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Prof. Dr. Birgit Kleinschmit
- © B. Kleinschmit
Head
Phone: +49 (0)30 / 314 - 72 84 7
Email: birgit.kleinschmit(at)tu-berlin.de
Room: EB 235a
Consultation hour: by arrangement
| Date and place of birth: 1973 (Münster, Westphalia, Germany) |
| 2011 | Announced as University Professor and Head of the Department of Geoinformation in Environmental Planning at the Institute of Landscape Architecture and Environmental Planning of the Berlin University of Technology |
| 2003-2011 | Assistant Professor (“Juniorprofessorin”) at the Department of Geoinformation Processing for Landscape and Environmental Planning of the Berlin University of Technology |
| 2001-2003 | Consultant and software developer, INTEND Geoinformatik GmbH, Kassel, Germany |
| 1998-2001 | Scientific staff member ("Wissenschaftliche Mitarbeiterin"), Georg-August-Universität, Göttingen, Department of Forest Assessment & Remote Sensing, Forest Growth, Forest Planning |
| 1993-1998 | Diploma study of forest science at the University of Göttingen |
| 2001 | Doctorate (doctor forest), Georg-August Universität Göttingen, Grade: magna cum laude |
| 1998 | Diploma, Georg-August Universität Göttingen, Grade: 1,9 (on a scale from 1 to 6, where 1 is highest) |
Research Topics
- Studying land use dynamics on different scales to understand natural and human environmental systems using geospatial information technologies (GIS & Remote Sensing)
- Modelling environmental changes and assessing the impacts on humans and ecosystems
- Knowledge-based combination of geoinformation and remote sensing data
- Evaluating of new sensor technologies
Important Functions, Awards, Honors
- Since 2019
Member of Scientific Advisory Board on Forest Policy at the Federal Ministry of Food and Agriculture - Since 2019
Research Transfer advisory board Member, TU Berlin - Since 2018
Deputy Director, Institute of Landscape Architecture and Environmental Planning, TU Berlin - Since 2015
Co-speaker of the DFG research training group Urban water interfaces - Since 2016
Admissions and Steering Committee member of the Berlin International Graduate School in Model and Simulation based Research (BIMoS), TU Berlin - 2012-2018
Leader of the Special Interest Group „Analysis of remote sensing data” of the German Association for Photogrammetry, Remote Sensing and Geoinformation - Since 2018
Member of the Commission for the Allocation of Doctoral Grants of Elsa Neumann Scholarships - Since 2010
Steering Committee member of Geo.X – Research Network for Geosciences in Berlin and Potsdam
Other Publications
| Citation key | Doepper2020 |
|---|---|
| Author | Döpper, V. and Gränzig, T. and Kleinschmit, B. and Förster, M. |
| Year | 2020 |
| DOI | https://doi.org/10.3390/rs12101552 |
| Journal | remote sensing |
| Volume | 12 |
| Number | 10 |
| Abstract | Thermal infrared measurements acquired with unmanned aerial systems (UAS) allow for high spatial resolution and flexibility in the time of image acquisition to assess ground surface temperature. Nevertheless, thermal infrared cameras mounted on UAS suffer from low radiometric accuracy as well as low image resolution and contrast hampering image alignment. Our analysis aims to determine the impact of the sun elevation angle (SEA), weather conditions, land cover, image contrast enhancement, geometric camera calibration, and inclusion of yaw angle information and generic and reference pre-selection methods on the point cloud and number of aligned images generated by Agisoft Metashape. We, therefore, use a total amount of 56 single data sets acquired on different days, times of day, weather conditions, and land cover types. Furthermore, we assess camera noise and the effect of temperature correction based on air temperature using features extracted by structure from motion. The study shows for the first time generalizable implications on thermal infrared image acquisitions and presents an approach to perform the analysis with a quality measure of inter-image sensor noise. Better image alignment is reached for conditions of high contrast such as clear weather conditions and high SEA. Alignment can be improved by applying a contrast enhancement and choosing both, reference and generic pre-selection. Grassland areas are best alignable, followed by cropland and forests. Geometric camera calibration hampers feature detection and matching. Temperature correction shows no effect on radiometric camera uncertainty. |
| Bibtex Type of Publication | Kleinschmit |