Publications of the Remote Sensing and Spatial Modelling Research Group

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

2023

• Linnenbrink, Jan*; Milà, Carles*; Ludwig, Marvin; Meyer, Hanna. 2023. kNNDM: k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation EGUsphere. doi: 10.5194/egusphere-2023-1308. [submitted / under review]
• 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: https://doi.org/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, No. 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, No. 1: 833. doi: 10.1038/s41597-023-02720-z.

2022

• 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.
• Rommel E, Giese L, Fricke K, Kathöfer F, Heuner M, Mölter T, Deffert P, Asgari M, Näthe P, Dzunic F, Rock G, Bongartz J, Burkart A, Quick I, Schröder U, Baschek B. 2022. ‘Very High-Resolution Imagery and Machine Learning for Detailed Mapping of Riparian Vegetation and Substrate Types.’ Remote Sensing 14, No. 4. doi: 10.3390/rs14040954.
• 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.
• 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, No. 2: 196–202. doi: 10.1111/aec.13113.

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, No. 22. doi: 10.3390/rs13224673.
• Mohr CH, Manga M, Helle G, Heinrich I, Giese L, Korup O. 2021. ‘Trees Talk Tremor—Wood Anatomy and δ13C Content Reveal Contrasting Tree-Growth Responses to Earthquakes.’ Journal of Geophysical Research: Biogeosciences 126, No. 10: e2021JG006385. doi: 10.1029/2021JG006385.
• 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.
• 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, No. 2: 90. doi: 10.1007/s10661-021-08852-2.

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.
• 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, No. 22: 3831. doi: 10.3390/rs12223831.

2019

• 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, No. 12. doi: 10.18637/jss.v089.i12.
• 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.
• 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.
• 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, No. 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, No. 1: 20. doi: 10.3390/drones3010020.
• 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, No. 12: 2163–2172. doi: 10.1111/2041-210X.13294.

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, 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.
• 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, 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.’ 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, No. 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, No. 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, No. 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, No. 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, 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.’ 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. 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; 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. 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; 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. Berlin: Springer VDI Verlag. doi: 10.1007/978-3-642-38137-9_2.