008, and 2010 are 86.0%, 88.6%, 89.8%, and 88.7%, respectively, and the Kappa coefficients are 0.72, 0.77, 0.79, and 0.78, respectively. This study also demonstrates that the integration of the spatial-temporal contextual information and the use of bidirectional Markov random field model are effective in improving the accuracy and temporal consistency of urban area mapping using time-series nighttime light data.-
dc.languageeng-
dc.relation.ispartofIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing-
dc.subjectMarkov random field (MRF)-
dc.subjectnighttime light (NTL) data-
dc.subjectsupport vector machine (SVM)-
dc.subjecturban area-
dc.titleMapping Global Urban Areas from 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/JSTARS.2019.2900457-
dc.identifier.scopuseid_2-s2.0-85064718988-
dc.identifier.volume12-
dc.identifier.issue4-
dc.identifier.spage1143-
dc.identifier.epage1153-
dc.identifier.eissn2151-1535-
dc.identifier.isiWOS:000464756600008-

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