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• W2802187966 abstract "The rapid urbanization in China has corresponded with rapid economic development and beneficial transformations of society that have also exacerbated many sensitive urban problems. Several “ghost cities” have emerged due to the mismatch between the speed of expansion and the rationality of urban planning, especially when the extensive city construction goes far beyond the actual peoples’ demands. This paper presents a novel method for investigating “ghost city” phenomenon based on multisource remotely sensed data. Three components are included. First, coarse-to-fine built-up area extraction and multiscale integration based on nighttime light Defense Meteorological Satellite Program operational linescan system imagery, Chinese land cover/use datasets and maximum green vegetation fraction product. Second, improved “ghost city” index (IGCI) construction and discrimination of “ghost city” phenomenon comprising the rate of land urbanization, the rate of population urbanization, and the intensity of nighttime light. Third, comparison and validation of the IGCI with the traditional “ghost city” index with respect to spatio-temporal distribution and the landscape pattern of “ghost city.” The proposed method was validated using entire districts in Ordos, China. Experimental results demonstrated that the IGCI takes advantages of the spatio-temporal variations of cities and the characteristics of “ghost city” phenomenon. This is also validated by means of the per capita housing construction area and the landscape expansion index, and showed that the urban construction land grows much faster than the population of Ordos. With respect to the accuracy and distribution of spatial patterns, the obtained experimental results exhibit good consistency with results from previous statistical studies." @default.
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• W2802187966 date "2018-07-01" @default.
• W2802187966 modified "2023-10-16" @default.
• W2802187966 title "A Multisource Remotely Sensed Data Oriented Method for “Ghost City” Phenomenon Identification" @default.
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• W2802187966 doi "https://doi.org/10.1109/jstars.2018.2824302" @default.
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