Object-oriented extraction of urban impervious surface coverage

2011 ◽  
Author(s):  
Xiaotian Ge ◽  
Xiaoping Lu
Keyword(s):  
Surface Coverage ◽  
Object Oriented ◽  
Impervious Surface ◽  
Urban Impervious Surface

scholarly journals APPLING REMOTE SENSING TECHNIQUE TO MONITORING SPATIAL EXPANSION OF IMPORTANT CITIES IN CHINA-INDOCHINA PENINSULA ECONOMIC CORRIDOR

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1229-1235
Author(s):  
J. Li ◽  
H. Wang ◽  
G. Wang ◽  
H. Zhai ◽  
M. Han ◽  
...  
Keyword(s):  
Random Forest ◽  
Urban Expansion ◽  
Object Oriented ◽  
Impervious Surface ◽  
Indochina Peninsula ◽  
Landscape Pattern Metrics ◽  
Random Forest Classification ◽  
Urban Impervious Surface ◽  
Forest Classification ◽  
Expansion Speed

Since twentieth Century, the process of economic globalization has made great progress, and Southeast Asia has developed rapidly under the background of international industrial transferring. In this paper, the 6 important nodes cities in China - Indochina Peninsula along the economic corridor are took as study area. The main data is time series Landsat data. The method of object-oriented random forest classification was used to extract the classification results of study area from 2000 to 2015. The urban expansion of the node cities was evaluated by calculating the expansion speed of the impervious surface and the landscape pattern metrics. The results indicated that the method of object oriented random forest classification can effectively extract the urban impervious surface. the overall accuracy is over 81 %, and the Kappa coefficient is over 0.82. In the past 15 years, the expansion speed of Vientiane city was fastest in 6 countries. The area of urban impervious surface expanded 8 times than that of 2000.The pattern of expansion is summarized as “gather first-diffuse then”, “diffuse first-gather then” and “gather”. Overall, the process of urbanization of these cities are still in the rising period.

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.

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