Research Focus on Digital Remote Sensing Methods

Leidemer, T., Gonroudobou, O.B.H., Nguyen, H.T., Ferracini, C., Burkhard, B., Diez, Y., Lopez Caceres, M.L. (2022): Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images,  Computation 10, 63.
DOI: https://doi.org/10.3390/computation10040063

Kentsch, S., Cabezas, M., Tomhave, L., Groß, J., Burkhard, B., Caceres, M.L.L., Waki, K., Diez, Y. (2021): Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning,  Sensors, 21: 471
DOI: https://doi.org/10.3390/s21020471

Kreklow, J. (2019): Facilitating radar precipitation data processing, assessment and analysis: A GIS-compatible python approach.,  Journal of Hydroinformatics 21 (4): 652-670
DOI: 10.2166/hydro.2019.048

Kreklow, J.; Tetzlaff, B.; Kuhnt, G.; Burkhard, B. (2019): A rainfall data inter-comparison dataset for Germany (Version 1.0)
DOI: 10.5281/zenodo.3262172

Neteler, M. (2010): Spatio-temporal reconstruction of satellite-based temperature maps and their application to the prediction of tick and mosquito disease vector distribution in Northern Italy,  Diss. Univ. Hannover, 145 S.

Meer, U. (2006): Methoden zur Beurteilung der Heterogenität und Disaggregierungsverfahren zur Verbesserung des Aussagegehaltes von Bodenbasisdaten,  Diss. Univ. Hannover. 145 S.

Kumpula, T., B. Burkhard & F. Müller (2005): Can you see it all in the image? Using remote sensing techniques for landscape assessment in northern Fennoscandia,  EcoSys 11. S. 13-23.