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Prof. Dr. Birgit Kleinschmit

Lupe
© B. Kleinschmit

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

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      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.. 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

      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.

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      Other Publications

      A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany
      Citation key Fersch2020
      Author Fersch, B. and Francke, T. and Heistermann, M. and Schrön, M. and Döpper, V. and Jakobi, J. and Baroni, G. and Blume, T. and Bogena, H. and Budach, C. and Gränzig, T. and Förster, M. and Güntner, A. and Hendricks Franssen, H.J. and Kasner, M. and Köhli, M. and Kleinschmit, B. and Kunstmann, H. and Patil, A. and Rasche, D. and Scheiffele, L. and Schmidt, U. and Szulc-Seyfried, S. and Weimar, J. and Zacharias, S. and Zreda, M. and Heber, B. and Kiese, R. and Mares, V. and Mollenhauer, H. and Völksch, I. and Oswald, S.
      Pages 2289-2309
      Year 2020
      ISSN 1866-3508
      DOI https://doi.org/10.5194/essd-12-2289-2020
      Journal Earth System Science Data
      Volume 12
      Abstract Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 [Titel anhand dieser DOI in Citavi-Projekt übernehmen] (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b [Titel anhand dieser DOI in Citavi-Projekt übernehmen] (Fersch et al., 2020b).
      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