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TU Berlin

Inhalt des Dokuments

Zeitschriftenbeiträge

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.


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.


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. 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.. 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. 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. 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.


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

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. Journal for Nature Conservation. Elsevier, 24-39.


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