• Forschungsschwerpunkte

    • Fernerkundung
    • Maschinelles Lernen für räumliche Umweltdaten

  • Vita

    Akademische Ausbildung

    Doktorand, Institut für Landschaftsökologie, Universität Münster
    Doktorand, Geographie - AG Umweltinformatik - Philipps-Universität Marburg
    M.Sc. Physische Geographie, Philipps-Universität Marburg
    B.Sc. Umweltwissenschaften, Universität Koblenz-Landau

    Beruflicher Werdegang

    Wissenschaftlicher Mitarbeiter - Fernerkundung und Räumliche Modellierung, Institut für Landschaftsökologie, WWU Münster
    Wissenschaftlicher Mitarbeiter - Natur 4.0, Philipps-Universität Marburg.

  • Lehre

  • 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.’ Geoscientific Model Development 2024, Nr. 17: 6007. doi: 10.5194/gmd-17-6007-2024.
    • Linnenbrink J; Milà C; Ludwig M; Meyer H. . ‘kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation.’ Geoscientific Model Development 17, Nr. 15: 5897–5912. doi: 10.5194/gmd-17-5897-2024.
    • Baumberger M; Haas B; Sivakumar S; Ludwig M; Meyer N; Meyer H. . ‘High-resolution soil temperature and soil moisture patterns in space, depth and time: An interpretable machine learning modelling approach.’ Geoderma 451: 117049. doi: 10.1016/j.geoderma.2024.117049.
    • Giese L; Baumberger M; Ludwig M; Schneidereit H; Sánchez E; Robroek BJ; Lamentowicz M; Lehmann J; Hölzel N; Knorr K; Meyer H. . ‘Recent trends in moisture conditions across European peatlands.’ Remote Sensing Applications: Society and Environment 2024: 101385. doi: https://doi.org/10.1016/j.rsase.2024.101385.
    • Schumacher F; Knoth C; Ludwig M; Meyer H. Estimation of local training data point densities to support the assessment of spatial prediction uncertainty EGUsphere. doi: 10.5194/egusphere-2024-2730.

    • 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: 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.

    • 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.

    • 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.
    • Ludwig, M; Mestre Runge, C; Friess, N; Koch, TL; Richter, S; Seyfried, S; Wraase, L; Lobo, A; Sebastià, M; Reudenbach, C; Nauss, T. . ‘Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics.’ Remote Sensing 12, Nr. 22: 3831. doi: 10.3390/rs12223831.

    • 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.
    • Gottwald, J; Zeidler, R; Friess, N; Ludwig, M; Reudenbach, C; Nauss, T. . ‘Introduction of an automatic and open‐source radio‐tracking system for small animals.’ Methods in Ecology and Evolution 10, Nr. 12: 2163–2172. doi: 10.1111/2041-210X.13294.