Use of Sentinel-3 OLCI Images and Machine Learning to Assess the Ecological Quality of Italian Coastal Waters.

Understanding and monitoring the ecological quality of coastal waters is crucial for preserving marine ecosystems. Eutrophication is one of the major problems affecting the ecological state of coastal marine waters. For this reason, the control of the trophic conditions of aquatic ecosystems is needed for the evaluation of their ecological quality. This study leverages space-based Sentinel-3 Ocean and Land Color Instrument imagery (OLCI) to assess the ecological quality of Mediterranean coastal waters using the Trophic Index (TRIX) key indicator. In particular, we explore the feasibility of coupling remote sensing and machine learning techniques to estimate the TRIX levels in the Ligurian, Tyrrhenian, and Ionian coastal regions of Italy. Our research reveals distinct geographical patterns in TRIX values across the study area, with some regions exhibiting eutrophic conditions near estuaries and others showing oligotrophic characteristics. We employ the Random Forest Regression algorithm, optimizing calibration parameters to predict TRIX levels. Feature importance analysis highlights the significance of latitude, longitude, and specific spectral bands in TRIX prediction. A final statistical assessment validates our model's performance, demonstrating a moderate level of error (MAE of 0.51) and explanatory power (R2 of 0.37). These results highlight the potential of Sentinel-3 OLCI imagery in assessing ecological quality, contributing to our understanding of coastal water ecology. They also underscore the importance of merging remote sensing and machine learning in environmental monitoring and management. Future research should refine methodologies and expand datasets to enhance TRIX monitoring capabilities from space.

Assessing the risk of oil spill impacts and potential biodiversity loss for coastal marine environment at the turn of the COVID-19 pandemic event.

The Tuscan Archipelago, with its great environmental and economic importance, is one of the highest oil spill density areas in the Western Mediterranean. In this study, an interdisciplinary approach, based on numerical applications and experimental methods, was implemented to quantify the risk of oil spill impact along the rocky shores of this archipelago in relation to the maritime activities. The risk, defined as a combination of the hazard and the damage, was quantified for the biennial 2019-2020 in order to account for the effects generated by the COVID-19 pandemic restrictions on the local maritime traffic. A high-resolution oceanographic and particle tracking model was applied to simulate the trajectories of possible oil spill events and to quantify the hazard of impacts on the coast of numerical particles, daily seeded in correspondence of those marine sectors that are characterised by relevant traffic of vessels. The damage, expressed as the product of exposure and vulnerability, was estimated following an extensive sampling approach aimed at quantifying the ecological status of the rocky shores in four selected islands of the Tuscan Archipelago. Results revealed and quantified the direct relationship between the temporary reduction of the maritime traffic due to the pandemic restrictions, and the probability of suffering damage from oil spill impact along the archipelago's rocky shores, which was highly context-dependent.