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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.

SubSurfaceGeoRobo: A Comprehensive Underground Dataset for SLAM-based Geomonitoring with Sensor Calibration

With the introduction of mobile mapping technologies, geomonitoring has become increasingly efficient and automated. The integration of Simultaneous Localization and Mapping (SLAM) and robotics has effectively addressed the challenges posed by many mapping or monitoring technologies, such as GNSS and unmanned aerial vehicles, which fail to work in underground environments. However, the complexity of underground environments, the high cost of research in this area, and the limited availability of experimental sites have hindered the progress of relevant research in the field of SLAM-based underground geomonitoring. In response, we present SubSurfaceGeoRobo, a dataset specifically focused on underground environments with unique characteristics of subsurface settings, such as extremely narrow passages, high humidity, standing water, reflective surfaces, uneven illumination, dusty conditions, complex geometry, and texture less areas. This aims to provide researchers with a free platform to develop, test, and train their methods, ultimately promoting the advancement of SLAM, navigation, and SLAM-based geomonitoring in underground environments. SubSurfaceGeoRobo was collected in September 2024 in the Freiberg silver mine in Germany using an unmanned ground vehicle equipped with a multi-sensor system, including radars, 3D LiDAR, depth and RGB cameras, IMU, and 2D laser scanners. Data from all sensors are stored as bag files, allowing researchers to replay the collected data and export it into the desired format according to their needs. To ensure the accuracy and usability of the dataset, as well as the effective fusion of sensors, all sensors have been jointly calibrated. The calibration methods and results are included as part of this dataset. Finally, a 3D point cloud ground truth with an accuracy of less than 2 mm, captured using a RIEGL scanner, is provided as a reference standard.

Collection of data sets for the Clouds over cOMPlEX environment - EarthCARE (COMPEX-EC) campaign, carried out in Kiruna in spring 2025

This data collection unites the individual data sets of the COMPEX-EC (Clouds over cOMPlEX environment - EarthCARE) campaign, carried out in Kiruna 2.-16.4.2025. COMPEX-EC has been designed as an EarthCARE validation campaign. For that purpose, Polar 5 (C-GAWI) has been equipped with instrumentation similar to the one operated on EarthCARE (W-band radar, lidar, radiometers, spectral imagers). Seven research flights (summing up to more than 30 flight hours) were conducted each of them underflying the EarthCARE satellite to validate its performance.

MELWA - Methanmessungen mit Lidar über Wasser

Arctic PASSION - High Resolution Synthetic Aperture Radar based Risk Index Outcome (AP-RIO)

The Risk Index Outcome (RIO) is a critical component of the Polar Operational Limit Assessment Risk Indexing System (POLARIS) developed by the International Maritime Organization (IMO, 2016). RIO evaluates the operational risks for ships navigating in ice-infested waters by evaluating ice conditions and offers a quantifiable measure of risk that aids in decision-making for safe navigation in polar regions based on ship ice class, sea ice type/stage of development (SOD) and sea ice concentration (SIC). The DMI-led Automated Sea Ice Products (DMI-ASIP; Wulf et al., 2024, dataset) provides daily maps of SOD and SIC based on Sentinel-1 SAR imagery, AMSR-2 Passive Microwave and Ice Charts from the Greenland and Canadian Ice Services, combined with novel AI retrieval and processing techniques. In the framework of EU funded Arctic PASSION project, we produced 10 years of satellite observation based weekly RIO maps referred as the Arctic PASSION-RIO (AP-RIO) by leveraging DMI-ASIP datasets. The AP-RIO dataset will provide weekly risk assessment maps for the given ship classes and will support the establishment of a 10 year climatology thereby enabling the assessment of RIO variability in the years covered by the input DMI-ASIP products. The AP-RIO dataset will enhance the safety and efficiency of maritime operations in the polar seas, providing a robust reference for evaluating normal and extreme ice conditions. AP-RIO is produced in the framework of the Arctic PASSION project (European Union's Horizon 2020 research and innovation program under grant agreement No. 101003472) and supported by the DMI-ASIP development team. Algorithm and Processing Scheme: SIC and SOD from ASIP are processed (by taking the mean and mode respectively) into a weekly field based on the daily files for that week. This is done for the time period of 3 Oct. 2014 - 3 Oct. 2024. The weekly SOD is used to find the Risk Value (RV) by looking at the lookup table (Dybkjær et al. 2025a). Risk Index Outcome (RIO) values are computed for each pixel in the field based on the RIO formula (RIO = SIC x RV) using the SIC from ASIP and the found RV. The meaning of the computed RIO values can be interpreted using the table in (Dybkjær et al. 2025b). The RIO field is finally saved to weekly NetCDF files.

Messstelle LT ALTE WESER (RADAR), WESER

Messstelle betrieben von BREMERHAVEN.

CropTypes - Crop Type Maps for Germany - Yearly, 10m

This raster dataset shows the main type of crop grown on each field in Germany each year. Crop types and crop rotation are of great economic importance and have a strong influence on the functions of arable land and ecology. Information on the crops grown is therefore important for many environmental and agricultural policy issues. With the help of satellite remote sensing, the crops grown can be recorded uniformly for whole Germany. Based on Sentinel-1 and Sentinel-2 time series as well as LPIS data from some Federal States of Germany, 18 different crops or crop groups were mapped per pixel with 10 m resolution for Germany on an annual basis since 2018. These data sets enable a comparison of arable land use between years and the derivation of crop rotations on individual fields. More details and the underlying (in the meantime slightly updated) methodology can be found in Asam et al. 2022.

BBD-V, Teilvorhaben: DLR e.V

Schwerpunktprogramm (SPP) 1294: Bereich Infrastruktur - Atmospheric and Earth system research with the 'High Altitude and Long Range Research Aircraft' (HALO), NAWDEX - North Atlantic Waveguide and Downstream Impact Experiment

The North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) aims to provide the foundation for future improvements in the prediction of high impact weather events over Europe. The concept for the field experiment emerged from the WMO THORPEX program and contributes to the World Weather Research Program WWRP in general and to the High Impact Weather (HIWeather) project in particular. An international consortium from the US, UK, France, Switzerland and Germany has applied for funding of a multi-aircraft campaign supported by enhanced surface observations, over the North Atlantic and European region. The importance of accurate weather predictions to society is increasing due to increasing vulnerability to high impact weather events, and increasing economic impacts of weather, for example in renewable energy. At the same time numerical weather prediction has undergone a revolution in recent years, with the widespread use of ensemble predictions that attempt to represent forecast uncertainty. This represents a new scientific challenge because error growth and uncertainty are largest in regions influenced by latent heat release or other diabatic processes. These regions are characterized by small-scale structures that are poorly represented by the operational observing system, but are accessible to modern airborne remote-sensing instruments. HALO will play a central role in NAWDEX due to the unique capabilities provided by its long range and advanced instrumentation. With coordinated flights over a period of days, it will be possible to sample the moist inflow of subtropical air into a cyclone, the ascent and outflow of the warm conveyor belt, and the dynamic and thermodynamic properties of the downstream ridge. NAWDEX will use the proven instrument payload from the NARVAL campaign which combines water vapor lidar and cloud radar, supplemented by dropsondes, to allow these regions to be measured with unprecedented detail and precision. HALO operations will be supported by the DLR Falcon aircraft that will be instrumented with wind lidar systems, providing synergetic measurements of dynamical structures. These measurements will allow the first closely targeted evaluation of the quality of the operational observing and analysis systems in these crucial regions for forecast error growth. They will provide detailed knowledge of the physical processes acting in these regions and especially of the mechanisms responsible for rapid error growth in mid-latitude weather systems. This will provide the foundation for a better representation of uncertainty in numerical weather predictions systems, and better (probabilistic) forecasts.

Zum Verständnis der Entstehung und Trajektorien von großem Hagel (LIFT)

Derzeitige radar-basierte Nowcastingverfahren basieren auf der Annahme, dass die zeitliche Entwicklung von Hagelereignissen in erster Linie durch Advektionsvorgänge gesteuert ist; die relevanten physikalischen Prozesse, die für die Entstehung und das Größenwachstum von Hagel entscheidend sind, bleiben dabei unberücksichtigt. In Verbindung mit der komplexen internen Struktur und Dynamik von Hagelstürmen ergeben sich daraus große Unsicherheiten bei der Vorhersage der Hagelgrößenverteilung und der von Hagel betroffenen Fläche am Boden. Das Ziel des Projekts LIFT (Large Hail Formation and Trajectories) ist es, die Hagelentstehung und Hageltrajektorien besser zu verstehen, um daraus als wichtige Komponenten eines physikalisch-basierten Nowcastings erstmals ein radar-basiertes Verfahren für das Hagelwachstums zu entwickeln. Zu diesem Zweck wird im Rahmen von LIFT eine Messkampagne Süddeutschland durchgeführt, wo die größte Hagelwahrscheinlichkeit in Deutschland auf vielfältige Beobachtungssysteme trifft, die im Rahmen der Messkampagne Swabian MOSES mit einem dichten Netzwerk betrieben werden. Zum ersten Mal werden im Rahmen von LIFT moderne Radargeräte, In-situ Messgeräte, Fotogrammetrie und numerische Modellierung synergistisch kombiniert und ein umfassender Datensatz zur Rekonstruktion der zeitlichen Entwicklung des Hagelwachstums erstellt. Betroffene Bürger werden aktiv in die Messaktivitäten mit einbezogen und aufgerufen, Hagelkörnern einschließlich ihrer Haupteigenschaften in die WarnWetter App des DWD zu melden. Die Messkampagne mit ihrem mobilen und flexiblen Konzept beinhaltet die Anwendung neuer, innovativer Messtechniken, darunter Lagrangesche Trajektorien mittels kleiner Messsysteme, die in die Wolken eingebracht werden, und dronengesteuerte Luftbildaufnahmen zur Bestimmung der Hagelspektren. Aus Fernerkundungsdaten gewonnene Signaturen von Hagelereignissen liefern Informationen über die Charakteristika der Hagelereignisse und werden mittels numerischer Simulationen sorgfältig auf Messungenauigkeiten und Sensitivitäten bzgl. atmosphärischer Umgebungsvariablen evaluiert. Indikatoren für die Hagelentstehung und das Hagelwachstum werden aus Beobachtungsdaten und Simulationen identifiziert, und liefern die Grundlage für ein beobachtungs-basiertes Hagelwachstumsmodell. Schließlich wird dieses Multi-Parameter Hagelwachstumsmodell mit den bestimmten Hageltrajektorien und Schmelzprozessen kombiniert, um zu bestimmen, welche Prozesse am wichtigsten sind für das Nowcasting von Hagel. Das Projekt LIFT liefert damit einen wichtigen Betrag für zukünftige radar-basierte Hagelwarnsysteme mit einer verbesserten Vorhersagezeit und Vorhersagequalität.

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