Marine aquaculture has proliferated over the past decade, expanding into new, untapped open-water cultivation areas, such as lakes, rivers and deeper offshore environments, in response to increasing demand for seafood by consumers. However, to ensure sustainable development, it is necessary to minimize the impact of other ocean activities and the environment through science-based spatial planning. The choice of the primary site (physical carrying capacity) depends mainly on the aquaculture system, which varies around the world. However, the site is considered one of the factors (production, ecological and social) keys to any aquaculture operation, especially in the African continent. This choice affects both the success and sustainability of the products cultivated and the resolution of conflicts between different activities as well as the rational use of space. This study aims to identify suitable areas (primary site selection) for aquaculture in the Moroccan Atlantic continental shelf focused on the sub-area located between Cap Ghir 31.25° and Tarfaya 27.47°, based on the assessment of the dominant wave energy by implementing the hydrodynamical SWAN (Simulating Waves Nearshore) model dedicated for this kind of study. We derived the inputs for the SWAN model from WW3 (WAVEWATCH III model), which the AVISO data-products have extensively validated. The results show that, even if the Atlantic area is known for the agitation of its seas, there is the possibility of having adequate areas for aquaculture with an overall capacity that could extinguish the 389 ha in the study area if aquatic cultivation manages to exploit the offshore areas. At the level of the sub-zone belonging to the sous-Massa region (zone 1), the results show a strong coherence between the values of the surfaces estimated by the study and the actual values resulting from the development plan, with a value of 69 Ha for the first and 75 for the second, i.e., equal to 6 Ha, due to the geomorphology of the coast and natural coastal shelters, which play favorably on the environment for aquaculture development. These areas may attract the greed of investors, although they are in the process of being the subject of an aquaculture development plan.

This research proposes the acquisition of a time series from optical satellites to observe changes in the Venice lagoon, an ecosystem which is very challenging to monitor by means of in situ survey activities, let alone using remote sensing techniques, given the presence of land and sandbars (vegetated intertidal areas). The work describes the specific validation process performed by ISPRA on the results obtained as applied on some target sites of the Venice Lagoon, both natural and partially artificial islands, using fully artificial islands as reference.

Offshore aquaculture is increasingly viewed as a strategic solution to food security and coastal space constraints, but the identification of suitable sites remains a major challenge, especially at early planning stages. This study presents a rapid pre-screening framework for offshore aquaculture site selection by integrating numerical wave modeling with GIS-based spatial multi-criteria evaluation. The framework uses three primary criteria: wave exposure, water depth, and distance to the nearest harbor. Significant wave height was simulated with the SWAN model, forced by WAVEWATCH III boundary conditions, and combined with bathymetric and logistical layers to generate suitability maps for the central-eastern coast of Tunisia. Results show that 18.12% of the study area was classified as highly suitable according to wave conditions, while the final scenario-based suitability analysis identified 11% (491.13 km²) as appropriate for offshore aquaculture development. The proposed framework demonstrates that open-access datasets and widely used modeling tools can efficiently reduce the spatial search area and support early-stage marine spatial planning. It offers a transferable and cost-effective decision-support approach for identifying candidate zones for offshore aquaculture in data-limited coastal regions.