Introduction of image-based water transparency descriptors to quantify marine snow and turbidity features. A study with data from a stationary observatory

Möller T, Nilssen I, Nattkemper TW. Introduction of image-based water transparency descriptors to quantify marine snow and turbidity features. A study with data from a stationary observatory. Presented at the MIW 2014 - Marine Imaging Workshop, Southampton

Computational analysis of spatial species distribution for integrated stationary environmental monitoring

Osterloff J, Nilssen I, Möller T, Nattkemper TW. Computational analysis of spatial species distribution for integrated stationary environmental monitoring. Presented at the Geohab 2015, Salvador, Bahia, Brazil

Extracting Scalar Quantities from Underwater Images - a Toolbox for Image Data from Fixed Observatories.

Osterloff J, Möller T, Nilssen I, Nattkemper TW. Extracting Scalar Quantities from Underwater Images - a Toolbox for Image Data from Fixed Observatories. Presented at the Marine Imaging Workshop 2017, Kiel

Change Detection in underwater time laps videos from stationary observatories

Möller T, Nilssen I, Sumida P, Osterloff J, Nattkemper TW. Change Detection in underwater time laps videos from stationary observatories. Presented at the GEOHAB, Salvador, Brazil

Automated Image based Biomass Quantification in Mesocosm Studies

Osterloff J, Nilssen I, de Oliveira Figueiredo MA, de Souza Tâmega FT, Möller T, Nattkemper TW. Automated Image based Biomass Quantification in Mesocosm Studies. Presented at the Geohab 2014, Lorne, Victoria, Australia

Online preconcentration ICP-MS analysis of rare earth elements in seawater

The rare earth elements (REEs) with their systematically varying properties are powerful tracers of continental inputs, particle scavenging intensity and the oxidation state of seawater. However, their generally low (∼pmol/kg) concentrations in seawater and fractionation potential during chemical treatment makes them difficult to measure. Here we report a technique using an automated preconcentration system, which efficiently separates seawater matrix elements and elutes the preconcentrated sample directly into the spray chamber of an ICP-MS instrument. The commercially available “seaFAST” system (Elemental Scientific Inc.) makes use of a resin with ethylenediaminetriacetic acid and iminodiacetic acid functional groups to preconcentrate REEs and other metals while anions and alkali and alkaline earth cations are washed out. Repeated measurements of seawater from 2000 m water depth in the Southern Ocean allows the external precision (2σ) of the technique to be estimated at <23% for all REEs and <15% for most. Comparison of Nd concentrations with isotope dilution measurements for 69 samples demonstrates that the two techniques generally agree within 15%. Accuracy was found to be good for all REEs by using a five point standard addition analysis of one sample and comparing measurements of mine water reference materials diluted with a NaCl matrix with recommended values in the literature. This makes the online preconcentration ICP-MS technique advantageous for the minimal sample preparation required and the relatively small sample volume consumed (7 mL) thus enabling large data sets for the REEs in seawater to be rapidly acquired

Temporal variations of perfluoroalkyl substances and polybrominated diphenyl ethers in alpine snow

The occurrence and temporal variation of 18 perfluoroalkyl substances (PFASs) and 8 polybrominated diphenyl ethers (PBDEs) in the European Alps was investigated in a 10 m shallow firn core from Colle Gnifetti in the Monte RosaMassif (4455mabove sea level). The firn core encompasses the years 1997e2007. Firn core sections were analyzed by liquid chromatographyetandem mass spectrometry (PFASs) and gas chromatographyemass spectrometry (PBDEs). We detected 12 PFASs and 8 PBDEs in the firn samples. Perfluorobutanoic acid (PFBA; 0.3e1.8 ng L1) and perfluorooctanoic acid (PFOA; 0.2e0.6 ng L1) were the major PFASs while BDE 99 (<MQLe4.5 ng L1) and BDE 47 (n.d.e2.6 ng L1) were the major PBDEs. This study demonstrates the occurrence of PFASs and PBDEs in the European Alps and provides the first evidence that PFASs compositions may be changing to PFBA-dominated composition

ALMI - A Generic Active Learning System for Computational Object Classification in Marine Observation Images

Möller T, Nattkemper TW. ALMI - A Generic Active Learning System for Computational Object Classification in Marine Observation Images. Sensors. 2021;21(4): 1134.In recent years, an increasing number of cabled Fixed Underwater Observatories (FUOs) have been deployed, many of them equipped with digital cameras recording high-resolution digital image time series for a given period. The manual extraction of quantitative information from this data regarding resident species is necessary to link the image time series information to data from other sensors but requires computational support to overcome the bottleneck problem in manual analysis. Since a priori knowledge about the objects of interest in the images is almost never available, computational methods are required that are not dependent on the posterior availability of a large training data set of annotated images. In this paper, we propose a new strategy for collecting and using training data for machine learning-based observatory image interpretation much more efficiently. The method combines the training efficiency of a special active learning procedure with the advantages of deep learning feature representations. The method is tested on two highly disparate data sets. In our experiments, we can show that the proposed method ALMI achieves on one data set a classification accuracy A>90% with less than N=258 data samples and A>80% after N=150 iterations, i.e. training samples, on the other data set outperforming the reference method regarding accuracy and training data required

Change Detection in Marine Observatory Image Streams using Bi-Domain Feature Clustering

Möller T, Nilssen I, Nattkemper TW. Change Detection in Marine Observatory Image Streams using Bi-Domain Feature Clustering. In: 2016 23rd International Conference on Pattern Recognition (ICPR). Piscataway, NJ: IEEE; 2016: 793-798