Influence of Dispersed Oil on the Remote Sensing Reflectance-Field Experiment in the Baltic Sea

OS-BREEZE: Oil Spills Boundary Red Emission Zone Estimation Using Unmanned Surface Vehicles

Maritime transport, responsible for delivering over eighty percent of the world's goods, is the backbone of the global delivery industry. However, it also presents considerable environmental risks, particularly regarding aquatic contamination. Nearly ninety percent of marine oil spills near shores are attributed to human activities, highlighting the urgent need for continuous and effective surveillance. To address this pressing issue, this paper introduces a novel technique named OS-BREEZE. This method employs an Unmanned Surface Vehicle (USV) for assessing the extent of oil pollution on the sea surface. The OS-BREEZE algorithm directs the USV along the spill edge, facilitating rapid and accurate assessment of the contaminated area. The key contribution of this paper is the development of this novel approach for monitoring and managing marine pollution, which significantly reduces the path length required for mapping and estimating the size of the contaminated area. Furthermore, this paper presents a scale model experiment executed at the Coastal and Marine Engineering Research Institute (CAMERI). This experiment demonstrated the method's enhanced speed and efficiency compared to traditional monitoring techniques. The experiment was methodically conducted across four distinct scenarios: the initial and advanced stages of an oil spill at the outer anchoring, as well as scenarios at the inner docking on both the stern and port sides.

Environmental Impacts and Challenges Associated with Oil Spills on Shorelines

Oil spills are of great concern because they impose a threat to the marine ecosystem, including shorelines. As oil spilled at sea is transported to the shoreline, and after its arrival, its behavior and physicochemical characteristics change because of natural weathering phenomena. Additionally, the fate of the oil depends on shoreline type, tidal energy, and environmental conditions. This paper critically overviews the vulnerability of shorelines to oil spill impact and the implication of seasonal variations with the natural attenuation of oil. A comprehensive review of various monitoring techniques, including GIS tools and remote sensing, is discussed for tracking, and mapping oil spills. A comparison of various remote sensors shows that laser fluorosensors can detect oil on various types of substrates, including snow and ice. Moreover, current methods to prevent oil from reaching the shoreline, including physical booms, sorbents, and dispersants, are examined. The advantages and limitations of various physical, chemical, and biological treatment methods and their application suitability for different shore types are discussed. The paper highlights some of the challenges faced while managing oil spills, including viewpoints on the lack of monitoring data, the need for integrated decision-making systems, and the development of rapid response strategies to optimize the protection of shorelines from oil spills.

Semi-Empirical Model of Remote-Sensing Reflectance for Chosen Areas of the Southern Baltic

Coastal waters are the richest parts of ocean ecosystems characterised by dynamic changes in water biology, physical and chemical features. Establishing local relationships between water constituents and optical properties in these areas will help to develop successful ocean colour algorithms allowing a thorough understanding of complex coastal waters and improving water quality monitoring. In this paper, the authors present the use of optical and biogeochemical measurements in complex aquatic environments and aim to create a semi-empirical model of remote-sensing reflectance (R_rs(λ_i)) for four wavelengths (λ_i = 420 nm, 488 nm, 555 nm, and 620 nm) based on multiparameter algorithms of absorption (a(λ_i)) and backscattering (b_b(λ_i)) coefficients. The bio-optical properties of water were determined based on empirical data gathered from aboard the r/v Oceania from April 2007 to March 2010 in chosen areas of the southern Baltic (Polish coast). The analyses reveal that R_rs(λ_i) in the studied area can be described with satisfactory accuracy using a five-parameter model. Positive results with a statistical error magnitude of R_rs(λ_i) of less than 50% were achieved for all four applied wavelengths. Bio-optical algorithms proposed by the authors enable evaluating biogeochemical characteristics of coastal areas in a broader context of ecosystem assessment and contribute significantly to the development of Earth and environmental sciences.

Monte Carlo Radiative Transfer Simulation to Analyze the Spectral Index for Remote Detection of Oil Dispersed in the Southern Baltic Sea Seawater Column: The Role of Water Surface State

The article presents the results of simulations that take into account the optical parameters of the selected sea region (from literature data on the southern Baltic Sea) and two optically extreme types of crude oil (from historical data) which exist in the form of a highly watered-down oil-in-water emulsion (10 ppm). The spectral index was analyzed based on the results of modeling the radiance reflectance distribution for almost an entire hemisphere of the sky (zenith angle from 0 to 80°). The spectral index was selected and is universal for all optically different types of oil (wavelengths of 650 and 412 nm). The possibility of detecting pollution in the conditions of the wavy sea surface (as a result of wind of up to 10 m/s) was studied. It was also shown that if the viewing direction is close to a direction perpendicular to the sea surface, observations aimed at determining the spectral index are less effective than observations under the zenith angle of incidence of sunlight for all azimuths excluding the direction of sunlight’s specular reflection.