A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery

Abstract

Monitoring urban compositions spatially and temporally is a crucial issue for urban planning and management. Nowadays, remote sensing techniques have been widely applied for urban compositions extraction. Compared with other remote sensing techniques, spectral indices have significant advantages due to their parameter-free and easy implementation. However, existing indices cannot extract different urban compositions well, and some of them can only extract one composition with less attention to other urban compositions. In this study, based on the water- impervious surface-pervious surface (W-I-P) model, a novel urban composition index (UCI) was developed by analyzing the robust features from the global spectral samples. Additionally, a semi-empirical threshold of UCI was proposed to extract different urban compositions (water, impervious surface area and pervious surface area). Four cities of China were selected as study areas, Landsat-8 images and Google Earth images were used for quantitative analysis. Correlation analysis, separability analysis, and accuracy assessment were conducted among UCI and five other existed indices (single and multiple composition indices) at the urban and global scales. Results indicated that UCI had a stronger correlation with the ISA proportion and a higher separability between each urban composition. UCI also achieved the highest overall accuracy and Kappa coefficient in urban compositions extraction. The suggested semi-empirical threshold was also testified to be reliable and can be a reference for practical application. There is convincing evidence that UCI is a simple, efficient, and reliable index for urban compositions extraction.

Development of Global Impervious Surface Area Index for Automatic Spatiotemporal Urban Mapping

“…As a simplistic urban composite index, UCI has better user accuracy and commission error values but lacks in all other aspects of accuracy metrics. Although Zhang, Tian and Liu [15] found overall accuracy is 0.931 and a Kappa coefficient of 0.89, this study uses random sampling points. It gets overall accuracy of 0.830 and a Kappa coefficient of 0.660 for Beijing city.…”

“…On the other hand, Urban Composite Index (UCI) is very simplistic [15]. It uses a single cloud-free image, unlike NUACI and GISAI.…”

“…On the other hand, Zhang, et al [15] proposed Water-Impervious-Pervious (W-I-P) model, where feature spaces have been used to develop an urban composition index (UCI). In the W-I-P model, water, Impervious surface and Pervious surfaces can be derived from a single composition index named UCI.…”

Mapping land cover and vegetation detection in urban areas

“…The land cover difference depends on the specific condition of the urban areas. Some researches have divided urban areas into some classes such as vegetated areas, built-up areas, agricultural, bare, and scrublands [23]; bare land, built-up area, vegetation and water [30]; or bare soil with low vegetation density, built-up, vegetation, and water body [3].…”

Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target Detection

“…S one of the significant components of terrestrial water storage, surface water maintains diverse and complex societies and ecological systems [1][2][3]. Urban surface water is a vital indicator for flood monitoring [4][5], urban hydrological evaluation [6][7][8], and urban heat island effect assessment [9][10] and plays a significant role in urban planning. Given the background that the rapid urbanization Q. Liu, Y. Tian, and L. Zhang are with the School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China (e-mail: lqwei@cug.edu.cn; ygangtian@cug.edu.cn; zhanglihao@cug.edu.cn) process has caused dynamic changes in water bodies, it is of great significance to obtain the water body information and its spatio-temporal distribution quickly and accurately [11][12].…”