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In this paper, the feasibility of satellite remote sensing in detecting and predicting locations of buried objects in the archaeological site of Saruq Al-Hadid, United Arab Emirates (UAE) was investigated. Satellite-borne synthetic aperture radar (SAR) is proposed as the main technology for this initial investigation. In fact, SAR is the only satellite-based technology able to detect buried artefacts from space, and it is expected that fine-resolution images of ALOS/PALSAR-2 (L-band SAR) would be able to detect large features (>1 m) that might be buried in the subsurface (

Satellite remote sensing-based monitoring of coastal habitats, like those in the Arabian Gulf, presents a special challenge due to the attenuation of light through the turbid atmosphere and water, as well as the spectral similarity of many benthic habitats. The present study aims to evaluate the potential of DubaiSat-2 imagery in mapping corals and benthic habitat in the vicinity of Dalma Island, United Arab Emirates (UAE). To do so, this study proposes a spectral-spatial method that uses a combination of different remote sensing techniques in order to discern benthic communities with reference to their spatial distribution. This method involves multi-level segmentation, non-linear feature analysis and ensemble learning methods for the characterization of corals, benthic classes and underlying seabeds. Maximum Likelihood and Random Forest, pixel-based methods, were also used for comparison of classification performances. Comparative data are derived from published marine habitat maps. The spectral...

Like coral reefs around the world, the reefs of the United Arab Emirates (UAE) are facing global climate change and associated threats. The coasts and islands that flank Abu Dhabi host an important number of corals that should be the focus of conservation actions. Well-designed conservation and management plans require efficient monitoring systems that include understanding coral reef patterns. To understand some of these patterns; coral cover data, satellite-derived and in-situ water quality parameters from nine key reef environments in the UAE from 2011 to 2014 to model coral patterns were used. The objectives were to model coral patterns and realistically predict coral damage intensity with changing environmental variables. Coral damage cover models were defined and estimated for the coral damage cover. Effects of environmental factors were estimated, and predictions of coral damage intensity were presented with changing factors. Main findings, based on the studied data, showed that nutrient enrichment, a proxy for anthropogenic pressure, and salinity are the most influential factors to induce coral damage in UAE waters. Furthermore, results demonstrated that the probability of severe damage increases with decreasing water oxygenation and with increasing temperature, light, salinity, acidity and nutrient levels. The defined and estimated predictions accounted for corals’ behavioural aspects, across individual reefs and over time. This approach is more appropriate than estimation predictions that just account for historic trends. Nevertheless, there are, probably, many components within the model framework that can be expanded and/or improved as more information become available. An extended dataset will enable a means to independently validate the defined models and test other modelling approaches. Continually increasing the in-situ and remote sensing data sizes, spatially and temporally, defines a long-term priority.

Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.