Smart City Gnosys
Smart city article details
| Title | Multi Temporal Satellite Images For Growth Detection And Urban Sprawl Analysis; Dubai City, Uae |
|---|---|
| ID_Doc | 38076 |
| Authors | Aldogom D.; Aburaed N.; Al-Saad M.; Al Mansoori S.; Al Shamsi M.R.; Al Maazmi A.A. |
| Year | 2019 |
| Published | Proceedings of SPIE - The International Society for Optical Engineering, 11157 |
| DOI | http://dx.doi.org/10.1117/12.2533097 |
| Abstract | Urbanization is a spatiotemporal process that has significant role in economic, social, and environmental structures. Spatiotemporal analysis for urban growth is vital for city management planning. With highly recognized financial and social developing trends, Dubai City, UAE appears as one of most challenging cities in terms of research and preparation toward a smart city aspect. Integrated technologies of remote sensing and geographic information system (GIS) facilitate urban growth detection and its relation to population distribution. In this study Multi-temporal, medium-resolution Landsat images were used to detect and analyze the urbanization trend in Dubai over the last three decades(1986-2019). Moreover, the influence of urbanization on the aspects of smart city tendency was investigated. The study methodology consisted of three parts. First, classification algorithms along with change detection, segmentation, and extraction of urban areas were used to obtain land Use/land Cover (LULC) maps. Second, Shannon's entropy was used to investigate Dubai's growth toward compact or sprawl city based on two city centers and a major highway. Finally, CA-Markov, associated with the digital elevation model and road map of Dubai, was used to simulate the urban change for 2030, 2050, and 2100. With more than 90% overall accuracy, the statistical analysis for LULC percentages and Shannons entropy values indicated that Dubai experienced a considerable increase in urban fabric while maintaining a compact growth. CA-Markov model estimated 3% urban growth by 2030, which would result in potential loss of green areas and open spaces. This study could be used in improving planning and management methods to achieve sustainable urban growth. © 2019 SPIE. |
| Author Keywords | image classification; land Use/land Cover; Landsat; Shannons entropy; Spatiotemporal analysis; sprawl analysis |
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