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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.

Sentinel-2 Mosaike rgb und cir Hessen

Sentinel-2 cir- und rgb-Mosaike (10 m Bodenauflösung) in möglichst wolkenfreier Monatsfolge für: 2021-06, 2021-07, 2021-09, 2021-12, 2022-02, 2022-03, 2022-06, 2022-08, 2022-09, 2023-02, 2023-04, 2023-06, 2024-01, 2024-08, 2024-09, 2025-02, 2025-03, 2025-04, 2025-05, 2025-06, 2025-08, 2025-12, 2026-03, 2026-04, 2026-05, 2026-07, 2026-08

WMS SL Sentinel-2 TCI - Sentinel-2 TCI 2015

Sentinel-2 Echtfarbenbild (TCI), Kombination der Spektralkanäle B4 (rot), B3 (grün) und B2 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert das Sentinel-2 Echtfarbenbild (TCI) des Jahr 2015.

WMS SL Sentinel-2 TCI - Sentinel-2 TCI 2023

Sentinel-2 Echtfarbenbild (TCI), Kombination der Spektralkanäle B4 (rot), B3 (grün) und B2 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert das Sentinel-2 Echtfarbenbild (TCI) des Jahr 2023.

WMS SL Sentinel-2 CIR - Sentinel-2 CIR 2022

Sentinel-2 Falschfarbenbild (ColoredInfraRed), Kombination der Spektralkanäle B8 (rot), B4 (grün) und B3 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert die Sentinel-2 Falschfarbenbilder(CIR) des Jahr 2022.

WMS SL Sentinel-2 NDVI - Sentinel-2 NDVI 2016

Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert den Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI) des Jahr 2016.

WMS SL Sentinel-2 NDVI - Sentinel-2 NDVI 2019

Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert den Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI) des Jahr 2019.

WMS SL Sentinel-2 NDVI - Sentinel-2 NDVI 2026

Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert den Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI) des Jahr 2026.

Database for Integrating vision transformers with multi-criteria analysis for Direct Air Capture and CO2 Storage (DACCS) siting

This dataset supports first-stage site screening for Direct Air Capture and CO₂ Storage (DACCS) in the North German Basin (NGB) and the German North Sea. It comprises (1) multi-criteria decision-making (MCDM) suitability layers and scores for onshore and offshore areas, derived from geospatial criteria (elevation, soil type, population, infrastructure, storage traps, renewable energy potential, etc.); (2) aligned Sentinel-2 satellite imagery with MCDM-derived suitability labels; (3) DINOv3 (ViT-L/16) patch features extracted from that imagery; (4) a trained deep neural network (RegressionMLP) that predicts suitability from Sentinel-2 images alone (`suitability_regression_model.pth`); and (5) evaluation outputs (metrics, calibration, confusion matrix, spatial and ranking indices). The MCDM maps provide reference suitability in 0–1 for rational site selection; the trained model enables suitability prediction from only Sentinel-2 imagery in data-sparse regions where full MCDM layers are unavailable. All workflows are scripted (Python) and reproducible with documented dependencies and paths.

WMS SL Sentinel-2 TCI - Sentinel-2 TCI 2017

Sentinel-2 Echtfarbenbild (TCI), Kombination der Spektralkanäle B4 (rot), B3 (grün) und B2 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert das Sentinel-2 Echtfarbenbild (TCI) des Jahr 2017.

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