Els Knaeps

Dredging activities regularly occurring in near-shore and coastal waters generate turbid waters within the surface layer with high concentrations of suspended particulate matter collected in bottom sediments. The potential impact of these dredge plumes on natural ecosystems must be monitored using cost-effective methods and observations. Here, we investigate the biogeochemical and optical properties of dredge plumes selected mainly in European and African coastal waters. Laboratory analyses realized on numerous water samples collected in dredge plumes reveal (extremely) high water turbidity and high concentrations of inorganic particles in suspension, sometimes mixed with high concentrations of phytoplankton particles. The most peculiar optical property of these particles is a spectral light absorption coefficient significantly flatter than that of suspended particles in natural turbid waters (e.g., river plumes or estuarine maximum turbidity zones). This peculiar optical property is also detected on ocean color satellite data corrected for atmospheric effects, with a water reflectance signal higher than natural turbid waters at short visible wavebands (400–550 nm). Such an atypical spectral signature, which can be detected and mapped from space, makes the operational monitoring of dredge plumes in coastal waters using high-spatial-resolution (e.g., Sentinel2-MSI) satellite data possible.

Dredging activities regularly occurring in near-shore and coastal waters generate turbid waters within the surface layer with high concentrations of suspended particulate matter collected in bottom sediments. The potential impact of these dredge plumes on natural ecosystems must be monitored using cost-effective methods and observations. Here, we investigate the biogeochemical and optical properties of dredge plumes selected mainly in European and African coastal waters. Laboratory analyses realized on numerous water samples collected in dredge plumes reveal (extremely) high water turbidity and high concentrations of mineral-rich particles in suspension, sometimes mixed to high concentrations of phytoplankton particles. The most peculiar optical property of these particles is a spectral light absorption coefficient significantly flatter than that of suspended particles in natural turbid waters (e.g., river plumes or estuarine maximum turbidity zones). This peculiar optical property is also detected on ocean color satellite data corrected for atmospheric effects, with a water reflectance signal higher than natural turbid waters at short visible wavebands (400-550 nm). Such an atypical spectral signature, that can be detected and mapped from space, makes the operational monitoring of dredge plumes in coastal waters using high spatial resolution (e.g., Sentinel2-MSI) satellite data possible.

Different sources and pathways contribute to the plastic waste inputs into rivers, such as inadequate waste management in the river basin, insufficient water treatment of urban wastewater and rural/industrial discharges, and stormwater runoff. The plastic polluting rivers can then be transported downstream and reach the ocean, expanding the impacts of the land-based sources of plastic pollution from riverine to marine environments. Thus, the detection and collection of plastics and other waste in the rivers is instrumental in mitigating the pressing marine litter issue. In parallel, preventing the current inputs of plastic waste into the river allows for a reduction of the pollution at the source and of its impact on the riverine environment. The Innovative Solutions for Plastic Free European Rivers (INSPIRE) project’s primary objective is to contribute to the reduction of plastic litter by implementing a holistic strategy that focuses on detecting, collecting, and preventing waste in river systems. For that, we are currently testing and evaluating 20 technologies and actions that enable us to: i) detect litter in rivers through sampling with different devices or through observations with monitoring apps, drones and bridge-mounted camaras; ii) collect litter from rivers (water surface/column, riverbed and riverbanks) using different cleanup technologies and manual cleanups organized with the engagement of local citizens; iii) prevent litter by removing it from the waste streams with different plastic removal technologies installed in urban infrastructure (e.g., in wastewater treatment plants); iv) develop sustainable alternatives for existing non-degradable polluting products (e.g., plastic packaging and agricultural films). The 20 technologies and actions are being implemented in 6 demo river sites across Europe and in other prevention sites to test agricultural applications and to reduce single-use plastic waste associated with events (e.g., music festivals). The data collected is being used to create a database of plastic pollution levels in the European rivers, and to feed riverine models that will be used to estimate the transported and accumulated plastic in the rivers and to support the selection of the optimal solutions through a decision-support tool. The sustainability of the solutions is evaluated with a techno-economic analysis, which will enable the creation of action plans towards upscaling and replication. Together, the cost-benefit sustainability analysis and tools created culminate in a master plan for achieving the project primary goal and to protect and restore the health of our ocean and waters through research, innovation, blue investments, and increased awareness with the involvement of local citizens and stakeholders.

Dredging operations are essential to protect coastlines, safe navigation and ensuring access to harbours. With threats of sea level rise, flooding and more extreme storms, the need for dredging operations will only increase and their sustainable implementation will be key.One of the key aspects to control during dredging, is the release of sediments in the water and increase of turbidity during dredging, which can degrade water quality and harm sensitive ecosystems. Imposing turbidity limits where dredging contractors have to comply with, is common in most parts of the world. However, there are various sources of turbidity near the dredging area: from resuspension, to dredged bottom sediments and outflows from rivers. Hence, a turbidity increase is not necessarily linked to a (nearby) dredging activity. It is therefore key to understand the extent of the dredge plume and natural background turbidity to avoid unacceptable ecological impacts, adhere to the directives in place but also to avoid unnecessary shutdowns.We have developed a method for a dedicated dredge plume monitoring based on Earth Observation data. This method allows to assess the turbidity of the dredge plume but also its extent and impact on the nearby ecosystem. First, a satellite database of dredge plumes was compiled, including several large dredging sites around the world. The database is complemented with an automated Artificial intelligence based plume delineation. This plume delineation automatically recognizes the plumes in the images and derived the near field dredging plume. In a second step, optical properties of the dredge plumes are evaluated. This is done by developing a dredge plume spectral library from the database and by collecting field samples during dredging activities. These field samples are analysed and interpreted to understand their optical properties (light attenuation, absorption, scattering and backscattering, turbidity and fluorescence per units of suspended particulate matter and chlorophyll-a concentrations). Finally we will present the results of our new dredge plume algorithm, adapted for the specific conditions of a dredging site.

Earth Observation (EO) technologies can provide large scale systematic monitoring capabilities of all floating marine matter composed mostly of plastic based items. The main monitoring capabilities that have been demonstrated using EO include detection, identification, quantification and tracking of floating marine matter. The detection of floating matter from EO technologies is achieved by understanding and leveraging unique signal characteristics which can be observed remotely, and for which litter is different from the background or from other optically active materials. A broad range of different EO sensor technologies have been investigated with respect to floating matter including LiDAR, MiDAR, RADAR and multi-angle polarimetry. The vast majority of the work on detection of floating matter and marine plastics however is being performed using optical imaging, with passive sensors. The distinctive feature here is the reflectance of floating matter and plastics. Most types of floating matter have much higher reflectance than the reflectance of water, especially in the NIR and SWIR spectral range. This offers a strong basis for straightforward detection of floating matter vs. water, although it does not necessarily indicate that different types (i.e., natural or anthropogenic) of floating matter can be distinguished. Here we will present a critical review of the capabilities and limitations of optical satellites to monitor floating matter and marine plastics. We further asses the requirements for a new satellite mission for the monitoring of marine plastic litter.

Coastal and inland aquatic ecosystems are of fundamental interest to society and economy, given their tight link to urbanization and economic value creation. These ecosystems, which are continuously impacted by natural processes and human activities such as litter pollution, including marine plastics, play a significant role in the carbon cycle, and they comprise critical habitats for biodiversity. Particularly, improved knowledge of distributions of marine plastics is becoming critical. Systematic, high-quality and global observations, such as those provided by satellite remote sensing techniques, are key to understand complex aquatic systems. While multitudes of remote sensing missions have been specifically designed for studying ocean biology and biogeochemistry as well as for evaluating terrestrial environments, missions dedicated to studying critical coastal and inland aquatic ecosystems at global scale are non-existent. Thus, these ecosystems remain among the most understudied habitats on the Earth’s surface. A satellite mission called Global Assessment of Limnological, Estuarine and Neritic Ecosystems (GALENE), is proposed to ESA’s Earth Explorer Mission Idea call to respond to current and future challenges linked to coastal and inland ecosystems. The mission concept consists of a synergy of three innovative instruments, namely a hyperspectral sensor, a panchromatic camera and a polarimeter. GALENE will then provide optimized measurements of these aquatic ecosystems by enabling an adaptive spectral, spatial, multidirectional and polarimetric sampling of properties and processes in water column, benthic habitats and associated wetlands. GALENE will substantially contribute to solving global water challenges, including the marine plastic detection, water pollution and ensuring clean drinking water supply for all and protecting coastal areas and populations. In particular, co-located hyperspectral and multi-angular polarized measurements will consolidate the capacity for providing descriptors of plastics. The GALENE science objectives and the main innovative features will be presented.

Oil spills in ports are particular cases of oil pollution in water environments that call for specific monitoring measures. Apart from the ecological threats that they pose, their proximity to human activities and the financial losses induced by disturbed port activities add to the need for immediate action. However, in ports, established methods based on short-wave infrared sensors might not be applicable due to the relatively low thickness of the oil layer, and satellite images suffer from insufficient spatial resolution, given the agglomeration of objects in ports. In this study, a lightweight ultraviolet (UV) camera was exploited in both controlled experiments and a real port environment to estimate the potential and limitations of UV imagery in detecting oil spills, in comparison to RGB images. Specifically, motivated by the scarce research literature on this topic, we set up experiments simulating oil spills with various oil types, different viewing angles, and under different weather conditions, such that the separability between oil and background (water) could be better understood and objectively assessed. The UV camera was also used to detect real-world oil spills in a port environment after installing it on a vessel for continuous monitoring. Various separability metrics between water and oil, computed in both scenarios (controlled experiments and port environment), show that the UV cameras have better potential than RGB in detecting oil spills in port environments.

In June 2023 the Horizon Europe project INSPIRE kicked off. INSPIRE will fight against the plastic pollution in rivers by introducing 20 scalable technologies to prevent and eliminate litter. The technologies will be demonstrated in 6 rivers across Europe. Monitoring of the plastic load in the river and the riverbanks is essential to develop a baseline and evaluate effectiveness of the technologies. Here we will introduce a camera and drone-based system to monitor plastic flux in the river and macroplastic densities on the riverbanks. The fixed camera system consists of a series of Commercial Of-The-Shelf (COTS) surveillance cameras with housing and real-time datalink. The cameras work autonomous and will provide a continuous feed of data uploaded to the cloud. The drone system consists of a high resolution RGB and multispectral Micasense camera. Specific attention goes to the conversion from the raw drone data into standardized Analysis Ready Data (ARD) including: (1) image alignment of the multispectral camera. (2) Converting raw drone data into reflectance products (using an irradiance sensor) allows the methodology to be applicable in any circumstance (clear, overcast, cloudy conditions) and transferable to other regions. (3) Sensor fusion, to align high spatial resolution RGB with high spectral resolution MicaSense data. For plastic detection and characterization robust machine learning models are being used including new pre-trained foundation models like Segment Anything. New methods are being tested to transform the camera-based plastic detections into a plastic flux product taking into account the tide effects in the river. This includes feature detection techniques like SIFT (Scale_Invariant Feature Transform), SURF (Speeded-Up Robust Features) or ORB (Oriented FAST and Rotated Binary Robust Independent Elementary Features) in combination with a feature matching algorithm (e.g. FLANN based matcher). Here, we will present the INSPIRE project and its first results demonstrated at the Temse bridge (Belgium) and riverbanks along the Scheldt river (Belgium).

In this work, we studied the potential of the visible, near-infrared, and shortwave infrared wavelength regions for monitoring oil spill incidents using optical reflectance. First, a simple physical model was designed for accurate oil thickness and volume estimation using optical reflectance. The developed method was made invariant to changes in acquisition and illumination conditions. In the next step, an algorithm based on an artificial neural network was designed to detect spilled oil. The training samples that are required to optimize the parameters of the network were generated by utilizing the proposed physical model. To validate the method, experiments were conducted in laboratory and outdoor scenarios for detection and thickness/volume estimation on four different oil types. In particular, we developed hyperspectral datasets of oil samples with varying thickness between 500 µm and 5000 µm acquired using two different sensors, an Agrispec spectrometer and an Imec snapscan shortwave infrared hyperspectral camera, in strictly controlled experimental settings. To demonstrate the potential of the proposed method in outdoor environments using solely the visible wavelength region, we monitored the evolution of artificially spilled oil in an outdoor scene with an RGB camera mounted on a drone.

Using airborne drones to monitor water quality in inland, transitional or coastal surface waters is an emerging research field. Airborne drones can fly under clouds at preferred times, capturing data at cm resolution, filling a significant gap between existing in situ, airborne and satellite remote sensing capabilities. Suitable drones and lightweight cameras are readily available on the market, whereas deriving water quality products from the captured image is not straightforward; vignetting effects, georeferencing, the dynamic nature and high light absorption efficiency of water, sun glint and sky glint effects require careful data processing. This paper presents the data processing workflow behind MapEO water, an end-to-end cloud-based solution that deals with the complexities of observing water surfaces and retrieves water-leaving reflectance and water quality products like turbidity and chlorophyll-a (Chl-a) concentration. MapEO water supports common camera types and performs a geometric and radiometric correction and subsequent conversion to reflectance and water quality products. This study shows validation results of water-leaving reflectance, turbidity and Chl-a maps derived using DJI Phantom 4 pro and MicaSense cameras for several lakes across Europe. Coefficients of determination values of 0.71 and 0.93 are obtained for turbidity and Chl-a, respectively. We conclude that airborne drone data has major potential to be embedded in operational monitoring programmes and can form useful links between satellite and in situ observations.