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A time-series analysis of urbanization-induced impervious surface area extent in the Dianchi Lake watershed from 1988–2017

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Chinese urbanization has drawn widespread attention since the 21st century. Understanding urban expansion at a watershed scale including cities of different sizes is important for improving our current knowledge of the urban extent and its impact on the hydrological cycle, water management, surface energy balances, and biodiversity. Impervious surface area (ISA) can be used as a synthesized quantifiable index to reflect the intensity of natural ecosystems changing into urban ecosystems. It is important to understand ISA patterns and characteristics, which requires long-term impervious surface data at a high spatial and temporal resolution. Previous methods of ISA estimation mainly focused on the spectral differences between ISA and other land covers, and most studies were inclined to use one or a few images without fully considering the long time series of the temporal domain of the reflective data on remote-sensing images. This assessed the Dianchi Lake watershed as a case study area to illustrate ISA change characteristics in the context of natural and cultural conditions. Firstly, more than two hundred Landsat images (from 1988 to 2017) were downloaded through the United States Geological Survey (USGS) online portal. Secondly, the improved normalized difference build-up index (INDBI) and linear spectral mixture analysis (LSMA) algorithm were combined to apply the method to a series of ISA maps of the Dianchi Lake watershed at an annual resolution. Thirdly, ISA extent characteristics of the Dianchi Lake watershed were analysed from trend and pattern aspects. The results show that the proposed method was highly reliable for detecting and characterizing change, with an extracted ISA accuracy of 92.51%, based on a sample of independent validation points. The Dianchi Lake watershed has begun to adopt ‘Rashly Advancing’ and ‘Great Leap Forward’ strategies of urbanization.
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Document Type: Research Article

Affiliations: 1: School of Information Science and Technology, Yunnan Normal University, Yunnan, Kunming, China 2: School of Tourism and Geographical Science, Yunnan Normal University, Yunnan, Kunming, China 3: Department of Geology and Geography, Georgia Southern University, Statesboro, GA, USA

Publication date: January 17, 2019

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