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利用时间序列 Landsat 数据定量研究城市土地覆盖变化:四个不同城市的比较。

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

机构信息

Institute of Space and Earth Information Science, The Chinese University of Hong Kong, New Territories, Hong Kong 999077, China.

Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518000, China.

出版信息

Sensors (Basel). 2018 Dec 7;18(12):4319. doi: 10.3390/s18124319.

DOI:10.3390/s18124319
PMID:30544553
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6308402/
Abstract

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.

摘要

城市土地覆盖变化的短期特征已经可以从卫星图像中观察到并报告,尽管城市景观主要受到人为因素的影响。城市地区的这些短期变化是由快速城市化、季节性气候变化和物候生态变化引起的。量化和理解各种土地覆盖类型的这些短期变化特征对于许多城市研究非常重要,例如城市化评估和管理。许多先前的研究主要集中在一个研究区域,数据量不足。为了更可靠和自信地研究时间变化模式,本研究使用傅立叶级数,利用五年期间(2011 年至 2015 年)所有可用的四个城市(墨尔本、圣保罗、汉堡和芝加哥)的所有 Landsat 图像,量化了不同城市土地覆盖类型的季节性变化。总体精度大于 86%,kappa 系数大于 0.80。每个城市的 R-squared 值大于 0.80,均方根误差小于 7.2%。结果表明:(1)在不同地区,水体的变化期通常为半年至一年半;(2)在墨尔本、圣保罗和汉堡,城市不透水面的变化期约为 700 天,而在芝加哥,变化期约为 215 天,这实际上是由于使用卫星数据混淆各种土地覆盖类型而不可避免的分类错误造成的。最后,分析和讨论了这些量化结果的不确定性。这些短期特征为使用卫星遥感技术监测和评估城市地区提供了重要信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/2b41e10711f3/sensors-18-04319-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/361f0d48b2b2/sensors-18-04319-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/0d81f4fae28e/sensors-18-04319-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/a3439a7f15c3/sensors-18-04319-g003a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/6bd371c42a85/sensors-18-04319-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/adf0af692a8a/sensors-18-04319-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/9f65d64df107/sensors-18-04319-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/2b41e10711f3/sensors-18-04319-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/361f0d48b2b2/sensors-18-04319-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/0d81f4fae28e/sensors-18-04319-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/a3439a7f15c3/sensors-18-04319-g003a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/6bd371c42a85/sensors-18-04319-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/adf0af692a8a/sensors-18-04319-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/9f65d64df107/sensors-18-04319-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2137/6308402/2b41e10711f3/sensors-18-04319-g007.jpg

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