TU Berlin

Geoinformation in Environmental PlanningKleinschmit, Birgit

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Prof. Dr. Birgit Kleinschmit

Lupe

Head

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

Email:

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

      Articles

      Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning
      Citation key Vulova2020
      Author Vulova, S. and Meier, F. and Fenner, D. and Nouri, H. and Kleinschmit, B.
      Pages 1-15
      Year 2020
      ISSN 1939-1404
      DOI 10.1109/JSTARS.2020.3019696
      Journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
      Volume 562
      Abstract Urban areas tend to be warmer than their rural surroundings, well-known as the “urban heat island” effect. Higher nocturnal air temperature (Tair) is associated with adverse effects on human health, higher mortality rates, and higher energy consumption. Prediction of the spatial distribution of Tair is a step towards the “Smart City” concept, providing an early warning system for vulnerable populations. The study of the spatial distribution of urban Tair was thus far limited by the low spatial resolution of traditional data sources. Volunteered geographic information (VGI) provides alternative data with higher spatial density, with citizen weather stations monitoring Tair continuously in hundreds or thousands of locations within a single city. In this study, the aim was to predict the spatial distribution of nocturnal Tair in Berlin, Germany, one day in advance at a 30- m resolution using open-source remote sensing and geodata from Landsat and Urban Atlas, crowdsourced Tair data, and machine learning (ML) methods. Results were tested with a “leave-onedate- out” training scheme (testingcrowd) and reference Tair data (testingref). Three ML algorithms were compared - Random Forest (RF), Stochastic Gradient Boosting (GBM), and Model Averaged Neural Network (avNNet). The optimal model based on accuracy and computational speed is RF, with an average RMSE for testingcrowd of 1.16 °C (R2 = 0.512) and RMSE for testingref of 1.97 °C (R2 = 0.581). Overall, the most Important GIS predictors were morphometric parameters and albedo. The proposed method relies on open-source datasets and can, therefore, be adapted to many cities worldwide.
      Bibtex Type of Publication Kleinschmit
      Link to original publication Download Bibtex entry

      Other Publications

      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. 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. 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. Earth System Science Data, 2289-2309.


      Döpper, V., Gränzig, T., Kleinschmit, B. and Förster, M. (2020). Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification.. remote sensing, 1-22.


      2019

      Vallentin, C., Dobers, E. S., Itzerott, S., Kleinschmit, B. and Spengler, D. (2019). Delineation of management zones with spatial data fusion and belief theory. Precision Agriculture. Springer, 1-29.


      Schulz, C. and Kleinschmit, B. (2019). Zentralasiatische Tugai-Auwälder – Ein gefährdetes Ökosystem. Auenmagazin, 11-17.


      2018

      Holtgrave, A.-K., Förster, M., Greifeneder, F., Notarnicola, C. and Kleinschmit, B. (2018). Estimation of Soil Moisture in Vegetation-Covered Floodplains with Sentinel-1 SAR Data Using Support Vector Regression. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 85–101.


      Klinke, R., Kuechly, H., Frick, A., Förster, M., Schmidt, T., Holtgrave, A.-K. a. K. B., Spengler, D. and Neumann, C. (2018). Indicator-Based Soil Moisture Monitoring ofWetlands by Utilizing Sentinel and Landsat Remote Sensing Data. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 71–84.


      Heuner, M., Schröder, B., Schröder, U. and Kleinschmit, B. (2018). Contrasting elevational responses of regularly flooded 4 marsh plants in navigable estuaries. Ecohydrology & Hydrobiology, 1-17.


      Luan, X., Buyantuev, A., Baur, A. H., Kleinschmit, B., Wang, H., Wei, S., Liu, M. and Xu, C. (2018). Linking greenhouse gas emissions to urban landscape structure: the relevance of spatial and thematic resolutions of land use/cover data. Landscape Ecology, 1211–1224.


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