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      • Prof. Dr. Hanna Meyer
  • Prof. Dr. Hanna Meyer
  • Dr. Jan Lehmann
  • Maite Lezama Valdes
  • Marvin Ludwig
  • Maiken Baumberger
  • Laura Giese
  • Henning Schneidereit
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© Hanna Meyer

Prof. Dr. Hanna Meyer

Remote Sensing and Spatial Modelling Research Group
Institute of Landscape Ecology
Heisenbergstr. 2, D-48149 Münster
room 510
phone +49(0)251-83 30 097
fax +49(0)251-83 38 338
e-mail hanna.meyer [at] wwu.de
Github https://github.com/HannaMeyer
Twitter https://twitter.com/hanna123987

office hours on appointment

  • Research Areas

    • Optical remote sensing
    • machine learning for spatial data
    • Environmental monitoring
    • Spatio-temporal modelling
  • CV

    Education

    2014 - 2018
    PhD student at the Philipps Universität Marburg
    2010 - 2013
    M.Sc. "Environmental Geography - Systems, Processes and Interactions" at the Philipps-Universität Marburg
    2007 - 2010
    B.Sc. Geography at the Philipps-Universität Marburg

    Positions

    since 10.​2019
    Full professor for Remote Sensing and Spatial Modelling, Institute of landscape ecology, WWU Münster
    01.​2019 - 10.​2019
    Junior professor for Remote Sensing and Image Interpretation, Institute for Geoinformatics, WWU Münster
    2013 - 2018
    Scientific assistant in the working grouo of environmental Informatics, Philipps- Universität Marburg
    2015 - 2016
    Guest scientist at the University of Canterbury, New Zealand
  • Teaching

    Summer Term 2023

    • Exkursion: Exc.: Remote sensing-based monitoring in the "Harz" National Park [142741]
    • Exkursion: Exc. Nature conservation monitoring through networked sensor technology in the "Marburg Open Forest" [142740]
    • Praktische Übung: P Remote sensing based analysis of environmental change [142708]
      [04.04.2023 - 11.07.2023 | 14:00 - 16:00 | wöchentlich | Di. | StudLab GEO1 130 | Prof. Dr. Hanna Meyer]
    • Übung: Ü Remote sensing methods in landscape ecology A [142693]
      [03.04.2023 - 10.07.2023 | 12:00 - 14:00 | wöchentlich | Mo. | StudLab GEO1 130 | Prof. Dr. Hanna Meyer]
    • Übung: Ü Remote sensing methods in landscape ecology B [142694]
      [03.04.2023 - 10.07.2023 | 14:00 - 16:00 | wöchentlich | Mo. | StudLab GEO1 130 | Prof. Dr. Hanna Meyer]
    • Seminar: S Remote Sensing and Spatial Modeling Forum [142709]
      (in cooperation with Henning Schneidereit)
      [03.04.2023 - 10.07.2023 | 16:00 - 18:00 | wöchentlich | Mo. | GEO1 401 | Prof. Dr. Hanna Meyer]
    • Vorlesung: Einführung in die Fernerkundungsmethoden in den Geowissenschaften - Vorlesung [142955]
      (in cooperation with Dr. Torsten Prinz)
      [04.04.2023 - 11.07.2023 | 08:00 - 10:00 | wöchentlich | Di. | GEO1 Hrsaal | Prof. Dr. Hanna Meyer]

    Winter Term 2022/23

    • Projektveranstaltung: P Remote sensing of ecosystems [140593]
      [n. V. | Prof. Dr. Hanna Meyer]
    • Übung: Ü Spatial data analyses with R [140583]
      (in cooperation with Marvin Ludwig)
      [n. V. | Marvin Ludwig]
    • Praktikum: P Remote sensing and machine learning for spatial monitoring of the environment [140553]
      [14.10.2022 - 03.02.2023 | 10:00 - 12:00 | wöchentlich | Fr. | StudLab GEO1 130 | Prof. Dr. Hanna Meyer]
    • Seminar: S Current topics of environmental remote sensing [140554]
      [11.10.2022 - 31.01.2023 | 14:00 - 16:00 | wöchentlich | Di. | GEO1 513 | Prof. Dr. Hanna Meyer]
    • Seminar: S Humans and the Environment [140549]
      (in cooperation with Henning Schneidereit)
      [10.10.2022 - 30.01.2023 | 10:00 - 12:00 | wöchentlich | Mo. | GEO1 401 | Prof. Dr. Hanna Meyer]
    • Seminar: Remote Sensing and Spatial Modelling Forum [140570]
      (in cooperation with Henning Schneidereit)
      [11.10.2022 - 31.01.2023 | 16:00 - 18:00 | wöchentlich | Di. | Prof. Dr. Hanna Meyer]
    • Vorlesung: V Remote sensing and spatial modelling of the environment [140552]
      [10.10.2022 - 30.01.2023 | 16:00 - 18:00 | wöchentlich | Mo. | StudLab GEO1 130 | Prof. Dr. Hanna Meyer]

    Summer Term 2022

    • Exkursion: Exc. Nature conservation monitoring through networked sensor technology in the "Marburg Open Forest" [148927]
    • Exkursion: Exc.: Remote sensing-based monitoring in the Bavarian Forest National Park [148928]
    • Praktische Übung: P Remote sensing based analysis of environmental change [148890]
    • Übung: Tutorial [148921]
    • Übung: Ü Remote sensing methods in landscape ecology B [148869]
    • Übung: Ü Remote sensing methods in landscape ecology A [148868]
    • Seminar: Ü Acquisition and analysis of UAS-based image data [148897]
    • Seminar: S Humans and the Environment [148886]
      (in cooperation with Henning Schneidereit)
    • Vorlesung: Einführung in die Fernerkundungsmethoden in den Geowissenschaften - Vorlesung [148983]
      (in cooperation with Dr. Torsten Prinz)
  • Projects

    • ReVersal - ERA-Net Cofund BiodivRestore (Joint Call 2020-2021): Restoring peatlands of the nemoral zone under conditions of varying water supply and quality (2022 - 2025)
      EU-project hosted outside WWU: DFG - BiodivERsA (ERA-Net Cofunds) | Project Number: KN 929/26-1; ME 5512/3-1
    • Carbon4D - Carbon4D: A landscape-scale model of soil organic carbon mineralization in space, depth, and time (2021 - 2024)
      Individual project: DFG - Individual Grants Programme | Project Number: ME 5512/2-1
    • Uebersat - Spatio-temporal transferability of satellite-based AI-models (2021 - 2023)
      Individual project: Federal Ministry of Economic Affairs and Climate Action | Project Number: 50EE2009
  • Publications

    • 2023
    • 2022
    • 2021
    • 2020
    • 2019
    • 2018
    • 2017
    • 2016
    • 2015
    • 2014
    • 2013

    2023

    • Ludwig, Marvin; Moreno-Martinez, Alvaro; Hölzel, Norbert; Pebesma, Edzer; Meyer, Hanna. 2023. ‘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.

    2022

    • Mila C, Mateu J, Pebesma E, Meyer H. 2022. ‘Nearest neighbour distance matching leave-one-out cross-validation for map validation.’ Methods in Ecology and Evolution n/a. doi: 10.1111/2041-210X.13851.
    • Meyer H, Pebesma E. 2022. ‘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. 2022. ‘Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro.’ Remote Sensing 14, No. 3: 786. doi: 10.3390/rs14030786.
    • Ludwig M, Bahlmann J, Pebesma E, Meyer H. 2022. ‘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. 2022. ‘Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data.’ Remote Sensing 14, No. 19: 4903. doi: 10.3390/rs14194903.

    2021

    • Petermann E, Meyer H, Nussbaum M, Bossew P. 2021. ‘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. 2021. ‘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. 2021. ‘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.
    • Lezama Valdes M; Katurji M; Meyer H. 2021. ‘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, No. 22. doi: 10.3390/rs13224673.

    2020

    • Schumacher B, Katurji M, Meyer H, Appelhans T, Otte I, Nauss T. 2020. ‘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. 2020. ‘PioLaG: a piosphere landscape generator for savanna rangeland modelling.’ Landscape Ecology 35, No. 9: 2061–2082. doi: 10.1007/s10980-020-01066-w.

    2019

    • Lehnert LW, Meyer H, Obermeier WA, Silva B, Regeling B, Bendix J. 2019. ‘Hyperspectral Data Analysis in R: The hsdar Package.’ Journal of Statistical Software 89, No. 12. doi: 10.18637/jss.v089.i12.
    • Meyer H, Schmidt J, Detsch F, Nauss T. 2019. ‘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.
    • Ludwig M, Morgenthal T, Detsch F, Higginbottom TP, Lezama Valdes M, Nauß T, Meyer H. 2019. ‘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.
    • Meyer H, Reudenbach C, Wöllauer S, Nauss T. 2019. ‘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.

    2018

    • Meyer N, Meyer H, Welp G, Amelung W. 2018. ‘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. 2018. ‘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. 2018. ‘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. 2018. ‘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.
    • Reudenbach C, Meyer H. 2018. uavRst: Unmanned Aerial Vehicle Remote Sensing Tools. R package version 0.5-2..
    • Meyer H, Reudenbach C, Nauss T. 2018. CAST: 'caret' Applications for Spatial-Temporal Models. R package version 0.1.0..

    2017

    • Messenzehl K, Meyer H, Otto J, Hoffmann T, Dikau R. 2017. ‘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. 2017. ‘Satellite-based high-resolution mapping of rainfall over southern Africa.’ Atmospheric Measurement Techniques 10, No. 6: 2009–2019. doi: 10.5194/amt-10-2009-2017.
    • Meyer H, Lehnert LW, Wang Y, Reudenbach C, Nauss T, Bendix J. 2017. ‘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. 2017. ‘Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique.’ Remote Sensing Letters 8, No. 7: 647–656. doi: 10.1080/2150704X.2017.1312026.

    2016

    • Meyer H, Katurji M, Appelhans T, Müller MU, Nauss T, Roudier P, Zawar-Reza P. 2016. ‘Mapping Daily Air Temperature for Antarctica Based on MODIS LST.’ Remote Sensing 8, No. 9. doi: 10.3390/rs8090732.
    • Meyer H, Kühnlein M, Appelhans T, Nauss T. 2016. ‘Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals.’ Atmos. Res. 169, Part B: 424–433. doi: 10.1016/j.atmosres.2015.09.021.
    • Ludwig A, Meyer H, Nauss T. 2016. ‘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.
    • Lehnert L.W, Meyer H, Bendix J. 2016. hsdar: Manage, analyse and simulate hyperspectral data in R. R package version 0.5.1..

    2015

    • Lehnert LW, Meyer H, Wang Y, Miehe G, Thies B, Reudenbach C, Bendix J. 2015. ‘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. 2015. ‘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.
    • Nauss T, Meyer H, Detsch F, Appelhans T. 2015. Manipulating satellite data with satellite. R package version 1.0.0..

    2014

    • Thies B, Meyer H, Nauss T, Bendix J. 2014. ‘Projecting land-use and land-cover changes in a tropical mountain forest of Southern Ecuador.’ Journal of Land Use Science 9, No. 1: 1–33.
    • Lehnert L, Meyer H, Meyer N, Reudenbach C, Bendix J. 2014. ‘A hyperspectral indicator system for rangeland degradation on the Tibetan Plateau: A case study towards spaceborne monitoring.’ Ecol. Indic. 39: 54–64. doi: 10.1016/j.ecolind.2013.12.005.

    2013

    • Windhorst D, Silva B, Peters T, Meyer H, Thies B, Bendix J, Frede H, Breuer L. 2013. ‘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. Spr. 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. 2013. ‘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, 205–218. Springer 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. 2013. ‘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. Springer Verlag. doi: 10.1007/978-3-642-38137-9_2.
  • Talks

    • Meyer, Hanna (2022): ‘Machine learning-based global maps of ecological variables and the challenge of assessing them’. ELLIS Seminars on Machine Learning for Earth and Climate Sciences, Online, 10/05/2022.
    • Meyer, Hanna (2020): ‘Machine learning as a tool to “map the world” ? On remote sensing and predictive modelling for environmental monitoring (Keynote)’. 17th Biodiversity Exploratories Assembly (Biodiversity Exploratories), Wernigerode, Germany, 04/03/2020.
    • Meyer, Hanna (2020): ‘Machine learning applications in environmental remote sensing – Moving from data reproduction to spatial prediction’. Workshop: Machine Learning in Earth system science (Helmholtz Artificial Intelligence Cooperation Unit (HAICU)), German Climate Computing Center (DKRZ) Hamburg, Germany, 03/02/2020.
    • Meyer, Hanna (2019): ‘Remote sensing and machine learning in landscape ecology - Moving from field observations to maps of ecosystem variables’. Kolloquium Ökologie, Naturschutz, Biodiversität, Philipps Universität Marburg, Marburg, Germany, 26/11/2019.
    • Meyer, Hanna (2019): ‘Machine learning applications in environmental remote sensing – Moving from data reproduction to spatial prediction’. The 1st Artificial Intelligence for Copernicus Workshop, ECMWF, Reading, UK, 05/11/2019.
    • Meyer, Hanna (2018): ‘Improving machine-learning strategies for spatial and spatio-temporal environmental data’. Kolloquium Geoinformatik, Friedrich-Schiller-Universität Jena, Jena, Germany, 07/11/2018.
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    University of Münster
    Remote Sensing and Spatial Modelling Research Group

    Heisenbergstraße 2
    D-48149 Münster

    Tel: +49(0)251-83 30 097
    Fax: +49(0)251-83 38 338
    hanna.meyer [at] wwu.de
     
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