Prof.Dr. Hanna Meyer

Prof.Dr. Hanna Meyer

Heisenbergstraße 2, Raum 240
48149 Münster

T: +49 251 83-30097

Akademisches Profil

  • Forschungsschwerpunkte

    • Maschinelle Lernverfahren für räumliche Daten
    • Optische Fernerkundung
    • Umweltmonitoring
    • Raum-zeitliche Modellierung
  • Weitere Zugehörigkeit an der Universität Münster

  • Vita

    Akademische Ausbildung

    Doktorandin an der Philipps Universität Marburg
    M.Sc. "Environmental Geography - Systems, Processes and Interactions" an der Philipps-Universität Marburg
    B.Sc. Geographie an der Philipps-Universität Marburg

    Beruflicher Werdegang

    Professorin für Fernerkundung und Räumliche Modellierung, Institut für Landschaftsökologie, WWU Münster
    Juniorprofessorin für Remote Sensing and Image Interpretation, Institut für Geoinformatik, WWU Münster
    Wissenschaftliche Mitarbeiterin in der AG Umweltinformatik, Philipps- Universität Marburg
    Gastwissenschaftlerin an der University of Canterbury, Neuseeland
  • Lehre

     

    Vorlesung
    Seminar
    Praktikum
    Übung
    Kolloquium
    Sonstige Lehrveranstaltung

    Vorlesung
    Übungen
    Exkursion
    Kolloquium

    Vorlesung
    Seminar
    Praktikum
    Übung
    Kolloquium
    Sonstige Lehrveranstaltung

    Vorlesung
    Seminare
    Übungen
    Exkursionen

    Vorlesung
    Seminare
    Praktikum
    Übung
    Sonstige Lehrveranstaltung
  • Projekte

    • Quantifying and modelling peat breathing with satellite radar data ()
      Durch die Universität Münster intern gefördertes Projekt: Universität Münster-interne Förderung - Collaboration Grant for Young Researchers
    • PRISM – Preservation and RecognItion of Spatial patterns using Machine learning ()
      EU-Projekt koordiniert an der Universität Münster: EU Horizon Europe - Marie Skłodowska-Curie Actions - Postdoctoral Fellowship | Förderkennzeichen: 101147446
    • BEyond – SPP 1374: Biodiversitäts-Exploratorien - TP: Lernen aus den Exploratorien für Vorhersagen jenseits deren Grenzen: Kl-gestützte Modellierung und Erklärung von Biodiversitätsmustern und Ökosystemenfunktionen im Grünland ganzer Naturräume ()
      Teilprojekt in DFG-Verbund koordiniert außerhalb der Universität Münster: DFG - Schwerpunktprogramm | Förderkennzeichen: HO 3830/13-1; ME 5512/4-1
    • ReVersal – ERA-Net Cofund BiodivRestore (Joint Call 2020-2021): Renaturierung von Mooren der nemoralen Zone unter Bedingungen variabler Wasserverfügbarkeit und -qualität ()
      EU-Projekt koordiniert außerhalb der Universität Münster: DFG - BiodivERsA (ERA-Net Cofunds) | Förderkennzeichen: KN 929/26-1; ME 5512/3-1
    • Carbon4D – Carbon4D: Ein landschaftsskaliges Modell der Mineralisation organischen Bodenkohlenstoffs in Raum, Tiefe und Zeit ()
      Gefördertes Einzelprojekt: DFG - Sachbeihilfe/Einzelförderung | Förderkennzeichen: ME 5512/2-1
    • Uebersat – Raum-zeitliche Übertragbarkeit satellitenbasierter KI-Modelle ()
      Gefördertes Einzelprojekt: Bundesministerium für Wirtschaft und Klimaschutz | Förderkennzeichen: 50EE2009
  • Publikationen

    • Meyer H; Ludwig M; Milà C; Linnenbrink J; Schumacher F. . The CAST package for training and assessment of spatial prediction models in R arXiv. doi: 10.48550/arXiv.2404.06978.
    • Milà C; Ludwig M; Pebesma E; Tonne C; Meyer H. . Random forests with spatial proxies for environmental modelling: opportunities and pitfalls EGUsphere. doi: 10.5194/egusphere-2024-138. [submitted / under review]
    • Linnenbrink, J*; Milà, C*; Ludwig, M; Meyer, H. . kNNDM: k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation EGUsphere. doi: 10.5194/egusphere-2023-1308. [submitted / under review]
    • Ludwig, M; Moreno-Martinez, A; Hölzel, N; Pebesma, E; Meyer, H. . ‘Assessing and improving the transferability of current global spatial prediction models.’ Global Ecology and Biogeography 00: 1–13. doi: https://doi.org/10.1111/geb.13635.
    • Ziegler, A; Heisig, J; Ludwig, M; Reudenbach, C; Meyer, H; Nauss, T. . ‘Using GEDI as training data for an ongoing mapping of landscape-scale dynamics of the plant area index.’ Environmental Research Letters 18, Nr. 7. doi: 10.1088/1748-9326/acde8f.
    • Nielsen, EB; Katurji, M; Zawar-Reza, P; Meyer, H. . ‘Antarctic daily mesoscale air temperature dataset derived from MODIS land and ice surface temperature.’ Scientific data 10, Nr. 1: 833. doi: 10.1038/s41597-023-02720-z.
    • Mila, C; Mateu, J; Pebesma, E; Meyer, H. . ‘Nearest neighbour distance matching leave-one-out cross-validation for map validation.’ Methods in Ecology and Evolution 13: 1304–1316. doi: 10.1111/2041-210X.13851.
    • Meyer, H; Pebesma, E. . ‘Machine learning-based global maps of ecological variables and the challenge of assessing them.’ Nature Communications 13. doi: 10.1038/s41467-022-29838-9.
    • Ziegler, A; Meyer, H; Otte, I; Peters, MK; Appelhans, T; Behler, C; Böhning-Gaese, K; Classen, A; Detsch, F; Deckert, J; Eardley, CD; Ferger, SW; Fischer, M; Gebert, F; Haas, M; Helbig-Bonitz, M; Hemp, A; Hemp, C; Kakengi, V; Mayr, AV; Ngereza, C; Reudenbach, C; Röder, J; Rutten, G; Schellenberger Costa, D; Schleuning, M; Ssymank, A; Steffan-Dewenter, I; Tardanico, J; Tschapka, M; Vollstädt, MGR; Wöllauer, S; Zhang, J; Brandl, R; Nauss, T. . ‘Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro.’ Remote Sensing 14, Nr. 3: 786. doi: 10.3390/rs14030786.
    • Ludwig, M; Bahlmann, J; Pebesma, E; Meyer, H. . ‘Developing Transferable Spatial Prediction Models: a Case Study of Satellite Based Landcover Mapping.’ Contributed to the ISPRS, Nice. doi: 10.5194/isprs-archives-XLIII-B3-2022-135-2022.
    • Kleinewillinghöfer, L; Olofsson, P; Pebesma, E; Meyer, H; Buck, O; Haub, C; Eiselt, B. . ‘Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data.’ Remote Sensing 14, Nr. 19: 4903. doi: 10.3390/rs14194903.
    • Lezama Valdes, M; Katurji, M; Meyer, H. . ‘A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data.’ Remote Sensing 13, Nr. 22. doi: 10.3390/rs13224673.
    • Petermann, E; Meyer, H; Nussbaum, M; Bossew, P. . ‘Mapping the geogenic radon potential for Germany by machine learning.’ Science of the Total Environment 754: 142291. doi: 10.1016/j.scitotenv.2020.142291.
    • Meyer, H; Pebesma, E. . ‘Predicting into unknown space? Estimating the area of applicability of spatial prediction models.’ Methods in Ecology and Evolution 12: 1620–1633. doi: 10.1111/2041-210X.13650.
    • Meyer, H; Pebesma, E.Estimating the Area of Applicability of Remote Sensing-Based Machine Learning Models with Limited Training Data.“ contributed to the IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, . doi: 10.1109/IGARSS47720.2021.9553999.
    • Schumacher, B; Katurji, M; Meyer, H; Appelhans, T; Otte, I; Nauss, T. . ‘Atmospheric moisture pathways of East Africa and implications for water recycling at Mount Kilimanjaro.’ International Journal of Climatology 2020. doi: 10.1002/joc.6468.
    • Hess, B; Dreber, N; Liu, Y; Wiegand, K; Ludwig, M; Meyer, H; Meyer, KM. . ‘PioLaG: a piosphere landscape generator for savanna rangeland modelling.’ Landscape Ecology 35, Nr. 9: 2061–2082. doi: 10.1007/s10980-020-01066-w.
    • Meyer, H; Schmidt, J; Detsch, F; Nauss, T. . ‘Hourly gridded air temperatures of South Africa derived from MSG SEVIRI.’ International Journal of Applied Earth Observation and Geoinformation 78: 261–267. doi: 10.1016/j.jag.2019.02.006.
    • Lehnert, LW; Meyer, H; Obermeier, WA; Silva, B; Regeling, B; Bendix, J. . ‘Hyperspectral Data Analysis in R: The hsdar Package.’ Journal of Statistical Software 89, Nr. 12. doi: 10.18637/jss.v089.i12.
    • Meyer, H; Reudenbach, C; Wöllauer, S; Nauss, T. . ‘Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction.’ Ecological Modelling 411: 108815. doi: 10.1016/j.ecolmodel.2019.108815.
    • Ludwig, M; Morgenthal, T; Detsch, F; Higginbottom, TP; Lezama Valdes, M; Nauß, T; Meyer, H. . ‘Machine learning and multi-sensor based modelling of woody vegetation in the Molopo Area, South Africa.’ Remote Sensing of Environment 222: 195–203. doi: 10.1016/j.rse.2018.12.019.
    • Reudenbach, C; Meyer, H. . uavRst: Unmanned Aerial Vehicle Remote Sensing Tools. R package version 0.5-2..
    • Meyer, H; Reudenbach, C; Nauss, T. . CAST: 'caret' Applications for Spatial-Temporal Models. R package version 0.1.0..
    • Meyer, N; Meyer, H; Welp, G; Amelung, W. . ‘Soil respiration and its temperature sensitivity (Q10): Rapid acquisition using mid-infrared spectroscopy.’ Geoderma 323: 31–40. doi: 10.1016/j.geoderma.2018.02.031.
    • Higginbottom, TP; Symeonakis, E; Meyer, H; Linden, S. . ‘Mapping fractional woody cover in semi-arid savannahs using multi-seasonal composites from Landsat data.’ ISPRS Journal of Photogrammetry and Remote Sensing 139: 88–102. doi: 10.1016/j.isprsjprs.2018.02.010.
    • Wang, Y; Lehnert, LW; Holzapfel, M; Schultz, R; Heberling, G; Görzen, E; Meyer, H; Seeber, E; Pinkert, S; Ritz, M; Fu, Y; Ansorge, H; Bendix, J; Seifert, B; Miehe, G; Long, R; Yang, Y; Wesche, K. . ‘Multiple indicators yield diverging results on grazing degradation and climate controls across Tibetan pastures.’ Ecological Indicators 93: 1199–1208. doi: 10.1016/j.ecolind.2018.06.021.
    • Meyer, H; Reudenbach, C; Hengl, T; Katurji, M; Nauss, T. . ‘Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation.’ Environmental Modelling and Software 101: 1–9. doi: 10.1016/j.envsoft.2017.12.001.
    • Messenzehl, K; Meyer, H; Otto, J; Hoffmann, T; Dikau, R. . ‘Regional-scale controls on the spatial activity of rockfalls (Turtmann Valley, Swiss Alps) — A multivariate modeling approach.’ Geomorphology 287: 29–45. doi: 10.1016/j.geomorph.2016.01.008.
    • Meyer, H; Drönner, J; Nauss, T. . ‘Satellite-based high-resolution mapping of rainfall over southern Africa.’ Atmospheric Measurement Techniques 10, Nr. 6: 2009–2019. doi: 10.5194/amt-10-2009-2017.
    • Meyer, H; Lehnert, LW; Wang, Y; Reudenbach, C; Nauss, T; Bendix, J. . ‘From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information?International Journal of Applied Earth Observation and Geoinformation 55: 21–31. doi: 10.1016/j.jag.2016.10.001.
    • Meyer, H; Kühnlein, M; Reudenbach, C; Nauss, T. . ‘Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique.’ Remote Sensing Letters 8, Nr. 7: 647–656. doi: 10.1080/2150704X.2017.1312026.
    • Lehnert, LW; Meyer, H; Bendix, J. . hsdar: Manage, analyse and simulate hyperspectral data in R. R package version 0.5.1..
    • Meyer, H; Katurji, M; Appelhans, T; Müller, MU; Nauss, T; Roudier, P; Zawar-Reza, P. . ‘Mapping Daily Air Temperature for Antarctica Based on MODIS LST.’ Remote Sensing 8, Nr. 9. doi: 10.3390/rs8090732.
    • Meyer, H; Kühnlein, M; Appelhans, T; Nauss, T. . ‘Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals.’ Atmospheric Research 169, Part B: 424–433. doi: 10.1016/j.atmosres.2015.09.021.
    • Ludwig, A; Meyer, H; Nauss T. . ‘Automatic classification of Google Earth images for a larger scale monitoring of bush encroachment in South Africa.’ International Journal of Applied Earth Observation and Geoinformation 50: 89–94. doi: 10.1016/j.jag.2016.03.003.
    • Nauss, T; Meyer, H; Detsch, F; Appelhans, T. . Manipulating satellite data with satellite. R package version 1.0.0..
    • Lehnert, LW; Meyer, H; Wang, Y; Miehe, G; Thies, B; Reudenbach, C; Bendix, J. . ‘Retrieval of grassland plant coverage on the Tibetan Plateau based on a multi-scale, multi-sensor and multi-method approach.’ Remote Sensing of Environment 164: 197–207. doi: 10.1016/j.rse.2015.04.020.
    • Gasch, CK; Hengl, T; Gräler, B; Meyer, H; Magney, TS; Brown, DJ. . ‘Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: The Cook Agronomy Farm data set.’ Spatial Statistics 14, Part A: 70–90.
    • Thies, B; Meyer, H; Nauss, T; Bendix, J. . ‘Projecting land-use and land-cover changes in a tropical mountain forest of Southern Ecuador.’ Journal of Land Use Science 9, Nr. 1: 1–33.
    • Lehnert, L; Meyer, H; Meyer, N; Reudenbach, C; Bendix, J. . ‘A hyperspectral indicator system for rangeland degradation on the Tibetan Plateau: A case study towards spaceborne monitoring.’ Ecological Indicators 39: 54–64. doi: 10.1016/j.ecolind.2013.12.005.
    • Windhorst, D; Silva, B; Peters, T; Meyer, H; Thies, B; Bendix, J; Frede, H; Breuer, L. . ‘Impacts of local land-use change on climate and hydrology.’ In Ecosystem services, Biodiversity and Environmental Change in a Tropical Mountain Ecosystem of South Ecuador, edited by Bendix, J; Beck, E; Bräuning, A; Makeschin, F; Mosandl, R; Scheu, S; Wilcke, W, 275–286. Berlin: Springer. doi: 10.1007/978-3-642-38137-9_20.
    • Roos, K; Bendix, J; Curatola, G; Gawlik, J; Gerique, A; Hamer, U; Hildebrandt, P; Knoke, T; Meyer, H; Pohle, P; Potthast, K; Thies, B; Tischer, A; Beck, E. . ‘Current provisioning services: pasture development and use, weeds (bracken) and management.’ In Ecosystem services, Biodiversity and Environmental Change in a Tropical Mountain Ecosystem of South Ecuador, edited by Bendix, J; Beck, E; Bräuning, A; Makeschin, F; Mosandl, R; Scheu, S; Wilcke, W, 205–217. Berlin: Springer VDI Verlag. doi: 10.1007/978-3-642-38137-9_15.
    • Peters, T; Drobnik, T; Meyer, H; Rankl, M; Richter, M; Rollenbeck, R; Thies, B; Bendix, J. . ‘Environmental changes affecting the Andes of Ecuador.’ In Ecosystem services, Biodiversity and Environmental Change in a Tropical Mountain Ecosystem of South Ecuador, edited by Bendix, J; Beck, E; Bräuning, A; Makeschin, F; Mosandl, R; Scheu, S; Wilcke, W, 19–29. Berlin: Springer VDI Verlag. doi: 10.1007/978-3-642-38137-9_2.