Over the last decade, Dubai emirate witnessed a vast, rapidly growing population, that doubled since 2008. Nowadays, Dubai considers as the most populated emirate within the United Arab Emirates (UAE). With such an increasing population and new urban developments, sustainable urban planning procedures play an essential role in Dubai's environmental quality such as air quality, and pollution. Therefore, this study will utilize the Remote Sensing and Geographic Information system (GIS) to investigate Dubai's environmental quality by addressing and locating green areas and pollution percentages within each district. The study methodology is divided into three steps. First, Landsat Satellite medium spatial resolution and multi-spectral imagery will be used as an input for segmentation and object-based analysis. Considering the spectral and spatial signatures for green areas machine learning techniques will be adopted to select the most significant features to classify and extract green areas. Second, using environmental relational indices, green areas percentages will be quantitatively compared to Sentinel air quality data, such as NO2 and SO2, as well as the population density maps. Finally, GIS techniques will be used to create Dubai Environmental Critical Map (DECM), to locate districts with limited green areas and high pollution to improve environmental standards. The study results can be used as a measure for the municipality policymakers to ensure sustainable urban development for a healthy living.
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.
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