D2.1 Report on analysis of the requirements for MONOCLE sensors including projection of cost-savings and stakeholder feedback. Deliverable report of project H2020 MONOCLE (grant 776480)

Requirements for MONOCLE sensors were analysed at the start of sensor development, particularly with regard to projected cost-savings in monitoring and specific stakeholder feedback. The main inputs from stakeholders were obtained from the MONOCLE water quality monitoring survey (D9.1) and are used here to define sensor-specific development priorities, particularly with respect to purpose, performance, cost and interoperability. This document guides both the initial development of new sensors and evolution of existing prototypes to higher technological readiness levels

Airborne Drones for Water Quality Mapping in Inland, Transitional and Coastal Waters-MapEO Water Data Processing and Validation

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

Airborne Drones for Water Quality Mapping in Inland, Transitional and Coastal Waters-MapEO Water Data Processing and Validation

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

Assessing Storm Response of Multiple Intertidal Bars Using an Open-Source Automatic Processing Toolbox

Intertidal bars are common features of sandy beaches in meso- and macro-tidal environments, yet their behaviour under storm impact and subsequent recovery remain poorly documented. Intensive surveys provide valuable information; however, it takes time to process the vast amount of data. This study presents the morphological response of a multibarred macro-tidal beach along the Belgian coast after a severe storm that happened on 8–12 February 2020, and to develop and apply an oPen-source Raster prOcessing Toolbox for invEstigation Coast intertidal bar displacemenT (PROTECT) in Python for automated bar extraction. This toolbox was applied to the digital surface models of pre- and post-storm airborne LiDAR surveys of a multibarred intertidal beach. The PROTECT toolbox is capable of detecting the position and elevation of intertidal bars accurately. The uncertainty in the elevation characteristics of the bars induces an error in the elevation dimension of 0.10 m. Using the toolbox, the results showed that the intertidal bars changed in term of variations in bar number, dimensions and shape across the storm event. Overall, the storm significantly eroded the dune and the upper-beach zone with a sand loss equivalent elevation decrease of 0.14 m. This was followed by a continuous and full recovery after 9 months under fair weather conditions. In contrast, the sand budget in the intertidal zone did not change over the entire monitoring period although the bars showed significant morphological change. Applying the PROTECT toolbox on high-resolution 3D topographic datasets allows to increase the temporal mapping resolution of intertidal bars from long-term (years) to short (storm events) time scales. Similar assessments at locations worldwide would allow the improvement of our knowledge on the morphodynamical role of multibarred beaches and to forecast their evolution, thus contributing to manage future storm response and the progressively accelerating sea level rise

Characterization of Intertidal Bar Morphodynamics Using a Bi-Annual LiDAR Dataset

Intertidal bars are common features on meso-and macro-tidal sandy beaches with low to moderate wave energy environments. Understanding their morphodynamics is, hence, crucial for enhancing our knowledge on beach processes which is beneficial for coastal management. However, most studies have been limited by assessing bar systems two-dimensionally and typically over the short-term. Morphology and dynamics of an intertidal bar system in a macro-tidal environment have been investigated using bi-annual LiDAR topographic surveys over a period of seven years and along 3.2 km at Groenendijk beach (Belgium). The detected bars demonstrate that a morphology of an intertidal bar is permanently on the beach. However, these individual features are dynamic and highly mobile over the course of half a year. The mean height and width of the bars were 1.1 and 82 m, respectively. The highest, steepest, and asymmetric features were found on the upper beach, while they were least developed in the lower intertidal zone. The bars were evenly distributed over the entire intertidal beach, but the largest concentration observed around the mean sea level indicated the occurrence at preferential locations. The most significant net change across the beach occurs between the mean sea level and mean-high-water neap which corroborates with the profile mobility pattern. The seasonal variability of the bar morphology is moderately related to the seasonally driven changes in storm and wave regime forcings. However, a distinct relationship may be inhibited by the complex combination of forcing-, relaxation time- and feedback-dominated response. This work conducted from bi-annual LiDAR surveys has provided an unprecedented insight into the complex spatial organization of intertidal bars as well as their variability in time from seasonal to annual scale

Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images

The occurrence of litter in natural areas is nowadays one of the major environmental challenges. The uncontrolled dumping of solid waste in nature not only threatens wildlife on land and in water, but also constitutes a serious threat to human health. The detection and monitoring of areas affected by litter pollution is thus of utmost importance, as it allows for the cleaning of these areas and guides public authorities in defining mitigation measures. Among the methods used to spot littered areas, aerial surveillance stands out as a valuable alternative as it allows for the detection of relatively small such regions while covering a relatively large area in a short timeframe. In this study, remotely piloted aircraft systems equipped with multispectral cameras are deployed over littered areas with the ultimate goal of obtaining classification maps based on spectral characteristics. Our approach employs classification algorithms based on random forest approaches in order to distinguish between four classes of natural land cover types and five litter classes. The obtained results show that the detection of various litter types is feasible in the proposed scenario and the employed machine learning algorithms achieve accuracies superior to 85% for all classes in test data. The study further explores sources of errors, the effect of spatial resolution on the retrieved maps and the applicability of the designed algorithm to floating litter detection