Abstract
Analysis of urban distribution and its expansion using remote sensing data has received increasing attention in the past three decades, but little research has examined spatial patterns of urban distribution and expansion with buffer zones in different directions. This research selected Hangzhou metropolis as a case study to analyze spatial patterns and dynamic changes based on time-series urban impervious surface area (ISA) datasets. ISA was developed from Landsat imagery between 1991 and 2014 using a hybrid approach consisting of linear spectral mixture analysis, decision tree classifiers, and post-processing. The spatial patterns of ISA distribution and its dynamic changes in eight directions—east, southeast, south, southwest, west, northwest, north, and northeast—at the temporal scale were analyzed with a buffer zone-based approach. This research indicated that ISA can be extracted from Landsat imagery with both producer and user accuracies of over 90%. ISA in Hangzhou metropolis increased from 146 km2 in 1991 to 868 km2 in 2014. Annual ISA growth rates were between 15.6 km2 and 48.8 km2 with the lowest growth rate in 1994–2000 and the highest growth rate in 2005–2010. Urban ISA increase before 2000 was mainly due to infilling within the urban landscape, and, after 2005, due to urban expansion in the urban-rural interfaces. Urban expansion in this study area has different characteristics in various directions that are influenced by topographic factors and urban development policies.
Highlights
Rapid population migrations from rural to urban regions and improved economic conditions in China have resulted in unprecedented urban expansion rates in the past three decades [1,2,3,4,5,6].urbanization generates serious environmental problems such as air pollution, urban heat island (UHI), and poor water quality, and produces challenges in urban planning and management [7,8,9,10,11]
We examined the impacts of elevation and slope on impervious surface area (ISA) distribution and dynamic change in Hangzhou metropolis
The hybrid approach for mapping ISA distribution produces an Overall accuracy (OA) of over 95% according to the accuracy assessment results for 2010 and 2014 (Table 2)
Summary
Rapid population migrations from rural to urban regions and improved economic conditions in China have resulted in unprecedented urban expansion rates in the past three decades [1,2,3,4,5,6].urbanization generates serious environmental problems such as air pollution, urban heat island (UHI), and poor water quality, and produces challenges in urban planning and management [7,8,9,10,11]. Rapid population migrations from rural to urban regions and improved economic conditions in China have resulted in unprecedented urban expansion rates in the past three decades [1,2,3,4,5,6]. Obtaining timely urban distribution and dynamic change data is necessary to examine the urban-environmental interactions and relationships [6,12,13,14]. In the past four decades, many studies have been conducted to explore technology/methods to accurately map urban land-use/cover distributions and detect their dynamic changes [15,16,17,18,19]. Change detection can be based on spectral responses (e.g., spectral bands, vegetation indices, image transforms), spatial features (textures, objects), and classified
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