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In September 2015, the members of United Nations adopted the 2030 Agenda for Sustainable Development with universal applicability of 17 Sustainable Development Goals (SDGs) and 169 targets. The SDGs are consequential for the development of the countries in the Nile watershed, which are a ffected by water scarcity and experiencing rapid urbanization associated with population growth. Earth Observation (EO) has become an important tool to monitor the progress and implementation of specific SDG targets through its wide accessibility and global coverage. In addition, the advancement of algorithms and tools deployed in cloud computing platforms provide an equal opportunity to use EO for developing countries with limited technological capacity. This study applies EO and cloud computing in support of the SDG 6 “clean water and sanitation” and SDG 11 “sustainable cities and communities” in the seven Nile watershed countries through investigations of EO data related to indicators of water stress (Indicator 6.4.2) and urbanization and living conditions (Indicators 11.3.1 and 11.1.1), respectively. Multiple approaches including harmonic, time series and correlational analysis are used to assess and evaluate these indicators. In addition, a contemporary deep-learning classifier, fully convolution neural networks (FCNN), was trained to classify the percentage of impervious surface areas. The results show the spatial and temporal water recharge pattern among different regions in the Nile watershed, as well as the urbanization in selected cities of the region. It is noted that the classifier trained from the developed countries (i.e., the United States) is effective in identifying modern communities yet limited in monitoring rural and slum regions.


This article was originally published in Remote Sensing, volume 12, in 2020.

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This work is licensed under a Creative Commons Attribution 4.0 License.



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