Inhalt des Dokuments
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Due to the current situation, the offices are currently not staffed. However, the department is reachable via email.
Start des Forschungsprojekts BioS
- DJI M300 UAV with LiDAR sensor in burned forest stand, Yakutia
- © R. Jackisch
Das Fachgebiet Geoinformation in der Umweltplanung koordiniert das neue Forschungsprojekt ‘BioS – Monitoring und Modellierung des kritischen Gleichgewichts von Biomasse und Biodiversität in Sibirien mit Fernerkundungsmethoden’, gefördert durch die Forschungsförderung Pro Nachhaltigkeit der TU Berlin, zusammen mit den Partnern des Alfred-Wegener-Institut (AWI), Helmholtz-Zentrum für Polar- und Meeresforschung Forschungsstelle Potsdam und der Ammosov North-Eastern Federal University (NEFU) in Jakutsk, Russland.
Forschungskolloquium – Wintersemester 2021/22
3.30 pm - via Zoom-Meeting
| 11.11.2021 | Christian Schulz | The spectral-temporal profiling of forest tree stands in Lower Saxony - How does SAR contribute to species mapping? |
|---|---|---|
| 24.11.2021 | Lukas Blickensdörfer (Thünen Institut) | Remote Sensing Based Tree Species Mapping for Germany: Discussion of First Results and State of Progress |
| 09.12.2021 | Felix Lobert (Thünen Institut) | Mowing event detection with CNNs and Sentinel-1, Sentinel-2, and Landsat 8 time series |
| 12.01.2022 | Robert Jackisch | First results of an integrated UAV-based multispectral and LiDAR survey to investigate the boreal forest in Central Yakutia, Russia |
| 26.01.2022 | Steve Ahlswede | Deep Learning for Forest Monitoring via Multi-Modal Data |
| 09.02.2022 | Paul Magdon (Uni Göttingen) | Development of an open reference database for remote sensing tree species classification based on the German National Forest Inventory |
| 02.03.2022 | Alby Duarte Rocha | R-INLA applied to Remote Sensing - spatial and spatial-temporal Bayesian models |
| 16.03.2022 | Ann-Kathrin Holtgrave | Current state of research on satellite-based detection of grassland use intensities and its implication for the new CopGrün project |
2021
Vulova, S., Meier, F., Rocha, A. D., Quanz, J., Nouri, H. and and Kleinschmit, B. (2021). Modeling urban evapotranspiration using remote sensing, flux footprints, and artificial intelligence. Science of The Total Environment
Döpper, V. U., Rocha, A. D., Gränzig, T., Kleinschmit, B. and Förster, M. (2021). Using radiative transfer models for mapping soil moisture content under grassland with UAS-borne hyperspectral data. Proc. SPIE 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII
2020
Fersch, B., Francke, T., Heistermann, M., Schrön, M., Döpper, V., Jakobi, J., Baroni, G., Blume, T., Bogena, H., Budach, C., Gränzig, T., Förster, M., Güntner, A., Hendricks Franssen, H., Kasner, M., Köhli, M., Kleinschmit, B., Kunstmann, H., Patil, A., Rasche, D., Scheiffele, L., Schmidt, U., Szulc-Seyfried, S., Weimar, J., Zacharias, S., Zreda, M., Heber, B., Kiese, R., Mares, V., Mollenhauer, H., Völksch, I. and Oswald, S. (2020). A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany. Earth System Science Data, 2289-2309.
Holtgrave, A., Röder, N., Ackermann, A., Erasmi, S. and Kleinschmit, B. (2020). Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring. remote sensing, 1-27.
Döpper, V., Gränzig, T., Kleinschmit, B. and Förster, M. (2020). Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification.. remote sensing
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