Professor Dr. Edzer Pebesma

Professur für Geoinformatik (Prof. Pebesma)
Professor Dr. Edzer Pebesma

Heisenbergstr. 2, Raum 131
48149 Münster

T: +49 251 8333081

  • Forschungsschwerpunkte

    • Geoinformatik
    • Geostatistik
    • Raumzeitlicher Modellierung

  • Vita

    Akademische Ausbildung

    Promotion an der Universtaet Utrecht
    Studium der Fysische Geografie, Universitaet Utrecht

    Beruflicher Werdegang

    Assistent Professor Universiteit Utrecht
    Postdoc an der Universität Amsterdam

    Ruf

    W3 Professur Institut fuer Geoinformatik, WWU
    WWU Münster, Geoinformatik (W3) – angenommen

  • Publikationen

    • , , , , und . . „Random forests with spatial proxies for environmental modelling: opportunities and pitfalls.“ Geoscientific Model Development, Nr. 2024 (17): 6007–603. doi: 10.5194/gmd-17-6007-2024.
    • , und . . „Standardizing Machine Learning APIs for Earth Observation Data Cubes.“ Beitrag präsentiert auf der The 5th Spatial Data Science, Online doi: https://doi.org/10.5281/zenodo.13960237.
    • , , , , und . . „Assessing and improving the transferability of current global spatial prediction models.“ Global Ecology and Biogeography, Nr. 00: 1–13. doi: 10.1111/geb.13635.
    • , , , , , , und . . „Allocation of humanitarian aid after a weather disaster.“ World Development, Nr. 166: 106204. doi: 10.1016/j.worlddev.2023.106204.
    • , , , und . . „Nearest neighbour distance matching leave-one-out cross-validation for map validation.“ Methods in Ecology and Evolution, Nr. 13: 1304–1316. doi: 10.1111/2041-210X.13851.
    • , und . . „Machine learning-based global maps of ecological variables and the challenge of assessing them.“ Nature Communications, Nr. 13 doi: 10.1038/s41467-022-29838-9.
    • , , , und . . „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.
    • , , , , , , und . . „Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data.“ Remote Sensing, Nr. 14 (19): 4903. doi: 10.3390/rs14194903.
    • , , und . . „Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing.“ Fire, Nr. 5 (1) doi: 10.3390/fire5010029.
    • , , und . „Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing.“ Fire, Nr. 5(1) (29) doi: 10.3390/fire5010029.
    • , und . . „Predicting into unknown space? Estimating the area of applicability of spatial prediction models.“ Methods in Ecology and Evolution, Nr. 12: 1620–1633. doi: 10.1111/2041-210X.13650.
    • , und . . „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.
    • , , , , , , , und . . „Patterns in Mongolian nomadic household movement derived from GPS trajectories.“ Applied Geography, Nr. 122 (September 2020): 102270. doi: 10.1016/j.apgeog.2020.102270.
    • , und . . „Spatiotemporal multi-resolution approximations for analyzing global environmental data.“ Spatial Statistics, Nr. 38 doi: 10.1016/j.spasta.2020.100465.
    • , und . . „Practical reproducibility in geography and geosciences.“ Annals of the American Association of Geographers, Nr. 2020 doi: 10.1080/24694452.2020.1806028.
    • , , , und . . „Reproducible Research in Geoinformatics: Concepts, Challenges and Benefits (Vision Paper).“ In 14th International Conference on Spatial Information Theory (COSIT 2019), Bd. 142 aus Leibniz International Proceedings in Informatics (LIPIcs), herausgegeben von S Timpf, C Schlieder, M Kattenbeck, B Ludwig und K Stewart. Wadern: Dagstuhl Publishing. doi: 10.4230/LIPIcs.COSIT.2019.8.
    • , und . . „On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library.“ Data, Nr. 4 (3) doi: 10.3390/data4030092.
    • , , , , , , und . . „Herders Mobility GPS Tracking:Insights From Novel Trajectory Data.“ In Conférence internationale "Systèmes Complexes, Intelligence Territorialeet Mobilité", herausgegeben von P Sajous und C Bertelle.
    • , , , und . . „dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R.“ Journal of Statistical Software, Nr. 2019
    • , , und . . „R vector and raster data cubes for openEO.“ Beitrag präsentiert auf der EGU General Assembly 2018, Vienna, Austria
    • , , , , , , , , , , , , , , und . . „openEO: an open API for cloud-based big Earth Observation processing platforms.“ Beitrag präsentiert auf der EGU General Assembly 2018, Vienna, Austria
    • , , , und . . „Open and scalable analytics of large Earth observation datasets: from scenes to multidimensional arrays using SciDB and GDAL.“ ISPRS Journal of Photogrammetry and Remote Sensing, Nr. 138: 47–56. doi: 10.1016/j.isprsjprs.2018.01.014.
    • , , , und . . „Combining automatic and manual image analysis in a web-mapping application for collaborative conflict damage assessment.“ Applied Geography, Nr. 97: 25–34. doi: 10.1016/j.apgeog.2018.05.016.
    • , , und . . „Multidimensional Arrays for Analysing Geoscientific Data.“ ISPRS International Journal of Geo-Information, Nr. 7 (8) doi: 10.3390/ijgi7080313.
    • , , , , und . „Running user-defined functions in R on Earth observation data in cloud back-ends.“ Beitrag präsentiert auf der 10th Geomundus conference, Lisbon, Portugal
    • , und . . „Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models.“ Sustainability, Nr. 10 (5) doi: 10.3390/su10051442.
    • , , , und . . „Quality of life, big data and the power of statistics.“ Statistics and Probability Letters, Nr. 136 doi: 10.1016/j.spl.2018.02.030.
    • , , und . . „Air quality monitoring network location optimization for robust Land Use Regression Model.“ Beitrag präsentiert auf der Spatial Statistics 2017: One World: One Health, Lancaster Elsevier.
    • , und . „A machine learning approach to demographic prediction using geohashes.“ In Bd. null aus International Workshop on Social Sensing New York, NY: ACM Press. doi: 10.1145/3055601.3055603.
    • , und . „Nurturing a growing field: Computers & Geosciences.“ Computers and Geosciences, Nr. 107 (null): A1–A2. doi: 10.1016/j.cageo.2017.08.006.
    • , und . „The GRASS GIS temporal framework.“ International Journal of Geographical Information Science, Nr. 31 (7): 1273–1292. doi: 10.1080/13658816.2017.1306862.
    • , , und . . „Usability Study to Assess the IGBP Land Cover Classification for Singapore.“ Remote Sensing, Nr. 2017 (9(10), 1075) doi: 10.3390/rs9101075.
    • , , und . . „Modelling spatiotemporal change using multidimensional arrays.“ Beitrag präsentiert auf der EGU General Assembly 2017, Vienna, Austria
    • , , und . . „Reproducible Earth observation analytics: challenges, ideas, and a study case on containerized land use change detection.“ Beitrag präsentiert auf der EGU General Assembly 2017, Vienna, Austria
    • , , , und . . „Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring.“ Remote Sensing, Nr. 9 (10) doi: 10.3390/rs9101025.
    • , , , , , , und . . „Opening reproducible research (o2r).“ Beitrag präsentiert auf der Open Science Conference, Berlin, Germany
    • , , , , , , und . . „Opening the Publication Process with Executable Research Compendia.“ D-Lib Magazine, Nr. 23 doi: 10.1045/january2017-nuest.
    • , , und . „Measurement units in R.“ R Journal, Nr. 8 (2): 490–498.
    • , , und . „Spatio-temporal interpolation using gstat.“ R Journal, Nr. 8 (1): 204–218.
    • , , , , , , , , und . . „Opening Reproducible Research.“ In Bd. 18 aus Geophysical Research Abstracts, herausgegeben von European Geophysical Union.
    • , , , und . „Modeling spatiotemporal information generation.“ International Journal of Geographical Information Science, Nr. null (null): 1–29. doi: 10.1080/13658816.2016.1151520.
    • , und . . „Detecting dwelling destruction in Darfur through object-based change analysis of very high resolution imagery.“ International Journal of Remote Sensing, Nr. 38 (1): 273–295. doi: 10.1080/01431161.2016.1266105.
    • , , , , und . . „Scalable Earth-observation Analytics for Geoscientists: Spacetime Extensions to the Array Database SciDB.“ Beitrag präsentiert auf der EGU General Assembly 2016, Vienna, Austria
    • , und . . „Spatio-temporal change detection from multidimensionalarrays: Detecting deforestation from MODIS time series.“ ISPRS Journal of Photogrammetry and Remote Sensing, Nr. 117 (227-236)
    • , , und . „Spatial and spatio-temporal modeling of meteorological and climatic variables using Open Source software.“ Spatial Statistics, Nr. null (null) doi: 10.1016/j.spasta.2015.06.005.
    • , und . „Optimising sampling designs for the maximum coverage problem of plume detection.“ Spatial Statistics, Nr. 13 (null): 21–44. doi: 10.1016/j.spasta.2015.03.004.
    • , , , , und . „Comparing adaptive and fixed bandwidth-based kernel density estimates in spatial cancer epidemiology.“ International Journal of Health Geographics, Nr. 14 (1) doi: 10.1186/s12942-015-0005-9.
    • , und . . „Spatio-temporal change modeling with array data.“ Beitrag präsentiert auf der EGU 2015, Vienna, Austria
    • , , , und . „Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series.“ ISPRS Journal of Photogrammetry and Remote Sensing, Nr. null (null) doi: 10.1016/j.isprsjprs.2016.03.007.
    • , , , , , und . „Small-area spatio-temporal analyses of participation rates in the mammography screening program in the city of Dortmund (NW Germany) Biostatistics and methods.“ BMC Public Health, Nr. 15 (1) doi: 10.1186/s12889-015-2520-9.
    • , , und . . „Scalable In-Database Regression Analysis of Large Earth-Observation Datasets.“ Beitrag präsentiert auf der EO Open Science 2.0, Frascati, Italy
    • , , , und . . „Software for Spatial Statistics.“ Journal of Statistical Software, Nr. 63 (1)
    • , , , , und . „plotKML: Scientific Visualization of Spatio-Temporal Data.“ Journal of Statistical Software, Nr. 63 (5)
    • , , , , , und . „rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks.“ Computers and Geosciences, Nr. 67: 180–190. doi: 10.1016/j.cageo.2014.02.009.
    • , , und . „Bayesian area-to-point kriging using expert knowledge as informative priors.“ International Journal of Applied Earth Observation and Geoinformation, Nr. 30 (1): 128–138. doi: 10.1016/j.jag.2014.01.019.
    • , , und . „An exploratory approach to spatial decision support.“ Computers, Environment and Urban Systems, Nr. 45 (null): 101–113. doi: 10.1016/j.compenvurbsys.2014.02.008.
    • , und . . „Detecting Destruction in Conflict Areas in Darfur.“ Beitrag präsentiert auf der GEOBIA 2014 - Geographic Object Based Image Analysis, Thessaloniki, Greece
    • , , , , , , und . . „Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution.“ Journal of Geophysical Research: Atmospheres, Nr. na doi: 10.1002/2013JD020803.
    • , und . „A temporal GIS for field based environmental modeling.“ Environmental Modelling and Software, Nr. 53 (null): 1–12. doi: 10.1016/j.envsoft.2013.11.001.
    • , , , und . „Meaningful spatial prediction and aggregation.“ Environmental Modelling and Software, Nr. 51 (null): 149–165. doi: 10.1016/j.envsoft.2013.09.006.
    • , , , , und . „enviroCar – Open car data and open analysis tools for sustainable transportation development.“ Beitrag präsentiert auf der 2nd International Conference ICT for Sustainability, Stockholm, Schweden
    • , und . . „Modeling change from large-scale high-dimensional spatio-temporal array data.“ Beitrag präsentiert auf der EGU 2014, Vienna, Austria
    • , und . . „Using geostatistical simulation to disaggregate air quality model results for individual exposure estimation on GPS tracks.“ Stochastic Environmental Research and Risk Assessment, Nr. 27 (1): 223–234. doi: 10.1007/s00477-012-0578-9.
    • , , , , , , , , und . . „Managing uncertainty in integrated environmental modelling: The UncertWeb framework.“ Environmental Modelling and Software, Nr. 39: 116–134.
    • , , , und . . „Spatio-temporal modelling of individual exposure to air pollution and its uncertainty.“ Atmospheric Environment, Nr. 64: 56–65. doi: 10.1016/j.atmosenv.2012.09.069.
    • , , und . Applied Spatial Data Analysis with R: Second Edition,, herausgegeben von Bivand Roger, Pebesma Edzer und Gomez-Rubio Virgilio. Heidelberg: Springer. doi: 10.1007/978-1-4614-7618-4.
    • , , , , , , und . „Bayesian networks for raster data (BayNeRD): Plausible reasoning from observations.“ Remote Sensing, Nr. 5 (11): 5999–6025. doi: 10.3390/rs5115999.
    • , , , , und . „Measuring allocation errors in land change models in amazonia.“ In Bd. null N/A: Selbstverlag / Eigenverlag.
    • , , , und . „Error-aware spatio-temporal aggregation in the model web.“ In Bd. null aus AGILE Conference on Geographic Information Science Dordrecht: Kluwer Academic. doi: 10.1007/978-3-319-00615-4_12.
    • , , , , , und . „Spatial statistic to assess remote sensing acreage estimates: An analysis of sugarcane in São Paulo State, Brazil.“ In Bd. null doi: 10.1109/IGARSS.2013.6723768.
    • , , , , und . „Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing Methods: A simulation study.“ International Journal of Health Geographics, Nr. null (null): 54. doi: 10.1186/1476-072X-12-54.
    • , und . . „Plume Tracking with a Mobile Sensor Based on Incomplete and Imprecise Information.“ Transactions in GIS, Nr. 2013 doi: 10.1111/tgis.12063.
    • , , , und . . „Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images.“ Theoretical and Applied Climatology, Nr. 107 (1-2): 265–277. doi: 10.1007/s00704-011-0464-2.
    • , , und . . „The R software environment in reproducible geoscientific research.“ Eos, Transactions American Geophysical Union, Nr. 93 (16): 163. doi: 10.1029/2012EO160003.
    • , , , und . . „Usability of Spatio-Temporal Uncertainty Visualisation Methods.“ BRIDGING THE GEOGRAPHIC INFORMATION SCIENCES: 3–23. doi: 10.1007/978-3-642-29063-3_1.
    • , , , und . . „Spatio-temporal aggregation of European air quality observations in the Sensor Web.“ Computers and Geosciences, Nr. 47: 111–118. doi: 10.1016/j.cageo.2011.11.008.
    • , , und . . „Spatial statistics for mapping the environment.“ The ITC Journal
    • , und . „Stationary Sampling Designs Based on Plume Simulations.“ In Spatio-temporal Design: Advances in Efficient Data Acquisition, Bd. null , herausgegeben von Mueller Werner Mateu Jorge. New York City: John Wiley & Sons. doi: 10.1002/9781118441862.ch14.
    • , , , , und . „Uncertainty propagation between web services - A case study using the eHabitat WPS to identify unique ecosystems.“ In Bd. null
    • , , , und . „Tools for uncertainty propagation in the model web using Monte Carlo simulation.“ In Bd. null
    • . . „spacetime: Spatio-Temporal Data in R.“ Journal of Statistical Software, Nr. 51 (7)
    • , und . . „Stationary Sampling Designs Based on Plume Simulations.“ In Spatio-Temporal Design, herausgegeben von Wiley. New York City: John Wiley & Sons. doi: 10.1002/9781118441862.ch14.
    • , , , und . . „Tools for uncertainty propagation in the Model Web using Monte Carlo simulation.“ In Proceedings of the sixth biannial meeting of the International Environmental Modelling and Software Society, herausgegeben von R Seppelt, A Voinov, S Lange und D Bankamp. Leipzig.
    • , , und . . „Spatio-temporal analysis and interpolation of PM10 measurements in Europe.“ online.
    • , , und . . „Connecting R to the Sensor Web.“ ADVANCING GEOINFORMATION SCIENCE FOR A CHANGING WORLD: 227–246. doi: 10.1007/978-3-642-19789-5_12.
    • , , und . . „Procedia Environmental Sciences: Editorial.“ Procedia Environmental Sciences, Nr. 3: 1. doi: 10.1016/j.proenv.2011.02.001.
    • , , , , , , , , , und . . „Planning sensor locations for the detection of radioactive plumes for Norway and the Balkans *.“ Radioprotection, Nr. 46 (6 SUPPL.): S55–S61. doi: 10.1051/radiopro/20116628s.
    • , , und . . „Shifts in western North American snowmelt runoff regimes for the recent warm decades.“ Journal of Hydrometeorology, Nr. 12 (5): 989–1006. doi: 10.1175/2011JHM1360.1.
    • , , , , und . . „A geostatistical approach to data harmonization - Application to radioactivity exposure data.“ International Journal of Applied Earth Observation and Geoinformation, Nr. 13 (3): 409–419. doi: 10.1016/j.jag.2010.09.002.
    • , , , , und . . „Introduction to this special issue on geoinformatics for environmental surveillance.“ Computers and Geosciences, Nr. 37 (3): 277–279. doi: 10.1016/j.cageo.2010.06.002.
    • , , , , , , , , und . . „INTAMAP: The design and implementation of an interoperable automated interpolation web service.“ Computers and Geosciences, Nr. 37 (3): 343–352. doi: 10.1016/j.cageo.2010.03.019.
    • , , , , und . . „Agricultural land use dynamics in the Brazilian Amazon based on remote sensing and census data.“ Applied Geography, Nr. 32 (2): 240–252.
    • , , und . . „Disaggregating gridded air quality data for individual exposure modelling.“ Procedia Environmental Sciences, Nr. 7: 146–151. doi: 10.1016/j.proenv.2011.07.026.
    • , und . . „The pair-copula construction for spatial data: a new approach to model spatial dependency.“ Procedia Environmental Sciences, Nr. 7: 206–211. doi: 10.1016/j.proenv.2011.07.036.
    • , , , , und . „Error aware near real-time interpolation of air quality observations in GEOSS.“ In Bd. 712
    • Schwering, A., Pebesma, E., und Behnke, K., Hrsg. . ifgi prints no 41, Conference Proceedings Geoinformatik 2011, Berlin: Akademische Verlagsgesellschaft.
    • , , , und . . „Comparison of Mapping Methods for Plumes Using Prior Knowledge from Simulations.“ In Proceedings of the Seventh International Symposium on Spatial Data Quality, herausgegeben von Fonte Cidalia C, Goncalves Luisa und Goncalves Gil.
    • , , , , und . . „Planning sensor locations for the detection of radioactive plumes for Norway and the Balkans.“ In Proceedings of the International Conference on Radioecology & Environmental Radioactivity, Bd. 46 (6) aus Radioprotection, herausgegeben von J-C Barescut, D Lariviere und T Stocki. Les Ulis: EDP Sciences. doi: 10.1051/radiopro/20116628s.
    • , und . . „Comparing techniques for vegetation classification using multi- and hyperspectral images and ancillary environmental data.“ International Journal of Remote Sensing, Nr. 31 (23): 6143–6161. doi: 10.1080/01431160903401379.
    • , , , und . . „Using rainfall radar data to improve interpolated maps of dose rate in the Netherlands.“ Science of the Total Environment, Nr. 409 (1): 123–133. doi: 10.1016/j.scitotenv.2010.08.051.
    • , , , und . . „Identifying and removing heterogeneities between monitoring networks.“ Environmetrics, Nr. 21 (1): 66–84.
    • , und . . „Estimating the influence of the neighbourhood in the development of residential areas in the Netherlands.“ Environment and Planning B: Planning and Design, Nr. 37 (1): 21–41.
    • , , , und . „The uncertainty enabled model web (UncertWeb).“ In Bd. 679
    • , und . „Conservative Updating of Sampling Designs.“ In Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, herausgegeben von Nicholas J. Tate und Peter F. Fisher.
    • , und . „Optimizing Spatio-Temporal Sampling Designs of Synchronous, Static, or Clustered Measurements.“ Beitrag präsentiert auf der European Geosciences Union General Assembly, Vienna
    • , , und . „Visualizing uncertainty in spatio-temporal data.“ In Bd. null aus International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences N/A: Selbstverlag / Eigenverlag.
    • , und . „Conservative updating of sampling designs.“ In Bd. null aus International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences N/A: Selbstverlag / Eigenverlag.
    • , , und . . „Visualizing uncertainty in spatio-temporal data.“ In Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, herausgegeben von NJ Tate und PF Fisher.
    • , , , und . . „Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network.“ Computers and Geosciences, Nr. 35 (8): 1711–1721. doi: 10.1016/j.cageo.2008.10.011.
    • , , , , , und . . „Mapping of background air pollution at a fine spatial scale across the European Union.“ Science of the Total Environment, Nr. 407 (6): 1852–1867. doi: 10.1016/j.scitotenv.2008.11.048.
    • . . „How we build geostatistical models and deal with their output.“ In Interfacing Geostatistics and GIS, Bd. null , herausgegeben von Springer. Düsseldorf: Springer VDI Verlag. doi: 10.1007/978-3-540-33236-7_1.
    • , , , , und . „Lessons learned from INTAMAP, an interoperable web service for the real-time interpolation of environmental variables.“ In Bd. null
    • , und . . „Usability of interactive and non-interactive visualisation of uncertain geospatial information.“ In Geoinformatik 2009 Konferenzband, Bd. 35 aus ifgiprints, herausgegeben von W Reinhardt, A Krüger und M Ehlers.
    • , , und . . „Geostatistics for automatic estimation of environmental variables-some simple solutions.“ Georisk, Nr. 2 (4): 257–270.
    • , , und . . „Comparing sampling patterns for kriging the spatial mean temporal trend.“ Journal of Agricultural, Biological, and Environmental Statistics, Nr. 13 (2): 159–176.
    • , , und . . UseR!, Applied Spatial Data Analysis with R, Düsseldorf: Springer VDI Verlag.
    • Pebesma, Edzer, Bishr, Mohammed, und Bartoschek, Thomas, Hrsg. . ifgiPrints, Bd. 32, Proceedings of the 6th Geographic Information Days, 400. Aufl. N/A: unbekannt / n.a. / unknown.
    • , , und . . „The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery.“ Photogrammetric Engineering and Remote Sensing, Nr. 73 (8): 905–912.
    • , , und . . „Interactive visualization of uncertain spatial and spatio-temporal data under different scenarios: An air quality example.“ International Journal of Geographical Information Science, Nr. 21 (5): 515–527. doi: 10.1080/13658810601064009.
    • , , und . . „Error analysis for the evaluation of model performance: Rainfall-runoff event summary variables.“ Hydrological Processes, Nr. 21 (22): 3009–3024. doi: 10.1002/hyp.6529.
    • , , , und . . „Automatic prediction of high-resolution daily rainfall fields for multiple extents: The potential of operational radar.“ Journal of Hydrometeorology, Nr. 8 (6): 1204–1224. doi: 10.1175/2007JHM792.1.
    • , , und . . „Automatic mapping in emergency: A geostatistical perspective.“ International Journal of Emergency Management, Nr. 4 (3): 455–467. doi: 10.1504/IJEM.2007.014297.
    • . . „The role of external variables and GIS databases in geostatistical analysis.“ Transactions in GIS, Nr. 10 (4): 615–632. doi: 10.1111/j.1467-9671.2006.01015.x.
    • , , und . „Dynamic visualisation of spatial and spatio-temporal probability distribution functions.“ In Bd. null aus International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences N/A: Selbstverlag / Eigenverlag.
    • , , und . . „Mapping sea bird densities over the North Sea: Spatially aggregated estimates and temporal changes.“ Environmetrics, Nr. 16 (6): 573–587. doi: 10.1002/env.723.
    • . . „Mapping radioactivity from monitoring data: Automating the classical geostatistical approach.“ Applied GIS, Nr. 1 (2)
    • , , und . . „Error analysis for the evaluation of model performance: Rainfall-runoff event time series data.“ Hydrological Processes, Nr. 19 (8): 1529–1548. doi: 10.1002/hyp.5587.
    • . . „Multivariable geostatistics in S: The gstat package.“ Computers and Geosciences, Nr. 30 (7): 683–691. doi: 10.1016/j.cageo.2004.03.012.
    • , , und . . „Mapping alpine vegetation using vegetation observations and topographic attributes.“ Landscape Ecology, Nr. 18 (8): 759–776. doi: 10.1023/B:LAND.0000014471.78787.d0.
    • , , und . . „Above-ground biomass assessment of Mediterranean forests using airborne imaging spectrometry: The DAIS Peyne experiment.“ International Journal of Remote Sensing, Nr. 24 (7): 1505–1520.
    • , , und . . „Uncertainties in spatially aggregated predictions from a logistic regression model.“ Ecological Modelling, Nr. 154 (1-2): 93–101.
    • , , und . . „Assessment of the prediction error in a large-scale application of a dynamic soil acidification model.“ Stochastic Environmental Research and Risk Assessment, Nr. 16 (4): 279–306.
    • , und . . „Nutrient fluxes at the river basin scale. II: The balance between data availability and model complexity.“ Hydrological Processes, Nr. 15 (5): 761–775.
    • , , , , , und . . „Assessment of uncertainty in simulation of nitrate leaching to aquifers at catchment scale.“ Journal of Hydrology, Nr. 242 (3–4): 210–227. doi: 10.1016/S0022-1694(00)00396-6.
    • , , und . . „Spatio-temporal kriging of soil water content.“ Geostatistics Wollongong 96 - Proceedings of the Fifth International Geostatistics Congress, Wollongong, Australia, September 1996: 1020–1030.
    • , und . . „Spatial aggregation and soil process modelling.“ Geoderma, Nr. 89 (1-2): 47–65. doi: 10.1016/S0016-7061(98)00077-9.
    • , und . . „Latin hypercube sampling of Gaussian random fields.“ Technometrics, Nr. 41 (4): 303–312. doi: 10.1080/00401706.1999.10485930.
    • , , , und . . „Uncertainty assessment in modelling soil acidification at the European scale: A case study.“ Journal of Environmental Quality, Nr. 28 (2): 366–377. doi: 10.2134/jeq1999.00472425002800020002x.
    • , , , , und . . „Quantification and simulation of errors in categorical data for uncertainty analysis of soil acidification modelling.“ Geoderma, Nr. 93 (3-4): 177–194. doi: 10.1016/S0016-7061(99)00056-7.
    • , und . . „Gstat: A program for geostatistical modelling, prediction and simulation.“ Computers and Geosciences, Nr. 24 (1): 17–31. doi: 10.1016/S0098-3004(97)00082-4.
    • , und . . „Mapping groundwater quality in the Netherlands.“ Journal of Hydrology, Nr. 200 (1–4): 364–386. doi: 10.1016/S0022-1694(97)00027-9.

  • Betreute Promotionen

    Infrastructures and Practices for Reproducible Research in Geography, Geosciences, and GIScience
    Supporting Conflict Damage Assessment with Object-Based Image Change Analysis
    Fitness for use of global land cover products to detect land change
    Spatiotemporal Change Modelling from Multidimensional Arrays
    Optimise Spatial Sampling Designs for Plume Monitoring Based on Simulations
    Evaluation of spatial methods for the surveillance of cancer risk using data from a population-based cancer registry
    Developing spatio-temporal copulas
    Spatio-temporal Aggregation in the Sensor Web
    Service Level Agreements in Spatial Data Infrastructures
    Discovery Mechanisms for the Sensor Web
    Spatio-temporal Modelling of Individual Exposure to Particulate Air Pollution