Publications of the Remote Sensing and Spatial Modelling Research Group

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

2025

• Baumberger, M, Haas, B, Tewes, W, Risse, B, Meyer, N, and 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, and Meyer, H. et al. 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, A, Meyer, H, and Buchholz, S. 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, № n/a (n/a) doi: 10.1111/jbi.15099.
• Dietenberger, S, Mueller, MM, Stöcker, B, Dubois, C, Arlaud, H, Adam, M, Hese, S, Meyer, H, and Thiel, C. et al. 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.
• Pernat, Nadja, Buchholz, Sascha, Kriegs, Jan Ole, Steen, Jan, and Hollens-Kuhr, Hilke. 2025. “Moving north under the eye of the public: the dispersal ecology of the Nosferatu spider, documented by citizen scientists.” Basic and Applied Ecology, № 84: 40–49. doi: 10.1016/j.baae.2025.02.002.

2024

• Meyer, H, Ludwig, M, Milà, C, Linnenbrink, J, and Schumacher, F. 2024. “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, and Meyer, H. 2024. “Random forests with spatial proxies for environmental modelling: opportunities and pitfalls.” Geoscientific Model Development, № 2024 (17): 6007–603. doi: 10.5194/gmd-17-6007-2024.
• Linnenbrink, J, Milà, C, Ludwig, M, and 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, and 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, and Meyer, H. 2024. “Estimation of local training data point densities to support the assessment of spatial prediction uncertainty.” EGUsphere doi: 10.5194/egusphere-2024-2730.
• Bald, L, Ziegler, A, Gottwald, J, Koch, TL, Ludwig, M, Meyer, H, Wöllauer, S, Zeuss, D, and Frieß, N. et al. 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.
• Maxwell, Tania L., Spalding, Mark D., Friess, Daniel A., Murray, Nicholas J., Rogers, Kerrylee, Rovai, Andre S., Smart, Lindsey S., Weilguny, Lukas, Adame, Maria Fernanda, Adams, Janine B., Austin, William E. N., Copertino, Margareth S., Cott, Grace M., Duarte De Paula Costa, Micheli, Holmquist, James R., Ladd, Cai J. T., Lovelock, Catherine E., Ludwig, Marvin, Moritsch, Monica M., Navarro, Alejandro, Raw, Jacqueline L., Ruiz-Fernández, Ana-Carolina, Serrano, Oscar, Smeaton, Craig, Van De Broek, Marijn, Windham-Myers, Lisamarie, Landis, Emily, and Worthington, Thomas A. et al. 2024. “Soil carbon in the world’s tidal marshes.” Nature Communications, № 15 10265. doi: 10.1038/s41467-024-54572-9.
• Datta, R, Katurji, M, Nielsen, E, Meyer, H, Zawar-Reza, P, and 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.
• Karwinkel, Thiemo, Winklhofer, Michael, Allenstein, Dario, Brust, Vera, Christoph, Paula, Holland, Richard, Hüppop, Ommo, Steen, Jan, Franz, Bairlein, and Schmaljohann, Heiko. et al. 2024. “A refined magnetic pulse treatment method for magnetic navigation experiments with adequate sham control: a case study on free-flying songbirds.” Journal of the Royal Society Interface, № 21 (214) doi: 10.1098/rsif.2023.0745.

2023

• Ludwig, M, Moreno-Martinez, A, Hölzel, N, Pebesma, E, and 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, and 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, and 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

• Linnenbrink, Jan, and Taeger, Stefan. 2022. “Habitateignungskarten für den Schutz von Langstreckenziehern unter veränderlichen Klimabedingungen – ein raumzeitlicher Habitatmodellierungs-Ansatz.” in AGIT- Journal für Angewandte Geoinformatik doi: 10.14627/537728005.
• Mila, C, Mateu, J, Pebesma, E, and 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, and 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, and Nauss, T. et al. 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, and Meyer, H. 2022. “Developing Transferable Spatial Prediction Models: a Case Study of Satellite Based Landcover Mapping.” contribution 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, and Eiselt, B. et al. 2022. “Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data.” Remote Sensing, № 14 (19): 4903. doi: 10.3390/rs14194903.
• Lehmann, JRK, Prior, MD, Paula, LFA, Azevedo, L, Porembski, S, Buttschardt, TK, and R., Solar. et al. 2022. “Using drone imagery to upscale estimates of water capacity in tank bromeliads on steep neotropical inselbergs.” Austral Ecology, № 47 (2): 196–202. doi: 10.1111/aec.13113.

2021

• Lezama Valdes, M, Katurji, M, and 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.
• Baumberger, M, Breuer, B, Lai, Y-J, Gabyshev, D, and Klemm, O. 2021. “Bidirectional Turbulent Fluxes of Fog at a Subtropical Montane Cloud Forest Covering a Wide Size Range of Droplets.” Boundary-Layer Meteorology, № 2021 doi: 10.1007/s10546-021-00654-w.
• Petermann, E, Meyer, H, Nussbaum, M, and 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, and 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, and 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.
• Durán, J, Rodríguez, A, Heiðmarsson, S, Lehmann, JRK, Mora, Á, Garrido-Benavent, I, and Ríos, A. et al. 2021. “Cryptogamic cover determines soil attributes and functioning in polar terrestrial ecosystems.” Science of the Total Environment, № 762: 143169–143169. doi: 10.1016/j.scitotenv.2020.143169.
• Oldeland, J, Revermann, R, Luther-Mosebach, J, Buttschardt, T, and JRK., Lehmann. 2021. “New tools for old problems — comparing drone- and field-based assessments of a problematic plant species.” Environmental Monitoring and Assessment, № 193 (2): 90–90. doi: 10.1007/s10661-021-08852-2.

2020

• Schumacher, B, Katurji, M, Meyer, H, Appelhans, T, Otte, I, and 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, and Meyer, KM. et al. 2020. “PioLaG: a piosphere landscape generator for savanna rangeland modelling.” Landscape Ecology, № 35 (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, and Nauss, T. et al. 2020. “Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics.” Remote Sensing, № 12 (22): 3831. doi: 10.3390/rs12223831.

2019

• Meyer, H, Schmidt, J, Detsch, F, and 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, and Bendix, J. 2019. “Hyperspectral Data Analysis in R: The hsdar Package.” Journal of Statistical Software, № 89 (12) doi: 10.18637/jss.v089.i12.
• Lezama, Valdes M, Katurji, M, and Meyer, H. 2019. “Downscaling Land Surface Temperature for the Antarctic Dry Valleys using Multi-Sensor Data and Machine Learning.” in Geophysical Research Abstracts
• Meyer, H, Reudenbach, C, Wöllauer, S, and 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, and Meyer, H. et al. 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.
• Gottwald, J, Zeidler, R, Friess, N, Ludwig, M, Reudenbach, C, and Nauss, T. 2019. “Introduction of an automatic and open‐source radio‐tracking system for small animals.” Methods in Ecology and Evolution, № 10 (12): 2163–2172. doi: 10.1111/2041-210X.13294.
• Carl, C, Lehmann, JRK, Landgraf, D, and Pretzsch, H. 2019. “Robinia pseudoacacia L. in short rotation coppice: Seed and stump shoot reproduction as well as UAS-based spreading analysis.” Forests, № 10 (3): 235–235. doi: 10.3390/f10030235.
• Teickner, H, Lehmann, JRK, Guth, P, Meinking, F, and Ott, D. 2019. “Recognize the little ones: Uas-based in-situ fluorescent tracer detection.” Drones, № 3 (1): 20–20. doi: 10.3390/drones3010020.

2018

• Reudenbach, C, and Meyer, H. 2018. uavRst: Unmanned Aerial Vehicle Remote Sensing Tools. R package version 0.5-2.,
• Meyer, H, Reudenbach, C, and Nauss, T. 2018. CAST: 'caret' Applications for Spatial-Temporal Models. R package version 0.1.0.,
• Meyer, N, Meyer, H, Welp, G, and 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, and 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, and Wesche, K. et al. 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, and 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, and 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, and 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, and 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, and 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.
• Lehmann, JRK, Prinz, T, Ziller, SR, Thiele, J, Heringer, G, Meira-Neto, JAA, and Buttschardt, TK. et al. 2017. “Open-source processing and analysis of aerial imagery acquired with a low-cost Unmanned Aerial System to support invasive plant management.” Frontiers in Environmental Science, № 5 doi: 10.3389/fenvs.2017.00044.

2016

• Lehnert, LW, Meyer, H, and 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, and Zawar-Reza, P. et al. 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, and 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, and 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.
• Lehmann, JRK, Münchberger, W, Knoth, C, Blodau, C, Nieberding, F, Prinz, T, Pancotto, VA, and Kleinebecker, T. et al. 2016. “High-resolution classification of south patagonian peat bog microforms reveals potential gaps in up-scaled CH4 fluxes by use of Unmanned Aerial System (UAS) and CIR imagery.” Remote Sensing, № 8 (3): 173–173. doi: 10.3390/rs8030173.
• Grabski-Kieron, U, Buttschardt, TK, Foerste, r J, Böhmichen, J, and Lehmann, JRK. 2016. Naturschutz und Biologische Vielfalt, Grenzüberschreitender Biotopverbund - Handlungsansätze und Herausforderungen für Planung und Naturschutzpraxis. Münster: Landwirtschaftsverlag.

2015

• Nauss, T, Meyer, H, Detsch, F, and 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, and Bendix, J. et al. 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, and 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.
• Lehmann, JRK, Nieberding, F, Prinz, T, and Knoth, C. 2015. “Analysis of unmanned aerial system-based CIR images in forestry-a new perspective to monitor pest infestation levels.” Forests, № 6 (3): 594–612. doi: 10.3390/f6030594.
• Lehmann, JRK, Große-Stoltenberg, A, Römer, M, and Oldeland, J. 2015. “Field spectroscopy in the VNIR-SWIR region to discriminate between mediterranean native plants and exotic-invasive shrubs based on leaf tannin content.” Remote Sensing, № 7 (2): 1225–1241. doi: 10.3390/rs70201225.
• Lehmann, JRK, Smithson, KZ, and Prinz, T. 2015. “Making the invisible visible: using UAS-based high-resolution color-infrared imagery to identify buried medieval monastery walls.” Journal of Unmanned Vehicle Systems, № 3 (2): 58–67. doi: 10.1139/juvs-2014-0017.

2014

• Thies, B, Meyer, H, Nauss, T, and 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, and 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, and Breuer, L. et al. 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, Vol. 221 of Ecological Studies, edited by J Bendix, E Beck, A Bräuning, F Makeschin, R Mosandl, S Scheu and W Wilcke et al.. Heidelberg: 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, and Beck, E. et al. 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, Vol. 221 of Ecological Studies, edited by J Bendix, E Beck, A Bräuning, F Makeschin, R Mosandl, S Scheu and W Wilcke et al.. Düsseldorf: 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, and Bendix, J. et al. 2013. “Environmental changes affecting the Andes of Ecuador.” in Ecosystem services, Biodiversity and Environmental Change in a Tropical Mountain Ecosystem of South Ecuador, Vol. 221 of Ecological Studies, edited by J Bendix, E Beck, A Bräuning, F Makeschin, R Mosandl, S Scheu and W Wilcke et al.. Düsseldorf: Springer VDI Verlag. doi: 10.1007/978-3-642-38137-9_2.