Das BfG-GNSS-Messnetzes besteht aus über 50 GNSS-Stationen im Bereich der Nord- und Ostsee. Primärer Zweck ist die Georeferenzierung von Pegeln der Wasserstraßen- und Schifffahrtsverwaltung (WSV). Die Rohdaten umfassen die kontinuierlichen Beobachtungsdaten der Satellitensysteme GPS, Glonass, Galileo und Beidou. Der Höhenunterschied 'dH1' zwischen dem jeweiligen Referenzpunkt der GNSS-Antenne und den zugehörigen Pegelfestpunkten (PFP) kann dem Sitelog der Permanentstation entnommen werden. Der Sollhöhenunterschied 'dH2' zwischen den Pegelfestpunkten und dem Pegelnullpunkt (PNP) wird durch das zuständige Wasserstraßen- und Schifffahrtsamt geführt.
Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025
Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025
Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025
Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025
Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025
Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m². For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used. Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025
Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025
Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025
Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025
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