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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

      Articles

      Revealing areas of high nature conservation importance in aseasonally dry tropical forest in Brazil: Combination of modelled plantdiversity hot spots and threat patterns
      Citation key Koch2016
      Author Koch, R. and Almeida-Cortezb, J. S. and Kleinschmit, B.
      Pages 24-39
      Year 2017
      DOI http://dx.doi.org/10.1016/j.jnc.2016.11.004
      Journal Journal for Nature Conservation
      Volume 35
      Publisher Elsevier
      Abstract The Caatinga biome has been identified as one of the important wilderness areas on earth. However, lessthan 1% of the region is under strictly legal protection although Seasonally Dry Tropical Forests (SDTFs)are globally highly endangered. There is an urgent need to increase the understanding of diversity patternand threaten status of Caatinga plant species to preserve the unique biodiversity and protect endangeredspecies. Species distribution modelling (SDM) can support strategic decisions in nature conservation forpoorly researched tropical regions. This study provides the first highly representative, spatially explicitoverview of plant diversity and threat status for the entire Caatinga, a semi-arid area in Northeast Brazil.For this purpose, we developed (a) a stacked species distribution modelling (S-SDM) approach to pre-dict quantitatively floristic species richness and patterns of threatened plant species and (b) a combinedapproach of diversity hot spots and hubs of threatened species to derive conservation importance units(CIU) to contribute to improved nature reserve management. We applied the modelling technique MaxEntto establish predictive distribution models with 22 uncorrelated predictors including climate, topogra-phy, solar radiation and soil information at a high spatial resolution of 30 arc-seconds (approx. 1 km).Spatial patterns of species richness and threat status were derived by stacking 1062 plant species and 27endangered species, respectively. These outputs were compared to two levels of protected areas (Brazilianand international standards) and intensive human land use patterns to define a set of recommendationsfor conservation management. Our complementary S-SDM approach showed that our predicted CIUscovered an area of 24% across the entire biome, whereas only 7% of the Caatinga is currently protectedbased on the Brazilian standards. We found that apart from an excellent overlap of 38% between CIUsand the current protected areas, a substantial proportion of CIUs (89%) was predicted outside the existingreserve network. Moreover, our findings enable targeted surveys to be done in order to enhance conser-vation efforts and ensure the efficient use of available resources in this poorly studied tropical region.Future upcoming local and regional studies could focus on a multi-taxonomic approach including e.g.insects, reptiles or mammals as a holistic perspective towards biodiversity conservation.
      Link to original publication Download Bibtex entry

      Other Publications

      2019

      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.


      Gras, P., Knuth, S., Börner, K., Marescot, L., Benhaiem, S., Aue, A., Wittstatt, U., Kleinschmit, B. and Kramer-Schadt, S. (2018). Landscape Structures Affect Risk of Canine Distemper in Urban Wildlife. Frontiers in Ecology and Evolution, 1-16.


      2017

      Georgi, C., Spengler, D., Itzerott, S. and Kleinschmit (2017). Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data. Precision Agriculture


      Neumann, C., Itzerott, S., Weiss, G., Kleinschmit, B. and Schmidtlein, S. (2017). Mapping multiple plant species abundance patterns - A multiobjective optimization procedure for combining reflectance spectroscopy and species ordination. Ecological Informatics. Elsevier, 61-76.


      Ayazli, I. E., Kilic, F., Lauf, S., Kleinschmit, B. and Demir, H. (2017). Creating urban growth simulation models driven by the bosphorus bridges. Fresenius Environmental Bulletin, 113-117.


      Moran, N., Nieland, S., Tintrup gen. Suntrup, G. and Kleinschmit, B. (2017). Combining machine learning and ontological data handling for multi-source classification of nature conservation areas. International Journal of Applied Earth Observation and Geoinformation, 124–133.


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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