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Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities

Short-term characteristics of urban land cover change have been observed and reported from satellite images, although urban landscapes are mainly influenced by anthropogenic factors. These short-term changes in urban areas are caused by rapid urbanization, seasonal climate changes, and phenological...

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Autores principales: Zhang, Hongsheng, Wang, Ting, Zhang, Yuhan, Dai, Yiru, Jia, Jiangjie, Yu, Chang, Li, Gang, Lin, Yinyi, Lin, Hui, Cao, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308402/
https://www.ncbi.nlm.nih.gov/pubmed/30544553
http://dx.doi.org/10.3390/s18124319
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author Zhang, Hongsheng
Wang, Ting
Zhang, Yuhan
Dai, Yiru
Jia, Jiangjie
Yu, Chang
Li, Gang
Lin, Yinyi
Lin, Hui
Cao, Yang
author_facet Zhang, Hongsheng
Wang, Ting
Zhang, Yuhan
Dai, Yiru
Jia, Jiangjie
Yu, Chang
Li, Gang
Lin, Yinyi
Lin, Hui
Cao, Yang
author_sort Zhang, Hongsheng
collection PubMed
description Short-term characteristics of urban land cover change have been observed and reported from satellite images, although urban landscapes are mainly influenced by anthropogenic factors. These short-term changes in urban areas are caused by rapid urbanization, seasonal climate changes, and phenological ecological changes. Quantifying and understanding these short-term characteristics of changes in various land cover types is important for numerous urban studies, such as urbanization assessments and management. Many previous studies mainly investigated one study area with insufficient datasets. To more reliably and confidently investigate temporal variation patterns, this study employed Fourier series to quantify the seasonal changes in different urban land cover types using all available Landsat images over four different cities, Melbourne, Sao Paulo, Hamburg, and Chicago, within a five-year period (2011–2015). The overall accuracy was greater than 86% and the kappa coefficient was greater than 0.80. The R-squared value was greater than 0.80 and the root mean square error was less than 7.2% for each city. The results indicated that (1) the changing periods for water classes were generally from half a year to one and a half years in different areas; and, (2) urban impervious surfaces changed over periods of approximately 700 days in Melbourne, Sao Paulo, and Hamburg, and a period of approximately 215 days in Chicago, which was actually caused by the unavoidable misclassification from confusions between various land cover types using satellite data. Finally, the uncertainties of these quantification results were analyzed and discussed. These short-term characteristics provided important information for the monitoring and assessment of urban areas using satellite remote sensing technology.
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spelling pubmed-63084022019-01-04 Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities Zhang, Hongsheng Wang, Ting Zhang, Yuhan Dai, Yiru Jia, Jiangjie Yu, Chang Li, Gang Lin, Yinyi Lin, Hui Cao, Yang Sensors (Basel) Article Short-term characteristics of urban land cover change have been observed and reported from satellite images, although urban landscapes are mainly influenced by anthropogenic factors. These short-term changes in urban areas are caused by rapid urbanization, seasonal climate changes, and phenological ecological changes. Quantifying and understanding these short-term characteristics of changes in various land cover types is important for numerous urban studies, such as urbanization assessments and management. Many previous studies mainly investigated one study area with insufficient datasets. To more reliably and confidently investigate temporal variation patterns, this study employed Fourier series to quantify the seasonal changes in different urban land cover types using all available Landsat images over four different cities, Melbourne, Sao Paulo, Hamburg, and Chicago, within a five-year period (2011–2015). The overall accuracy was greater than 86% and the kappa coefficient was greater than 0.80. The R-squared value was greater than 0.80 and the root mean square error was less than 7.2% for each city. The results indicated that (1) the changing periods for water classes were generally from half a year to one and a half years in different areas; and, (2) urban impervious surfaces changed over periods of approximately 700 days in Melbourne, Sao Paulo, and Hamburg, and a period of approximately 215 days in Chicago, which was actually caused by the unavoidable misclassification from confusions between various land cover types using satellite data. Finally, the uncertainties of these quantification results were analyzed and discussed. These short-term characteristics provided important information for the monitoring and assessment of urban areas using satellite remote sensing technology. MDPI 2018-12-07 /pmc/articles/PMC6308402/ /pubmed/30544553 http://dx.doi.org/10.3390/s18124319 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Hongsheng
Wang, Ting
Zhang, Yuhan
Dai, Yiru
Jia, Jiangjie
Yu, Chang
Li, Gang
Lin, Yinyi
Lin, Hui
Cao, Yang
Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities
title Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities
title_full Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities
title_fullStr Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities
title_full_unstemmed Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities
title_short Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities
title_sort quantifying short-term urban land cover change with time series landsat data: a comparison of four different cities
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308402/
https://www.ncbi.nlm.nih.gov/pubmed/30544553
http://dx.doi.org/10.3390/s18124319
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