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TU Berlin

Inhalt des Dokuments

Prof. Dr. Birgit Kleinschmit



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


Raum: EB 235a
Sprechstunde: nach Vereinbarung


Ernennung zur Universitätsprofessorin und Leiterin des Fachgebiets Geoinformation in der Umweltplanung an der Technischen Universität Berlin

Juniorprofessorin am Fachgebiet für Geoinformationsverarbeitung in der Umweltplanung an der Technischen Universität Berlin
Softwareentwicklerin bei der INTEND Geoinformatik GmbH in Kassel
Promotion zum Dr. forest an der Universität Göttingen (magna cum laude)

Wissenschaftliche Mitarbeiterin an der Universität Göttingen am Institut für Forsteinrichtung, Ertragskunde und Fernerkundung
Studium der Forstwissenschaften an der Universität Göttingen
in Münster, Westfalen geboren


  • Skalenübergreifende Analyse von Landnutzungsänderungen mit Hilfe von Geographischen Informationssystemen (GIS und Fernerkundung) zum besseren Verständnis des Mensch-Umweltsystems
  • Modellierung von raum-zeitlichen Änderungen der Umwelt und Bewertung der Einflüsse auf Menschen und Ökosysteme 
  • Wissensbasierte Kombination von Geoinformationen und Fernerkundungsdaten
  • Evaluierung neuer Sensortechnologien


Revealing areas of high nature conservation importance in aseasonally dry tropical forest in Brazil: Combination of modelled plantdiversity hot spots and threat patterns
Zitatschlüssel Koch2016
Autor Koch, R. and Almeida-Cortezb, J. S. and Kleinschmit, B.
Seiten 24-39
Jahr 2017
DOI http://dx.doi.org/10.1016/j.jnc.2016.11.004
Journal Journal for Nature Conservation
Jahrgang 35
Verlag Elsevier
Zusammenfassung 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 zur Originalpublikation Download Bibtex Eintrag

Weitere Publikationen


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


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.


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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Fachgebiet Geoinformation in der Umweltplanung
Sekretariat EB5
Raum EB 236a
Straße des 17. Juni 145
D - 10623 Berlin
Tel.: +49 (0)30 314 - 73 29 0
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