scholarly journals Examining Urban Impervious Surface Distribution and Its Dynamic Change in Hangzhou Metropolis

2016 ◽  
Vol 8 (3) ◽  
pp. 265 ◽  
Author(s):  
Longwei Li ◽  
Dengsheng Lu ◽  
Wenhui Kuang
Keyword(s):  
Dynamic Change ◽  
Impervious Surface ◽  
Surface Distribution ◽  
Urban Impervious Surface

The Spatiotemporal Evolution of Urban Impervious Surface for Chengdu, China

2021 ◽  
Vol 87 (7) ◽  
pp. 491-502
Author(s):  
Mujie Li ◽  
Zezhong Zheng ◽  
Mingcang Zhu ◽  
Yue He ◽  
Jun Xia ◽  
...  
Keyword(s):  
Neural Networks ◽  
Growth Rate ◽  
Urban Area ◽  
Landsat Imagery ◽  
Impervious Surface ◽  
Kappa Coefficient ◽  
Spatiotemporal Evolution ◽  
Urban Impervious Surface ◽  
The Past ◽  
Secondary Industry

The spatiotemporal evolution of an impervious surface (IS) is significant for urban planning. In this paper, the IS was extracted and its spatiotemporal evolution for the Chengdu urban area was analyzed based on Landsat imagery. Our experimental results indicated that convolutional neural networks achieved the better performance with an overall accuracy of 98.32%, Kappa coefficient of 0.98, and Macro F1 of 98.28%, and the farmland was replaced by IS from 2001 to 2017, and the IS area (ISA) increased by 51.24 km2; that is, the growth rate was up to 13.8% in sixteen years. According to the landscape metrics, the IS expanded and agglomerated into large patches from small fragmented ones. In addition, the gross domestic product change of the secondary industry was similar to the change of ISA between 2001 and 2017. Thus, the spatiotemporal evolution of IS was associated with the economic development of the Chengdu urban area in the past sixteen years.

scholarly journals URBAN IMPERVIOUS SURFACE EXTRACTION BASED ON REMOTE SENSING IMAGES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 357-360
Author(s):  
X. Y. Long ◽  
Z. F. Shao ◽  
X. X. Feng
Keyword(s):  
Rapid Development ◽  
Impervious Surface ◽  
Support Vector ◽  
Impervious Surfaces ◽  
Surface Distribution ◽  
Human Environment ◽  
Important Indicator ◽  
Urbanization Level ◽  
Urban Impervious Surfaces

Abstract. Urban planning and constructions affect spatial patterns of urban impervious surfaces, which in turn modify the urban environment and affect human-environment interactions. Impervious surfaces can redistribute precipitation patterns, and the perviousness–imperviousness ratio is considered as one important indicator for assessing the degree of urbanization and the quality of urban eco-environment. The spatial distribution and dynamics of impervious surfaces contribute to better understand urbanization and its impacts on regional or urban hydrological environment, surface temperature balance and biodiversity, etc. Hengqin new area is located in Hengqin island, south of Zhuhai city, adjacent to Hong Kong and Macao. It was officially established as a free trade zone in 2009. Due to the rapid development of Hengqin in recent years, this paper discusses Landsat8 imagery of time series in mapping impervious surfaces, and analysis the changes of impervious surface in Hengqin from 2013 to 2018. Support vector machine (SVM) is a classical classifier that is supervised learning models and that use related learning algorithms to analyze data for classification and regression analysis (Vapnik, 1995). In this paper, we obtain the impervious surface distribution via SVM and get good accuracy. The impervious surface distribution of Hengqin in six years show that the quickly improve of urbanization level. However, with the development of urbanization, the impervious surface has not changed dramatically, which shows that the decision-making of urban managers is correct. After the urbanization construction in Hengqin, it is still an ecological island.

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