Postdoc
Room 2.0.EG.13
E-Mail: lisa.mandl (at) tum.de
Tel.: 08161-71 4373
Fax: 08161-71 4616
Research interests
- Optical and active remote sensing
- Spatiotemporal analyses
- Knowledge- & data-driven models
- Spatial inferences
- Big data
Selected publications
Mandl, L., Viana-Soto, A., Seidl, R., Stritih, A. Senf, C. (2024): Unmixing-based forest recovery indicators for predicting long-term recovery success. Remote Sensing of Environment, 308, Link: https://doi.org/10.1016/j.rse.2024.114194
Mandl, L., Stritih, A., Seidl, R., Ginzler, C., Senf, C. (2023) Spaceborne LiDAR for characterizing forest structure across scales in the European Alps. Remote Sensing in Ecology and Conservation, 9, 599-614. Link
Mandl, L., Lang, S. (2023) Uncovering Early Traces of Bark Beetle Induced Forest Stress via Semantically Enriched Sentinel-2 Data and Spectral Indices (2023). Journal of Photogrammetry, Remote Sensing and Geoinformation Science 91, 211-231. Link
CV
| Since July 2025 | Postdoc at the Professorship of Earth Observation for Ecosystem Management, Technical University of Munich |
| 2024- July 2025 | PhD student at the Professorship of Earth Observation for Ecosystem Management, Technical University of Munich |
| 2022-2024 | PhD student at the Chair of Ecosystem Dynamics and Forest Management, Technical University of Munich |
| 2019-2021 | Master of Science in Applied Geoinformatics, University of Salzburg |
| 2018-2025 | Scientist at Berchtesgaden National Park |
| 2015-2018 | Bachelor of Science in Geography, Ludwig-Maximilians-Universität Munich |