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Sea water desalination has experienced an unprecedented growth in the GCC countries to meet the ever growing demand of water for household consumption as well as for industrial and agricultural purposes. However, the current technologies used in water desalination are also accompanied by negative environmental impacts especially on the surrounding marine ecosystems. Since major seawater desalination plants are located by the shoreline, the main environmental considerations in desalination are water intakes and sea outfall discharges. We intent through this study to evaluate the potential of current polar orbiting satellites in evaluating the impact of desalination plant discharges, usually used to dispose of brine waste stream, on surrounding ecosystems and water quality. The objective of this project is to develop an automated approach for monitoring water quality and temperature (thermal properties) surrounding the discharges of desalination plants in the UAE coastal areas. Visible and thermal measurements provided by MODIS sensors on board of Terra and Aqua satellites are used in this project. The first four bands (visible) and band 31 & 32 (thermal) were selected. Future multi-spectral data from DubaiSat-1 (5-m resolution) will be also used to detect small changes in water color that cannot be detected with the MODIS data (250 m).Index Terms-environmental impact, remote sensing, desalination plants, MODIS, water quality.

We live in technological age development’s where many important data transmitted electronically from one device to another and in every place. Deep learning algorithms have facilitated the process of encoding and decoding digital images. Chaotic graph systems, on the other hand, are one of the most recent techniques utilized to encode image data based on the methods of cryptography. The chaos maps are divided into two main aspects, first one deals with the 1D map which requires fewer features and can be developed easily, the second one is the high dimensional map which is more complex than the 1D graph and it requires more features, more parameters, and it is relatively hard to develop. In this paper, we present a method for image encoding and decoding electronically using deep learning, the proposed algorithm was developed by using the hybrid technique of 3D chaos map generation, the best case of the proposed technique gave the following results: The average entropy calculation was (7.4838) before image encryption and (7.9896) after image encryption with average number of pixels change rate (NPCR) of (99.7085%) and the unified average changing intensity (UACI) of (33.2030%) which are the best outcomes when compared to other similar works.