Abstract
The impact of climate change on freshwater availability has been widely demonstrated to be severe. The capacity to timely and accurately detect, measure, monitor, and model volumetric changes in water reservoirs is therefore becoming more and more important for governments and citizens. In fact, monitoring over time the water volumes stored in reservoirs is mandatory to predict water availability for irrigation, civil and industrial uses, and hydroelectric power generation; this information is also useful to predict water depletion time with respect to various scenarios. Nowadays, water levels are usually monitored locally through traditional ground methods by a variety of administrations or companies managing the reservoirs, which are still not completely aware of the advantages of remote sensing applications. The continuous monitoring of water reservoirs, which can be performed by satellite data without the need for direct access to reservoir sites and with an overall cost that is independent of the actual extent of the reservoir, can be a valuable asset nowadays: water shortage and perduring periods of droughts interspersed with extreme weather events (as it has been experienced across all Europe in the latest years) make the correct management of water resources a critical issue in any European country (and especially in Southern Europe). The goal of this work is therefore to provide a methodology and to assess the feasibility of a service to routinely monitor and measure 3D (volumetric) changes in water reservoirs, exploiting Artificial Intelligence (AI) to improve the geometrical resolution of the available Sentinel-2 imagery (10 m).
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Funding
This project [11] has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 951745. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Germany, Italy, Slovenia, France, Spain.
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Ravanelli, R. et al. (2023). Earth Observation Big Data Exploitation for Water Reservoirs Continuous Monitoring: The Potential of Sentinel-2 Data and HPC. In: Ieracitano, C., Mammone, N., Di Clemente, M., Mahmud, M., Furfaro, R., Morabito, F.C. (eds) The Use of Artificial Intelligence for Space Applications. AII 2022. Studies in Computational Intelligence, vol 1088. Springer, Cham. https://doi.org/10.1007/978-3-031-25755-1_23
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DOI: https://doi.org/10.1007/978-3-031-25755-1_23
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