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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. [8].
Ecological Informatics. Elsevier, 1-10.
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 [9]. 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 [10].
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 [11]. Earth System Science
Data, 2289-2309.
Fenske, K., Feilhauer, H., Förster, M.,
Stellmes, M. and Waske, B. (2020). Hierarchical classification with
subsequent aggregation of heathland habitats using an intra-annual
RapidEye time-series [12]. International Journal of Applied Earth
Observation and Geoinformation, 1-13.
Döpper, V., Gränzig, T., Kleinschmit, B. and
Förster, M. (2020). Challenges in UAS-Based TIR Imagery Processing:
Image Alignment and Uncertainty Quantification. [13]. remote
sensing, 1-22.
2019
Kattenborn, T., Lopatina, J., Förster, M.,
Braun, A. C. and Fassnacht, F. E. (2019). UAV data as alternative to
field sampling to map woody invasive species based on combined
Sentinel-1 and Sentinel-2 data [14]. Remote Sensing of
Environment, 61-73.
Vallentin, C., Dobers, E. S., Itzerott, S.,
Kleinschmit, B. and Spengler, D. (2019). Delineation of management
zones with spatial data fusion and belief theory [15]. Precision
Agriculture. Springer, 1-29.
Schulz, C. and Kleinschmit, B. (2019).
Zentralasiatische Tugai-Auwälder – Ein gefährdetes Ökosystem
[16]. 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 [17]. 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 [18].
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 [19].
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
[20]. 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 [21]. Frontiers in Ecology and
Evolution, 1-16.
2017
Koch, R., Almeida-Cortezb, J. S. and
Kleinschmit, B. (2017). Revealing areas of high nature conservation
importance in aseasonally dry tropical forest in Brazil: Combination
of modelled plantdiversity hot spots and threat patterns [22].
Journal for Nature Conservation. Elsevier, 24-39.
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