Remote sensing plays a crucial role in the detection and monitoring of oil spills at sea, aiding in emergency response planning and monitoring routines. Despite significant progress in this field, estimating oil thickness remains challenging, impacting response efficacy and liability determination. Traditional methods like the Bonn Agreement Oil Appearance Code (BAOAC) are commonly used operationally. However, interpretation based only on visual identification suffers from subjectivity and limitations in the code designation for oil films. Hyperspectral sensing offers a promising solution for this matter. Previous works have demonstrated a relationship between oil spectral features and oil thickness. However, most of these studies rely on mathematical models validated with aerial or orbital data from old spills, hindering real-time applicability. This study proposes a novel approach based on unmanned aerial vehicles (UAVs) equipped with a hyperspectral camera for real-time data acquisition during offshore spills. The goal was to improve the BAOAC approach by integrating VNIR-SWIR spectral data with visual interpretation to classify oil spills. The methodology evaluates surface area, oil thickness, and weathering stage estimation through a realistic 11-week oil spill experiment. Results demonstrate that the high resolution of data acquired with UAVs can facilitate close-range monitoring of oil spills, especially near oil rigs and shorelines, providing more reliable results when combined with the BAOAC. The methodology can be upscaled by mounting the hyperspectral camera on aircraft or helicopters, providing promising applications for oil spill monitoring and offering practical insights for the short- and long-term management efforts carried out in the petroleum industry.

Microplastics (MPs) are polymeric particles, mainly fossil-based, widely found in marine ecosystems, linked to environmental and public health impacts due to their persistence and ability to carry pollutants. In São Paulo’s northern coast, geomorphological factors and anthropogenic activities intensify the deposition of these pollutants. Through multivariate techniques, this study aims to investigate the role of the morphometrical parameters as independent variables in quantifying the distribution of MPs on the region’s sandy beaches. Using beach face slope (tanβ) and orientation (Aspect) derived from remote sensing images, calibrated by in situ topographic profiles collected through GNSS positioning, and laboratory analyses, six machine learning models Random Forest, Gradient Boosting, Lasso and Ridge regression, Support Vector Regression, and Partial Least Squares regression were tested and evaluated for performance. The Gradient Boosting model demonstrated the best performance, indicating its superior capacity to capture complex relationships between predictor variables and MPs deposition, followed by Random Forest model. Morphometric analysis revealed, once again, that in this coastal section of São Paulo, beaches with Sloping profiles oriented toward the SSW are more susceptible to MPs accumulation, especially near urban centers. Ultimately, incorporating geomorphological variables into predictive models enhances understanding of MPs deposition, providing a foundation for environmental policies focused on marine pollution mitigation and coastal ecosystem conservation while also contributing to achieve SDG 14.