direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Page Content

Prof. Dr. Birgit Kleinschmit

Lupe [1]
© B. Kleinschmit

Head

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

Email: birgit.kleinschmit(at)tu-berlin.de [2]

Room: EB 235a
Consultation hour: by arrangement

Personal Data
Date and place of birth: 1973 (Münster, Westphalia, Germany)
Employment and academic vita
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
Degrees
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

      Publications

      • Articles
      • Other Publications

      Articles

      Modeling urban evapotranspiration using remote sensing, flux footprints, and artificial intelligence
      Citation key Vulova2021
      Author Vulova, S. and Meier, F. and Rocha, A. D. and Quanz, J. and Nouri, H. and and Kleinschmit, B.
      Pages 1-13
      Year 2021
      ISSN 0048-9697
      DOI https://doi.org/10.1016/j.scitotenv.2021.147293
      Journal Science of The Total Environment
      Volume 786
      Abstract As climate change progresses, urban areas are increasingly affected by water scarcity and the urban heat island effect. Evapotranspiration (ET) is a crucial component of urban greening initiatives of cities worldwide aimed at mitigating these issues. However, ET estimation methods in urban areas have so far been limited. An expanding number of flux towers in urban environments provide the opportunity to directly measure ET by the eddy covariance method. In this study, we present a novel approach to model urban ET by combining flux footprint modeling, remote sensing and geographic information system (GIS) data, and deep learning and machine learning techniques. This approach facilitates spatio-temporal extrapolation of ET at a half-hourly resolution; we tested this approach with a two-year dataset from two flux towers in Berlin, Germany. The benefit of integrating remote sensing and GIS data into models was investigated by testing four predictor scenarios. Two algorithms (1D convolutional neural networks (CNNs) and random forest (RF)) were compared. The best-performing models were then used to model ET values for the year 2019. The inclusion of GIS data extracted using flux footprints enhanced the predictive accuracy of models, particularly when meteorological data was more limited. The best-performing scenario (meteorological and GIS data) showed an RMSE of 0.0239 mm/h and R2 of 0.840 with RF and an RMSE of 0.0250 mm/h and a R2 of 0.824 with 1D CNN for the more vegetated site. The 2019 ET sum was substantially higher at the site surrounded by more urban greenery (366 mm) than at the inner-city site (223 mm), demonstrating the substantial influence of vegetation on the urban water cycle. The proposed method is highly promising for modeling ET in a heterogeneous urban environment and can support climate change mitigation initiatives of urban areas worldwide.
      Bibtex Type of Publication Kleinschmit
      Link to publication [3] Download Bibtex entry [4]

      Back [5]

      Other Publications

      You are at page:1 | 2 [6] | 3 [7] | 4 [8] | 5 [9] | 6 [10] | 7 [11] | 8 [12] | 9 [13] | 10 [14] | 11 [15] | 12 [16] | 13 [17] | 14 [18] | 15 [19]

      next >> [20]

      2021

      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. [21]. Ecological Informatics. Elsevier, 1-10.

      Kleinschmit, B., Singelton, A. and Gärtner, P. (2021). Identifying drivers and barriers of floodplain vegetation growth in the lower reaches of the Tarim River, China. [22]. Sustainable Management of River Oases along the Tarim River/China (SuMaRiO). Schwerzerbart, 87-101.

      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 [23]. 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. [24]. 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 [25]. Berichte über Landwirtschaft-Zeitschrift für Agrarpolitik und Landwirtschaft, 1-158.

      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 [26]. Science of The Total Environment, 1-13.

      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 [27]. Proc. SPIE 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII

      2020

      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 [28]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1-15.

      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 [29]. remote sensing, 1-27.

      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 [30]. Earth System Science Data, 2289-2309.

      You are at page:1 | 2 [31] | 3 [32] | 4 [33] | 5 [34] | 6 [35] | 7 [36] | 8 [37] | 9 [38] | 10 [39] | 11 [40] | 12 [41] | 13 [42] | 14 [43] | 15 [44]

      next >> [45]

      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 [46]
      ------ Links: ------

      Zusatzinformationen / Extras

      Quick Access:

      Schnellnavigation zur Seite über Nummerneingabe

      Auxiliary Functions

      This site uses Matomo for anonymized webanalysis. Visit Data Privacy for more information and opt-out options.
      Copyright TU Berlin 2008