Prof. Dr. Hanna Meyer

• 2026
• 2025
• 2024
• 2023
• 2022
• 2021
• 2020
• 2019
• 2018
• 2017
• 2016
• 2015
• 2014
• 2013

2026

• Mosig, C, Vajna-Jehle, J, Mahecha, MD, Cheng, Y, Hartmann, H, Montero, D, Junttila, S, Horion, S, Schwenke, MB, Koontz, MJ, Maulud, KNA, Adu-Bredu, S, Al-Halbouni, D, Ali, M, Allen, M, Altman, J, Amorós, L, Angiolini, C, Astrup, R, Awada, H, Barrasso, C, Bartholomeus, H, Beck, PS, Bozzini, A, Braun-Wimmer, J, Brede, B, Breunig, FM, Brugnaro, S, Buras, A, Burchard-Levine, V, Camarero, JJ, Candotti, A, Capuder, L, Carrieri, E, Centritto, M, Chirici, G, Cloutier, M, Conciani, D, Cushman, K, Dalling, JW, Dao, PD, Dempewolf, J, Denter, M, Dogotari, M, Díaz-Delgado, R, Ecke, S, Eichel, J, Eltner, A, Fabbri, A, Fabi, M, Fassnacht, F, Ferreira, MP, Fischer, FJ, Frey, J, Frick, A, Fuentes, J, Ganz, S, Garbarino, M, García, M, Gassilloud, M, Gazol, A, Gea-Izquierdo, G, Gerberding, K, Ghasemi, M, Giannetti, F, Gillan, J, Gonzalez, R, Gosper, C, Greene, T, Greinwald, K, Grieve, S, Große-Stoltenberg, A, Gutierrez, JA, Göritz, A, Hajek, P, Hedding, D, Hempel, J, Heremans, S, Hernández, M, Heurich, M, Honkavaara, E, Höfle, B, Jackisch, R, Jucker, T, Kalwij, JM, Kepfer-Rojas, S, Khatri-Chhetri, P, Kleinebecker, T, Klemmt, H, Klouček, T, Koivumäki, N, Kolagani, N, Komárek, J, Korznikov, K, Kraszewski, B, Kruse, S, Krüger, R, Kuechly, H, Kwong, IH, Laliberté, E, Langan, L, Latifi, H, Leal-Medina, C, Lehmann, JR, Li, L, Lines, E, Lisiewicz, M, Lopatin, J, Lucieer, A, Ludwig, A, Ludwig, M, Lyytikäinen-Saarenmaa, P, Ma, Q, Mansuy, N, Peña, JM, Marino, G, Maroschek, M, Martín, M, Martín-Benito, D, Matham, P, Mazzoni, S, Meloni, F, Menzel, A, Meyer, H, Miraki, M, Moreno, G, Moreno-Fernández, D, Muller-Landau, HC, Mälicke, M, Möhring, J, Müllerova, J, Naidu, SS, Nardi, D, Neumeier, P, Nita, MD, Näsi, R, Oppgenoorth, L, Orunbaev, S, Palmer, M, Paul, T, Pfenning, M, Potts, A, Prasanna, GL, Prober, S, Puliti, S, Pérez-Luque, AJ, Pérez-Priego, O, Reudenbach, C, Revuelto, J, Rivas-Torres, G, Roberge, P, Roggero, PP, Rossi, C, Ruehr, NK, Ruiz-Benito, P, Runge, CM, Satta, GGA, Scanu, B, Scherer-Lorenzen, M, Schiefer, F, Schiller, C, Schladebach, J, Schmehl, M, Schmid, J, Schmidt, TA, Schwarz, S, Seidl, R, Seifert, T, Barba, AS, Shafeian, E, Shapiro, A, {de, Simone} L, Sohrabi, H, Soltani, S, Sotomayor, L, Sparrow, B, Steer, BS, Stenson, M, Stöckigt, B, Su, Y, Suomalainen, J, Tamudo, E, Barbieri, MJT, Tomelleri, E, Torresani, M, Trepekli, K, Ullah, S, Ullah, S, Umlauft, J, Vargas-Ramírez, N, Vatandaslar, C, Visacki, V, Volpi, M, Vásquez, V, Wallis, C, Weinstein, B, Weiser, H, Wich, S, Ximena, TC, Zarco-Tejada, PJ, Zdunic, K, Zielewska-Büttner, K, {de, Oliveira} RA, {van, Wagtendonk} L, {von, Dosky} V, und Kattenborn, T. 2026. „deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics.“ Remote Sensing of Environment 332: 115027–115027. doi: https://doi.org/10.1016/j.rse.2025.115027.

2025

• Baumberger, M, Haas, B, Tewes, W, Risse, B, Meyer, N, und Meyer, H. 2025. „Gated recurrent units for modelling time series of soil temperature and moisture: An assessment of performance and process reflectivity.“ Environmental Modelling and Software 183: 106245–106245. doi: 10.1016/j.envsoft.2024.106245.
• Giese, L, Baumberger, M, Ludwig, M, Schneidereit, H, Sánchez, E, Robroek, BJ, Lamentowicz, M, Lehmann, J, Hölzel, N, Knorr, K, und Meyer, H. 2025. „Recent trends in moisture conditions across European peatlands.“ Remote Sensing Applications: Society and Environment 37: 101385–101385. doi: 10.1016/j.rsase.2024.101385.
• Krämer, Alina, Meyer, Hanna, und Buchholz, Sascha. 2025. „Habitat Suitability of the Sand Lizard (Lacerta agilis) at Its Distribution Limit—An Analysis Based on Citizen Science Data and Machine Learning.“ Journal of Biogeography 52 e15099. doi: 10.1111/jbi.15099.
• Dietenberger, S, Mueller, MM, Stöcker, B, Dubois, C, Arlaud, H, Adam, M, Hese, S, Meyer, H, und Thiel, C. 2025. „Accurate Mapping of Downed Deadwood in a Dense Deciduous Forest Using UAV-SfM Data and Deep Learning.“ Remote Sensing 17 (9). doi: 10.3390/rs17091610.

2024

• Meyer, H, Ludwig, M, Milà, C, Linnenbrink, J, und Schumacher, F. 2024. „The CAST package for training and assessment of spatial prediction models in R.“ Preprint. arXiv doi: 10.48550/arXiv.2404.06978.
• Milà, C, Ludwig, M, Pebesma, E, Tonne, C, und Meyer, H. 2024. „Random forests with spatial proxies for environmental modelling: opportunities and pitfalls.“ Preprint. Geoscientific Model Development 2024 (17): 6007–603. doi: 10.5194/gmd-17-6007-2024.
• Linnenbrink, J, Milà, C, Ludwig, M, und Meyer, H. 2024. „kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation.“ Geoscientific Model Development 17 (15): 5897–5912. doi: 10.5194/gmd-17-5897-2024.
• Baumberger, M, Haas, B, Sivakumar, S, Ludwig, M, Meyer, N, und Meyer, H. 2024. „High-resolution soil temperature and soil moisture patterns in space, depth and time: An interpretable machine learning modelling approach.“ Geoderma 451: 117049–117049. doi: 10.1016/j.geoderma.2024.117049.
• Schumacher, F, Knoth, C, Ludwig, M, und Meyer, H. 2024. „Estimation of local training data point densities to support the assessment of spatial prediction uncertainty.“ Preprint. EGUsphere doi: 10.5194/egusphere-2024-2730.
• Bald, L, Ziegler, A, Gottwald, J, Koch, TL, Ludwig, M, Meyer, H, Wöllauer, S, Zeuss, D, und Frieß, N. 2024. „Leveraging heterogeneous LiDAR data to model successional stages at tree species level in temperate forests.“ Environmental Data Science 3: e24–e24. doi: 10.1017/eds.2024.31.
• Datta, R, Katurji, M, Nielsen, E, Meyer, H, Zawar-Reza, P, und Valdes, ML. 2024. „The Winter Foehn Footprint Across McMurdo Dry Valleys of Antarctica Using a Satellite-Derived Data Set-AntAir v1.0.“ Journal of Geophysical Research: Atmospheres 129 (23): e2023JD039300.. doi: 10.1029/2023JD039300.

2023

• Ludwig, M, Moreno-Martinez, A, Hölzel, N, Pebesma, E, und Meyer, H. 2023. „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, und Nauss, T. 2023. „Using GEDI as training data for an ongoing mapping of landscape-scale dynamics of the plant area index.“ Environmental Research Letters 18 (7). doi: 10.1088/1748-9326/acde8f.
• Nielsen, EB, Katurji, M, Zawar-Reza, P, und Meyer, H. 2023. „Antarctic daily mesoscale air temperature dataset derived from MODIS land and ice surface temperature.“ Scientific data 10 (1): 833–833. doi: 10.1038/s41597-023-02720-z.

2022

• Mila, C, Mateu, J, Pebesma, E, und Meyer, H. 2022. „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, und 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, und Nauss, T. 2022. „Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro.“ Remote Sensing 14 (3): 786. doi: 10.3390/rs14030786.
• Ludwig, M, Bahlmann, J, Pebesma, E, und Meyer, H. 2022. „Developing Transferable Spatial Prediction Models: a Case Study of Satellite Based Landcover Mapping.“ Beitrag präsentiert auf der ISPRS, Nice doi: 10.5194/isprs-archives-XLIII-B3-2022-135-2022.
• Kleinewillinghöfer, L, Olofsson, P, Pebesma, E, Meyer, H, Buck, O, Haub, C, und Eiselt, B. 2022. „Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data.“ Remote Sensing 14 (19): 4903. doi: 10.3390/rs14194903.

2021

• Lezama Valdes, M, Katurji, M, und 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 (22). doi: 10.3390/rs13224673.
• Petermann, E, Meyer, H, Nussbaum, M, und 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, und 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, und Pebesma, E. 2021. „Estimating the Area of Applicability of Remote Sensing-Based Machine Learning Models with Limited Training Data.“ In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS doi: 10.1109/IGARSS47720.2021.9553999.

2020

• Schumacher, B, Katurji, M, Meyer, H, Appelhans, T, Otte, I, und 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, und Meyer, KM. 2020. „PioLaG: a piosphere landscape generator for savanna rangeland modelling.“ Landscape Ecology 35 (9): 2061–2082. doi: 10.1007/s10980-020-01066-w.

2019

• Meyer, H, Schmidt, J, Detsch, F, und 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.
• Lehnert, LW, Meyer, H, Obermeier, WA, Silva, B, Regeling, B, und Bendix, J. 2019. „Hyperspectral Data Analysis in R: The hsdar Package.“ Journal of Statistical Software 89 (12). doi: 10.18637/jss.v089.i12.
• Meyer, H, Reudenbach, C, Wöllauer, S, und 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.
• Ludwig, M, Morgenthal, T, Detsch, F, Higginbottom, TP, Lezama Valdes, M, Nauß, T, und 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.

2018

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

2017

• Messenzehl, K, Meyer, H, Otto, J, Hoffmann, T, und 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, und Nauss, T. 2017. „Satellite-based high-resolution mapping of rainfall over southern Africa.“ Atmospheric Measurement Techniques 10 (6): 2009–2019. doi: 10.5194/amt-10-2009-2017.
• Meyer, H, Lehnert, LW, Wang, Y, Reudenbach, C, Nauss, T, und 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, und Nauss, T. 2017. „Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique.“ Remote Sensing Letters 8 (7): 647–656. doi: 10.1080/2150704X.2017.1312026.

2016

• Lehnert, LW, Meyer, H, und Bendix, J. 2016. 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, und Zawar-Reza, P. 2016. „Mapping Daily Air Temperature for Antarctica Based on MODIS LST.“ Remote Sensing 8 (9). doi: 10.3390/rs8090732.
• Meyer, H, Kühnlein, M, Appelhans, T, und Nauss, T. 2016. „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, und T, Nauss. 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.

2015

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

2014

• Thies, B, Meyer, H, Nauss, T, und Bendix, J. 2014. „Projecting land-use and land-cover changes in a tropical mountain forest of Southern Ecuador.“ Journal of Land Use Science 9 (1): 1–33.
• Lehnert, L, Meyer, H, Meyer, N, Reudenbach, C, und Bendix, J. 2014. „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.

2013

• Windhorst, D, Silva, B, Peters, T, Meyer, H, Thies, B, Bendix, J, Frede, H, und 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, Bd. 221 aus Ecological Studies, herausgegeben von J Bendix, E Beck, A Bräuning, F Makeschin, R Mosandl, S Scheu und W Wilcke. 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, und 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, Bd. 221 aus Ecological Studies, herausgegeben von J Bendix, E Beck, A Bräuning, F Makeschin, R Mosandl, S Scheu und W Wilcke. 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, und 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, Bd. 221 aus Ecological Studies, herausgegeben von J Bendix, E Beck, A Bräuning, F Makeschin, R Mosandl, S Scheu und W Wilcke. Berlin: Springer VDI Verlag. doi: 10.1007/978-3-642-38137-9_2.