Publications of the Remote Sensing and Spatial Modelling Research Group

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

2024

• Meyer H; Ludwig M; Milà C; Linnenbrink J; 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; Meyer H. 2024. ‘Random forests with spatial proxies for environmental modelling: opportunities and pitfalls.’ Geoscientific Model Development 2024, № 17: 6007. doi: 10.5194/gmd-17-6007-2024.
• Linnenbrink J; Milà C; Ludwig M; 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; Tewes W; Risse B; Meyer N; Meyer H. 2024. ‘Gated recurrent units for modelling time series of soil temperature and moisture: An assessment of performance and process reflectivity.’ Environmental Modelling and Software 2024: 106245. doi: 10.1016/j.envsoft.2024.106245.
• Baumberger M; Haas B; Sivakumar S; Ludwig M; Meyer N; 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. 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. 2024. ‘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. 2024 Estimation of local training data point densities to support the assessment of spatial prediction uncertainty EGUsphere. doi: 10.5194/egusphere-2024-2730.

2023

• Ludwig, M; Moreno-Martinez, A; Hölzel, N; Pebesma, E; 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; 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; Meyer, H. 2023. ‘Antarctic daily mesoscale air temperature dataset derived from MODIS land and ice surface temperature.’ Scientific data 10, № 1: 833. doi: 10.1038/s41597-023-02720-z.

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 13: 1304–1316. 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, № 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, № 19: 4903. doi: 10.3390/rs14194903.
• Lehmann; JRK; Prior; MD; Paula; LFA; Azevedo; L; Porembski; S; Buttschardt; TK; Solar R. 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.
• Linnenbrink Jan , Taeger Stefan. 2022. „Habitateignungskarten für den Schutz von Langstreckenziehern unter veränderlichen Klimabedingungen – ein raumzeitlicher Habitatmodellierungs-Ansatz.“ In AGIT- Journal für Angewandte Geoinformatik , 41–50. doi: 10.14627/537728005.

2021

• 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, № 22. doi: 10.3390/rs13224673.
• 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. ‘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, 2021. doi: 10.1109/IGARSS47720.2021.9553999.
• Durán, J; Rodríguez, A; Heiðmarsson, S; Lehmann, JRK; Mora, Á; Garrido-Benavent, I; Ríos, A. 2021. ‘Cryptogamic cover determines soil attributes and functioning in polar terrestrial ecosystems.’ Science of the Total Environment 762: 143169. doi: 10.1016/j.scitotenv.2020.143169.
• Oldeland, J; Revermann, R; Luther-Mosebach, J; Buttschardt, T; Lehmann JRK. 2021. ‘New tools for old problems — comparing drone- and field-based assessments of a problematic plant species.’ Environmental Monitoring and Assessment 193, № 2: 90. doi: 10.1007/s10661-021-08852-2.
• Baumberger, M; Breuer, B; Lai, Y-J; Gabyshev, D; 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.

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, № 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. 2020. ‘Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics.’ Remote Sensing 12, № 22: 3831. doi: 10.3390/rs12223831.

2019

• Lezama Valdes M; Katurji M; Meyer H. ‘Downscaling Land Surface Temperature for the Antarctic Dry Valleys using Multi-Sensor Data and Machine Learning.’ contributed to the EGU General Assembly 2019, Wien, Österreich, 2019.
• 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.
• 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.
• Gottwald, J; Zeidler, R; Friess, N; Ludwig, M; Reudenbach, C; 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; 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. doi: 10.3390/f10030235.
• Teickner, H; Lehmann, JRK; Guth, P; Meinking, F; Ott, D. 2019. ‘Recognize the little ones: Uas-based in-situ fluorescent tracer detection.’ Drones 3, № 1: 20. doi: 10.3390/drones3010020.
• 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.
• 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, № 12. doi: 10.18637/jss.v089.i12.

2018

• 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..
• 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.

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, № 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, № 7: 647–656. doi: 10.1080/2150704X.2017.1312026.
• Lehmann, JRK; Prinz, T; Ziller, SR; Thiele, J; Heringer, G; Meira-Neto, JAA; Buttschardt, TK. 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; 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; 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; 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; 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.
• Lehmann, JRK; Münchberger, W; Knoth, C; Blodau, C; Nieberding, F; Prinz, T; Pancotto, VA; Kleinebecker, T. 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. doi: 10.3390/rs8030173.
• Grabski-Kieron, U; Buttschardt, TK; Foerste, r J; Böhmichen, J; Lehmann, JRK. 2016. Grenzüberschreitender Biotopverbund - Handlungsansätze und Herausforderungen für Planung und Naturschutzpraxis. 1^st Ed. Münster: Landwirtschaftsverlag.

2015

• Nauss, T; Meyer, H; Detsch, F; 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; 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.
• Lehmann, JRK; Nieberding, F; Prinz, T; 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; 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; 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; 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; 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; 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. 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; 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, 205–217. 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; 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. Düsseldorf: Springer VDI Verlag. doi: 10.1007/978-3-642-38137-9_2.