TU Berlin

Geoinformation in Environmental PlanningKleinschmit, Birgit

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Prof. Dr. Birgit Kleinschmit



Phone: +49 (0)30 / 314 - 72 84 7


Room: EB 235a
Consultation hour: by arrangement

Personal Data
Date and place of birth: 1973 (Münster, Westphalia, Germany)
Employment and academic vita
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
Assistant Professor (“Juniorprofessorin”) at the Department of Geoinformation Processing for Landscape and Environmental Planning of the Berlin University of Technology
Consultant and software developer, INTEND Geoinformatik GmbH, Kassel, Germany
Scientific staff member ("Wissenschaftliche Mitarbeiterin"), Georg-August-Universität, Göttingen, Department of Forest Assessment & Remote Sensing, Forest Growth, Forest Planning
Diploma study of forest science at the University of Göttingen
Doctorate (doctor forest), Georg-August Universität Göttingen, Grade: magna cum laude
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



      Aljoumani, B., Sanchez-Espigares, J., Kluge, B., Wessolek, G. and Kleinschmit, B. (2022). Analyzing Temporal Trends of Urban Evaporation Using Generalized Additive Models. Land

      Vallentin, C., Harfenmeister, K., Itzerott, S., Kleinschmit, B., Conrad, C. and Spengler, D. (2022). Suitability of satellite remote sensing data for yield estimation in northeast Germany. Precision Agriculture, 52–82.

      Duarte Rocha, A., Vulova, S., van der Tol, C., Förster, M. and Kleinschmit, B. (2022). Modelling hourly evapotranspiration in urban environments with SCOPE using open remote sensing and meteorological data. Hydrology and Earth System Sciences


      Rocchini, D., Salvatori, N., Beierkuhnlein, C., Chiarucci, A., de Boissieu, F., Förster, M., Garzon-Lopez, C., Gillespie, T. W., Hauffe, H., He, K., Kleinschmit, B., Lenoir, J., Malavasi, M., Moudrý, V., Nagendra, H., Payne, D., Šímová, P., Torresani, M., Wegmann, M. and Féret, J.-B. (2021). From local spectral species to global spectral communities: A benchmark for ecosystem diversity estimate by remote sensing.. Ecological Informatics. Elsevier, 1-10.

      Hölzl, S. E., Veskov, M., Scheibner, T., Le, T. T. and Kleinschmit, B. (2021). Vulnerable socioeconomic groups are disproportionately exposed to multiple environmental burden in Berlin - implications– for planning. International journal of urban sustainable development, 1-18.

      Gränzig, T., Fassnacht, F. E., Kleinschmit, B. and Förster, M. (2021). Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach.. International Journal of Applied Earth Observation and Geoinformation

      Bauhus, J., Seeling, U., Dieter, M., Farwig, N., Hafner, A., Kätzel, R., Kleinschmit, B., Lang, F., Lindner, M., Möhring, B., Müller, J., M., N., Richter, K. and Schraml, U. (2021). Die Anpassung von Wäldern und Waldwirtschaft an den Klimawandel. Berichte über Landwirtschaft-Zeitschrift für Agrarpolitik und Landwirtschaft, 1-158.

      Schulz, C., Holtrave, A. and Kleinschmit, B. (2021). Large-scale winter catch crop monitoring with Sentinel-2 time series and machine learning–An alternative to on-site controls?. Computers and Electronics in Agriculture

      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, 1-13.


      Vulova, S., Meier, F., Fenner, D., Nouri, H. and Kleinschmit, B. (2020). Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1-15.

      Other Publications

      Using radiative transfer models for mapping soil moisture content under grassland with UAS-borne hyperspectral data
      Citation key Döpper2021
      Author Veronika U. Döpper and Alby Duarte Rocha and Tobias Gränzig and Birgit Kleinschmit and Michael Förster
      Year 2021
      ISSN 0277-786X
      DOI https://doi.org/10.1117/12.2600296
      Journal Proc. SPIE 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII
      Abstract Soil moisture content (SMC) is a key parameter of environmental processes. Remote sensing provides effective methods for mapping SMC at different spatial resolutions. Using UAS-borne hyperspectral observations enables a SMC retrieval at sub-meter scales. Radiative transfer models (RTMs) such as ProSAIL or Scope include a SMC specific input variable and are thus a potential tool to derive SMC and avoiding extensive reference SMC measurements. The inverse application of RTMs supplies information on SMC and plant traits. Scope and ProSAIL involve SMC data of the root zone and at the surface, respectively. The combined use of both models offers the possibility to derive SMC at two vertical depths. Moreover, SMC relevant vegetation proxies such as leaf water content can be retrieved and alternatively used as indicator for SMC. Such plant traits are highest correlated to SMC at depths of major water uptake. However, their response can have a significant time-lag. We analyze the derivation of SMC at the soil surface and at the root zone using the SMC parameters within existing RTMs. As a first step, we investigate on the sensitivity of ProSAIL and Scope to their soil moisture parameters. We apply these findings on UAS-borne hyperspectral and TIR imagery acquired over a pre-alpine TERENO grassland area. The site is equipped with a SoilNet that measures SMC at different depths. Using this data, we assess the vertical extent of both soil moisture content parameters. By inverse modelling of the vegetation parameters and the use of the temporally continuous SoilNet data at root zone level, we analyze the time-lag between changes in SMC and the corresponding plant trait response to optimize the retrieval of SMC.
      Bibtex Type of Publication Kleinschmit
      Link to publication Download Bibtex entry


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