Publikationen
- . The CAST package for training and assessment of spatial prediction models in R arXiv. doi: 10.48550/arXiv.2404.06978.
- . . ‘Random forests with spatial proxies for environmental modelling: opportunities and pitfalls.’ Geoscientific Model Development 2024, Nr. 17: 6007. doi: 10.5194/gmd-17-6007-2024.
- . . ‘kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation.’ Geoscientific Model Development 17, Nr. 15: 5897–5912. doi: 10.5194/gmd-17-5897-2024.
- . . ‘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.
- . . ‘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.
- . . ‘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.
- . Estimation of local training data point densities to support the assessment of spatial prediction uncertainty EGUsphere. doi: 10.5194/egusphere-2024-2730.
- . . ‘Assessing and improving the transferability of current global spatial prediction models.’ Global Ecology and Biogeography 00: 1–13. doi: 10.1111/geb.13635.
- . . ‘Using GEDI as training data for an ongoing mapping of landscape-scale dynamics of the plant area index.’ Environmental Research Letters 18, Nr. 7. doi: 10.1088/1748-9326/acde8f.
- . . ‘Antarctic daily mesoscale air temperature dataset derived from MODIS land and ice surface temperature.’ Scientific data 10, Nr. 1: 833. doi: 10.1038/s41597-023-02720-z.
- . . ‘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.
- . . ‘Machine learning-based global maps of ecological variables and the challenge of assessing them.’ Nature Communications 13. doi: 10.1038/s41467-022-29838-9.
- . . ‘Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro.’ Remote Sensing 14, Nr. 3: 786. doi: 10.3390/rs14030786.
- . . ‘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.
- . . ‘Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data.’ Remote Sensing 14, Nr. 19: 4903. doi: 10.3390/rs14194903.
- . ‘Using drone imagery to upscale estimates of water capacity in tank bromeliads on steep neotropical inselbergs.’ Austral Ecology 47, Nr. 2: 196–202. doi: 10.1111/aec.13113.
- . . „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.
- . . ‘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, Nr. 22. doi: 10.3390/rs13224673.
- . . ‘Mapping the geogenic radon potential for Germany by machine learning.’ Science of the Total Environment 754: 142291. doi: 10.1016/j.scitotenv.2020.142291.
- . . ‘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.
- . „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.
- . ‘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.
- . ‘New tools for old problems — comparing drone- and field-based assessments of a problematic plant species.’ Environmental Monitoring and Assessment 193, Nr. 2: 90. doi: 10.1007/s10661-021-08852-2.
- . . ‘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.
- . . ‘Atmospheric moisture pathways of East Africa and implications for water recycling at Mount Kilimanjaro.’ International Journal of Climatology 2020. doi: 10.1002/joc.6468.
- . . ‘PioLaG: a piosphere landscape generator for savanna rangeland modelling.’ Landscape Ecology 35, Nr. 9: 2061–2082. doi: 10.1007/s10980-020-01066-w.
- . . ‘Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics.’ Remote Sensing 12, Nr. 22: 3831. doi: 10.3390/rs12223831.
- . „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, .
- . . ‘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.
- . . ‘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.
- . . ‘Introduction of an automatic and open‐source radio‐tracking system for small animals.’ Methods in Ecology and Evolution 10, Nr. 12: 2163–2172. doi: 10.1111/2041-210X.13294.
- . ‘Robinia pseudoacacia L. in short rotation coppice: Seed and stump shoot reproduction as well as UAS-based spreading analysis.’ Forests 10, Nr. 3: 235. doi: 10.3390/f10030235.
- . ‘Recognize the little ones: Uas-based in-situ fluorescent tracer detection.’ Drones 3, Nr. 1: 20. doi: 10.3390/drones3010020.
- . . ‘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.
- . . ‘Hyperspectral Data Analysis in R: The hsdar Package.’ Journal of Statistical Software 89, Nr. 12. doi: 10.18637/jss.v089.i12.
- . . uavRst: Unmanned Aerial Vehicle Remote Sensing Tools. R package version 0.5-2..
- . . CAST: 'caret' Applications for Spatial-Temporal Models. R package version 0.1.0..
- . . ‘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.
- . . ‘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.
- . . ‘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.
- . . ‘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.
- . . ‘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.
- . . ‘Satellite-based high-resolution mapping of rainfall over southern Africa.’ Atmospheric Measurement Techniques 10, Nr. 6: 2009–2019. doi: 10.5194/amt-10-2009-2017.
- . . ‘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.
- . . ‘Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique.’ Remote Sensing Letters 8, Nr. 7: 647–656. doi: 10.1080/2150704X.2017.1312026.
- . ‘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.
- . . hsdar: Manage, analyse and simulate hyperspectral data in R. R package version 0.5.1..
- . . ‘Mapping Daily Air Temperature for Antarctica Based on MODIS LST.’ Remote Sensing 8, Nr. 9. doi: 10.3390/rs8090732.
- . . ‘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.
- . . ‘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.
- . ‘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, Nr. 3: 173. doi: 10.3390/rs8030173.
- . Grenzüberschreitender Biotopverbund - Handlungsansätze und Herausforderungen für Planung und Naturschutzpraxis. 1. Aufl. Münster: Landwirtschaftsverlag.
- . . Manipulating satellite data with satellite. R package version 1.0.0..
- . . ‘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.
- . . ‘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.
- . ‘Analysis of unmanned aerial system-based CIR images in forestry-a new perspective to monitor pest infestation levels.’ Forests 6, Nr. 3: 594–612. doi: 10.3390/f6030594.
- . ‘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, Nr. 2: 1225–1241. doi: 10.3390/rs70201225.
- . ‘Making the invisible visible: using UAS-based high-resolution color-infrared imagery to identify buried medieval monastery walls.’ Journal of Unmanned Vehicle Systems 3, Nr. 2: 58–67. doi: 10.1139/juvs-2014-0017.
- . . ‘Projecting land-use and land-cover changes in a tropical mountain forest of Southern Ecuador.’ Journal of Land Use Science 9, Nr. 1: 1–33.
- . . ‘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.
- . . ‘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 , 275–286. Heidelberg: Springer. doi: 10.1007/978-3-642-38137-9_20.
- . . ‘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 , 205–217. Düsseldorf: Springer VDI Verlag. doi: 10.1007/978-3-642-38137-9_15.
- . . ‘Environmental changes affecting the Andes of Ecuador.’ In Ecosystem services, Biodiversity and Environmental Change in a Tropical Mountain Ecosystem of South Ecuador, edited by , 19–29. Düsseldorf: Springer VDI Verlag. doi: 10.1007/978-3-642-38137-9_2.