API src

Found 455 results.

Tree Species - Sentinel-2 - Germany, 2016

The Tree Species Germany product provides a map of dominant tree species across Germany for the year 2016 at a spatial resolution of 10 meters. The map depicts the distribution of ten tree species groups derived from multi-temporal optical Sentinel-2 data. The input features explicitly incorporate phenological information to capture seasonal vegetation dynamics relevant for species discrimination. A total of over 100,000 training and test samples were compiled from publicly accessible sources, including urban tree inventories, Google Earth Pro, Google Street View, and field observations. The final product was created by majority-voting on annual XGBoost Sentinel-2 tree species classifications (2016–2024) and filtering with forest structure data. If no clear majority vote was achieved, the class uncertain was assigned. The Tree Species Germany 2016 product achieves an overall F1-score of 0.95. For the dominant species pine, spruce, beech, and oak, class-wise F1-scores range from 0.92 to 0.99, while F1-scores for other widespread species such as birch, alder, larch, Douglas fir, fir, and other deciduous species range from 0.85 to 0.96. The product provides a consistent, high-resolution, and up-to-date representation of tree species distribution across Germany. Its transferable, cost-efficient, and repeatable methodology enables reliable large-scale forest monitoring and offers a valuable basis for assessing spatial patterns and temporal changes in forest composition in the context of ongoing climatic and environmental dynamics.

BWI - Bundeswaldinventur

Die Bundeswaldinventur stellt eine forstliche Großrauminventur auf Stichprobenbasis dar. Sie soll einen Gesamtüberblick über die großräumigen Waldverhältnisse und forstlichen Produktionsmöglichkeiten liefern. Die erste Bundeswaldinventur wurde zum Stichtag 1.10.1987 durchgeführt. Eine Wiederholung der Inventur ist vom 1.1.2001 bis zum 31.12.2002 (Stichtag 1.10.2002) geplant. Die zweite Inventur wird an den gleichen Aufnahmetrakten (150x150 m) in einem Mindestraster von 4x4 km stattfinden. So werden erste statistisch abgesicherte Veränderungsdaten gewonnen. Die Aufnahmen erfolgen auf Kreisflächen an den Eckpunkten der Trakte. Erhoben werden u. a.: Betriebsart, Eigentumsart, Bestandesstruktur; Baumarten, Alter, Baumdurchmesser, Baumhöhe (Auswahl), Geländeform, Schäden, Waldränder und Totholz. Die erhobenen Daten werden dem Bundesministerium für Ernährung, Landwirtschaft und Forsten übermittelt.

Waldzustandserhebung - WZE

Der aktuelle Waldzustand wird in Nordrhein-Westfalen seit 1984 jährlich nach einem zwischen Bund und Ländern abgestimmten, statistisch repräsentativen Stichprobenverfahren erhoben. Inzwischen erfolgt die Waldzustandserhebung europaweit nach gleicher Methodik. Neben der aktuellen Zustandsbeschreibung sollen auch Schadensschwerpunkte lokalisiert und Entwicklungstendenzen des Waldzustandes aufgezeigt werden. Die Waldzustandserhebung kann und soll nicht die Ursachen der Waldschäden aufdecken. Die Erfassung erfolgt an systematisch bestimmten, permanenten Stichprobenbäumen mittels des äußerlich, vom Boden aus sichtbaren Kronenzustandes. Hauptkriterien sind Nadel- und Blattverlust und der Grad der Vergilbung. Die Aufnahmepunkte liegen in einem Grundraster von 4x4 km. Die Erhebung erfolgt in den Monaten Juli und August.

Auswertungen zum Mobilitätspanel (laufend)

Beobachtung und Analyse des Mobilitätsverhaltens der Deutschen Bevölkerung sowie zu Treibstoffverbräuchen und Kfz-Fahrleistungen. Grundsätzliche Analysen zur Datenqualität und den Stichproben 1999 und 2000. Nachfragekennziffern des Mobilitätsverhaltens. Die Verkehrsnachfrage befindet sich im betrachteten Zeitraum in einer Stagnationsphase. Aktuelle Nachfragekennziffern werden vorgestellt, ebenso wie ein Vergleich des Verkehrsverhaltens zwischen alten und neuen Bundesländern. Es findet eine Analyse der Reaktionen von Haushalten auf die im Untersuchungszeitraum gestiegenen Treibstoffpreise statt. Betroffenheit von Haushalten mit bestimmten Eigenschaften.

Tree Species - Sentinel-1/2 - Germany, 2022

The Tree Species Germany product provides a map of dominant tree species across Germany for the year 2022 at a spatial resolution of 10 meters. The map depicts the distribution of ten tree species groups derived from multi-temporal optical Sentinel-2 data, radar data from Sentinel-1, and a digital elevation model. The input features explicitly incorporate phenological information to capture seasonal vegetation dynamics relevant for species discrimination. A total of over 80,000 training and test samples were compiled from publicly accessible sources, including urban tree inventories, Google Earth Pro, Google Street View, and field observations. The final classification was generated using an XGBoost machine learning algorithm. The Tree Species Germany product achieves an overall F1-score of 0.89. For the dominant species pine, spruce, beech, and oak, class-wise F1-scores range from 0.76 to 0.98, while F1-scores for other widespread species such as birch, alder, larch, Douglas fir, and fir range from 0.88 to 0.96. The product provides a consistent, high-resolution, and up-to-date representation of tree species distribution across Germany. Its transferable, cost-efficient, and repeatable methodology enables reliable large-scale forest monitoring and offers a valuable basis for assessing spatial patterns and temporal changes in forest composition in the context of ongoing climatic and environmental dynamics.

Measurements of bending properties of kelp, Laminaria hyperborea, Heligoland, Germany

The flexural rigidity and bending modulus of kelp, Laminaria hyperborea, collected at the MarGate area (https://www.awi.de/en/science/special-groups/scientific-diving/margate.html) north of Heligoland, Germany (latitude: 54° 11.700'N, longitude: 7° 52.600'E) was determined from measurements performed at the Alfred Wegener Institute (AWI) Helmholtz Centre for Polar and Marine Research. Scientific divers from the Biological Institute Helgoland, AWI, collected nine kelps (Laminaria hyperborea) from the MarGate area on 21.06.2022. The collected kelps were transported into the laboratory in boxes filled with seawater from the site and stored in laboratory sinks filled with running aerated seawater from the North Sea during the experiments. The measurements were carried out on 23.06.2022, 25.06.2022, and 27.06.2022. They consisted of cutting strips 20 cm in length (L) and 2.5 cm in width (b) from the blades close to the stipe of each kelp. The cut-out strips were towel-dried, and their thickness (t, mm) and weight in grams were measured. The weight in grams was converted to weight per unit area (w, N/m²) to compute the flexural rigidity per unit width (J, Nm). A standard ruler with precision for the nearest millimeter was used to measure the length (L), width (b), and cantilever length (l) of strips. The thickness (t) of the strip was measured with a caliper gauge that measured to the nearest 0.01 millimeter. The weight of the strip was measured by a weighing scale (Sartorius, LE323S), which had a precision of 0.001 grams. The cut-out strips from each kelp form the nine samples tested for the bending properties. Each sample is used to repeat the cantilever test four times, i.e., both sides' ends, as Henry (2014) recommended to improve the accuracy. An apparatus consisting of two planes, one angled at 45° (θ = 45°) and the other parallel to the horizontal, was used for the test. The device was clamped onto a table on the horizontal plane. The experimental protocol consists of laying each strip onto the apparatus with the strip's edge coinciding with the apparatus's angled edge. After that, the strip is slowly moved forward with a ruler, with the ruler's zero coinciding with the strip's edge. This is done until the tip of the strip touches the inclined plane. The horizontal projection of the length of the hanging strip is equal to the distance between the ruler's tip and the apparatus's angle, termed the cantilever length (l). The flexural rigidity per unit width (J, Nm) and the bending modulus (Eb, N/m²) are then calculated with the second moment of area (I, m⁴) as in Henry (2014).

Amino acids and hexosamines in suspended matter samples collected in different oceanic areas between 1999 and 2017 from shelf seas to the deep ocean

This large set of suspended matter (SPM) samples was collected in different oceanic areas between 1999 and 2017 from shelf seas to the deep ocean. The samples were compiled from previous studies and used for statistical analyses in order to better understand particle dynamics and organic matter cycling in the ocean and to test and refine amino acid (AA) and hexosamine (HA) based biogeochemical indicators. Samples were analysed for total nitrogen (N) and total carbon (C) using a Carlo Erba nitrogen analyser 1500. Total organic carbon (TOC) was measured with the same instrument after treatment of weighed samples with 1N HCl to remove carbonate. Stable nitrogen isotopes of total particulate nitrogen (δ15N-TPN) were analysed with the mass spectrometer Thermo Finnigan MAT 252. AA and HA contents and their individual monomers were analysed by liquid chromatography using a Biochrom 30 amino acid analyzer. Contents of AA and HA are presented in nmol/g and µg/g. AAC, AAN, HAC, HAN are presented in µg/g and as percentages of TOC (AAC/C, HAC/C) or TN (AAN/N, HAN/N). AA and HA monomers are presented in Mol% and comprise aspartic acid (ASP), glutamic acid (Glu), threonine (Thr), serine (Ser), glycine (Gly), alanine (Ala), valine (Val), methionine (Met), isoleucine (Ile), leucine (Leu), tyrosine (Tyr), phenylalanine (Phe), β-Alanine (β-Ala), γ-aminobutyric acid (γ-Aba), histidine (His), ornithine (Orn), lysine (Lys) and arginine (Arg), glucosamine (Gluam) and galactosamine (Galam). Cysteic acid (CYA), taurine (TAU), methionine sulfoximine (MSO) and tryptophane (TRP) were determined only in the more recent samples. Data gaps indicate that measurements were not carried out or that they were not stored in the older data sets. The RI was calculated according to Jennerjahn and Ittekkot (1997; https://archimer.ifremer.fr/doc/00093/20403/) and the DI after Dauwe et al. (1999; doi:10.4319/lo.1999.44.7.1809). Definitions of biogeochemical indicators SDI, RTI, ox/anox and a detailed description of the methods can be found in Gaye et al. (2022).

Amino acids and hexosamine in sediment trap samples from different oceanic areas collected between 1993 and 2017 from shelf seas to the deep ocean

This large set of sediment trap samples was collected in different oceanic areas between 1993 and 2017 from shelf seas to the deep ocean. The samples were compiled from previous studies and used for statistical analyses in order to better understand particle dynamics and organic matter cycling in the ocean and to test and refine amino acid (AA) and hexosamine (HA) based biogeochemical indicators. Samples were analysed for total nitrogen (N) using a Carlo Erba nitrogen analyser 1500. Total organic carbon (TOC) was measured with the same instrument after treatment of weighed samples with 1N HCl to remove carbonate. Stable nitrogen isotopes of total particulate nitrogen (δ15N-TPN) were analysed with the mass spectrometer ThermoFisher Scientific MAT 252. AA and HA contents and their individual monomers were analysed by liquid chromatography using a Biochrom 30 amino acid analyzer. Total fluxes, TOC and AA fluxes were calculated in mg m-2 d-1. Contents of AA and HA are presented in µmol/g and µg/g. AAC, AAN, HAC, HAN are presented in µg/g and as percentages of TOC (AA-C/C, HA-C/C) or TN (AA-N/N, HA-N/N). AA and HA monomers are presented in Mol% and comprise aspartic acid (ASP), glutamic acid (Glu), threonine (Thr), serine (Ser), glycine (Gly), alanine (Ala), valine (Val), methionine (Met), isoleucine (Ile), leucine (Leu), tyrosine (Tyr), phenylalanine (Phe), β-Alanine (β-Ala), γ-aminobutyric acid (γ-Aba), histidine (His), ornithine (Orn), lysine (Lys) and arginine (Arg), glucosamine (GlcN) and galactosamine (GalN), cysteic acid (CYA), taurine (TAU), methionine sulfoximine (MSO) and tryptophane (TRP) were determined only in the more recent samples. Data gaps indicate that measurements were not carried out or that they were not stored in the older data sets. The RI was calculated according to Jennerjahn and Ittekkot (1997; https://archimer.ifremer.fr/doc/00093/20403/) and the DI after Dauwe et al. (1999; doi:10.4319/lo.1999.44.7.1809). Definitions of biogeochemical indicators SDI, RTI, ox/anox and a detailed description of the methods can be found in Gaye et al. (2022; doi:org/10.5194/bg-19-807-2022).

DNA metabarcoding data of arthropods collected in different life stages of short rotation coppices (SRC)

<p>The dataset comprises presence data of arthropods, but also on the groups 'Annelida', 'Bacillariophyta', 'Ascomycota', 'Basidiomycota', 'Bryozoa', 'Chordata', 'Cnidaria', 'Echinodermata', 'Glomeromycota', 'Haptophyta', 'Mollusca', 'Mucoromycota', 'Nematoda', 'Nemertea', 'Ochrophyta', 'Oomycota', 'Porifera', 'Pseudomonadota', 'Rhodophyta', 'Rotifera' and 'Tardigrada'. The arthropods were collected in four different life stages of short rotation coppices (harvested, young (2 years), mature (3 years) and old (4 years)) using 3 different trapping techniques: branch sampling (BS), coloured canopy Malaise traps (MT) and pitfall traps (PIT). In each life stage, three sets of traps were placed (3 sites per life stage) and activated for two weeks, each in May, June, July and August. Once in a month, a branch sampling was conducted. In the branch sampling, 16 trees within a radius of 20m around the canopy Malaise traps were randomly selected and shaken for 10 s. Arthropods fell on a plastic tarpaulin of 1x1 m that was emptied into a collection bottle where the arthropods were stored in 96.7% ethanol.</p><p>The samples were analysed using DNA metabarcoding. In DNA metabarcoding, the Cytochrome Oxidase I-Region was targeted using the primers fwhF2 (forward) and fwhR2n (reverse) from Vamos et al 2017 (https://doi.org/10.3897/mbmg.1.14625) The sequences found in the samples were matched with sequences in the BOLD database. The sequences displayed are already grouped like it is known from OTUs. For this grouping, all sequences with a similarity of 97% were compiled, which means that the grouped sequences finally comprise different genetic variants of the same taxa. For each hit in the database, a plausibility check was performed by comparing the distribution range of a species (calculated from GBIF coordinates) and the trapping locations. For each detection of a sequence in a sample, the number of reads is also given. A flagging system helps the user to estimate the degree of uncertainty arising from each species hit.</p><p>This data and the data in the datasets "https://doi.org/10.15468/9pzhm6" and "https://doi.org/10.15468/9pzhm6" belongs to one study.</p>

Verteilung von Schwermetallen in Lebensmitteln tierischer Herkunft

Feststellung der Verteilung von Schwermetallen (insbesondere Pb, Cd, Hg, Cu, Zn) in Lebensmitteln tierischer Herkunft im Hinblick auf die Repraesentanz der Stichprobennahme. Zugleich ein Beitrag ueber die Schwermetallbelastung von Lebensmitteln tierischer Herkunft.

1 2 3 4 5 … 44 45 46