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Rapporten er en kortfattet gennemgang af udpegningsproccessen for ålegræsgenopretning. Anbefalingerne er erfaringsbaserede med baggrund i udplantninger i danske fjorde og kystområder. Formålet har været at sikre højest mulige succes via en blanding af metodiske processer fra model/GIS-analyser af kyststrækninger, som yderligere kvalificeres ved drone-analyser og dykker-observationer inden der gennemføres test-transplantationer som endeligt udpegningsgrundlag., Rapporten er en kortfattet gennemgang af udpegningsproccessen for ålegræsgenopretning. Anbefalingerne er erfaringsbaserede med baggrund i udplantninger i danske fjorde og kystområder. Formålet har været at sikre højest mulige succes via en blanding af metodiske processer fra model/GIS-analyser af kyststrækninger, som yderligere kvalificeres ved drone-analyser og dykker-observationer inden der gennemføres test-transplantationer som endeligt udpegningsgrundlag.

Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. Imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on timeconsuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%; however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings.

Traditional monitoring (e.g., in-water based surveys) of eelgrass meadows and perennial macroalgae in coastal areas is time and labor intensive, requires extensive equipment, and the collected data has a low temporal resolution. Further, divers and Remotely Operated Vehicles (ROVs) have a low spatial extent that cover small fractions of full systems. The inherent heterogeneity of eelgrass meadows and macroalgae assemblages in these coastal systems makes interpolation and extrapolation of observations complicated and, as such, methods to collect data on larger spatial scales whilst retaining high spatial resolution is required to guide management. Recently, the utilization of Unoccupied Aerial Vehicles (UAVs) has gained popularity in ecological sciences due to their ability to rapidly collect large amounts of area-based and georeferenced data, making it possible to monitor the spatial extent and status of SAV communities with limited equipment requirements compared to ROVs or diver surveys. This paper is focused on the increased value provided by UAV-based, data collection (visual/Red Green Blue imagery) and Object Based Image Analysis for gaining an improved understanding of eelgrass recovery. It is demonstrated that delineation and classification of two species of SAV (Fucus vesiculosus and Zostera marina) is possible; with an error matrix indicating 86–92% accuracy. Classified maps also highlighted the increasing biomass and areal coverage of F. vesiculosus as a potential stressor to eelgrass meadows. Further, authors derive a statistically significant conversion of percentage cover to biomass (R2 = 0.96 for Fucus vesiculosus, R2 = 0.89 for Zostera marina total biomass, and R2 = 0.94 for AGB alone, p < 0.001). Results here provide an example of mapping cover and biomass of SAV and provide a tool to undertake spatio-temporal analyses to enhance the understanding of eelgrass ecosystem dynamics

Denne rapport beskriver status for viden om ni potentielle marine virkemidler og tiltag, der kan anvendes til at forbedre miljø- og naturforholdene i Lillebælt. De ni potentielle virkemidler inkluderer: Etablering af ålegræs, etablering af stenrev, etablering af biogene rev, anvendelse af biohuts, dyrkning af muslinger og tang i vandsøjlen, opfiskning af krabber og søstjerner, områder beskyttet mod trawlfiskeri (MPAs) og anvendelse af naturbaserede løsninger til klima-sikring af kysterne. Virkemiddelbeskrivelserne har fokus på de forventede økosystemtjenester relateret til biodiversitet, natur og miljø, men andre økosystemtjenester associeret med virkemidlerne beskrives også. Anvendeligheden og effekterne af de forskellige virkemidler og tiltag til miljø- og naturgenopretning afhænger i høj grad af lokale fysiske og miljømæssige forhold, og derfor vil der være stor forskel på, hvor virkemidlerne kan placeres. Såfremt der findes viden om kriterierne for anvendelse, så indgår det i virkemiddelbeskrivelsen. Vidensgrundlaget for de beskrevne virkemidler er meget varierende og status for eksisterende vidensgrundlag er, ligesom områder hvor der mangler viden, inkluderet i beskrivelserne.