Dr. Michael Förster

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, 61, pp. 1-10. doi: https://doi.org/10.1016/j.ecoinf.2020.101195 [9]

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 [10]. Earth System Science Data, 12, pp. 2289-2309. doi: https://doi.org/10.5194/essd-12-2289-2020 [11]

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, 87, pp. 1-13. doi: https://doi.org/10.1016/j.jag.2019.102036 [13]

Döpper, V., Gränzig, T., Kleinschmit, B. and Förster, M. (2020): Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification. [14]. remote sensing, 12(1552), pp. 1-22. doi: https://doi.org/10.3390/rs12101552 [15]

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 [16]. Remote Sensing of Environment, 227(2019), pp. 61-73.

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, 2018, pp. 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, 2018, pp. 71–84.

Möller, M., Gerstmann, H., Gao, F., Dahms, T. C. and Förster, M. (2017): Coupling of phenological information and simulated vegetation index time series: Limitations and potentials for the assessment and monitoring of soil erosion risk [19]. Catena, 170, pp. 192-205. doi: 10.1016/j.catena.2016.11.016 [20]

Schmidt, J., Fassnacht, F. E., Neff, C., Lausch, A., Kleinschmit, B., Förster, M. and Schmidtlein, S. (2017): Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status [21]. International Journal of Applied Earth Observation and Geoinformation, 60, pp. 61-71. doi: http://dx.doi.org/10.1016/j.jag.2017.04.005 [22]

Gärtner, P., Förster, M. and Kleinschmit, B. (2016): The benefit of synthetically generated RapidEye and Landsat 8 data fusion time series for riparian forest disturbance monitoring [23]. Remote Sensing of Environment, 2016(177), pp. 237-247. doi: doi:10.1016/j.rse.2016.01.028 [24]