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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 | Valentin2019 |
|---|---|
| Author | Vallentin, C. and Dobers, E. S. and Itzerott, S. and Kleinschmit, B. and Spengler, D. |
| Pages | 1-29 |
| Year | 2019 |
| ISSN | 1573-1618 |
| DOI | https://doi.org/10.1007/s11119-019-09696-0 |
| Journal | Precision Agriculture |
| Publisher | Springer |
| Abstract | Precision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other “black box” models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: “low yield”, “medium yield” and “high yield”. It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture. |
| Bibtex Type of Publication | Kleinschmit |
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Geoinformation in Environmental Planning Lab
Office EB5
Straße des 17. Juni 145
D - 10623 Berlin
Phone: +49 (0)30 314 - 73 29 0
Fax: +49 (0)30 314 - 23 50 7
e-mail query
Office EB5
Straße des 17. Juni 145
D - 10623 Berlin
Phone: +49 (0)30 314 - 73 29 0
Fax: +49 (0)30 314 - 23 50 7
e-mail query