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


    WWU Münster, Geoinformatik (W3) – angenommen
  • Publikationen

    • Milà C; Ludwig M; Pebesma E; Tonne C; Meyer H. . Random forests with spatial proxies for environmental modelling: opportunities and pitfalls EGUsphere. doi: 10.5194/egusphere-2024-138. [submitted / under review]
    • Ludwig, M; Moreno-Martinez, A; Hölzel, N; Pebesma, E; Meyer, H. . ‘Assessing and improving the transferability of current global spatial prediction models.’ Global Ecology and Biogeography 00: 1–13. doi:
    • Mogge L; McDonald M; Knoth C; Teickner H; Purevtseren M; Pebesma E; Kraehnert K. . ‘Allocation of humanitarian aid after a weather disaster.’ World Development 166: 106204. doi:
    • Heisig J., Olson E., Pebesma E. . ‘Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing.’ Fire 5(1), Nr. 29. doi: 10.3390/fire5010029.
    • Mila, C; Mateu, J; Pebesma, E; Meyer, H. . ‘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. . ‘Machine learning-based global maps of ecological variables and the challenge of assessing them.’ Nature Communications 13. doi: 10.1038/s41467-022-29838-9.
    • Ludwig, M; Bahlmann, J; Pebesma, E; Meyer, H. . ‘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. . ‘Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data.’ Remote Sensing 14, Nr. 19: 4903. doi: 10.3390/rs14194903.
    • Meyer, H; Pebesma, E. . ‘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, . doi: 10.1109/IGARSS47720.2021.9553999.
    • Nüst Daniel, Pebesma Edzer. . ‘Practical reproducibility in geography and geosciences.’ Annals of the American Association of Geographers 2020. doi: 10.1080/24694452.2020.1806028.
    • Teickner H, Knoth C, Bartoschek T, Kraehnert K, Vigh M, Purevtseren M, Sugar M, Pebesma E. . ‘Patterns in Mongolian nomadic household movement derived from GPS trajectories.’ Applied Geography 122, Nr. September 2020: 102270. doi: 10.1016/j.apgeog.2020.102270.
    • Appel Marius, Pebesma Edzer. . ‘Spatiotemporal multi-resolution approximations for analyzing global environmental data.’ Spatial Statistics 38. doi: 10.1016/j.spasta.2020.100465.
    • Maus V, Camara G, Appel M, Pebesma E. . ‘dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R.’ Journal of Statistical Software 2019.
    • Kray C, Pebesma E, Konkol M, Nüst D. . ‘Reproducible Research in Geoinformatics: Concepts, Challenges and Benefits (Vision Paper).’ In 14th International Conference on Spatial Information Theory (COSIT 2019), edited by Timpf S, Schlieder C, Kattenbeck M, Ludwig B, Stewart K, 8:1–8:13. Dagstuhl, Germany: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. doi: 10.4230/LIPIcs.COSIT.2019.8.
    • Appel M, Pebesma E. . ‘On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library.’ Data 4, Nr. 3. doi: 10.3390/data4030092.
    • Kraehnert K, Vigh M, Knoth C, Teickner H, Purevtseren M, Sugar M, Pebesma E.Herders Mobility GPS Tracking:Insights From Novel Trajectory Data.“ contributed to the XTerM 2019, Le Havre, France, .
    • Gupta Shivam, Pebesma Edzer, Mateu Jorge, Degbelo Auriol. . ‘Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models.’ Sustainability 10, Nr. 5. doi: 10.3390/su10051442.
    • Gupta Shivam, Mateu Jorge, Degbelo Auriol, Pebesma Edzer. . ‘Quality of life, big data and the power of statistics.’ Statistics and Probability Letters 136. doi: 10.1016/j.spl.2018.02.030.
    • Pebesma E, Appel M, Lahn F.R vector and raster data cubes for openEO.“ contributed to the EGU General Assembly 2018, Vienna, Austria, .
    • Pebesma E, Wagner W, Soille P, Kadunc M, Gorelick N, Schramm M, Verbesselt J, Reiche J, Appel M, Dries J, Jacob A, Neteler M, Gebbert S, Briese C, Kempeneers P.openEO: an open API for cloud-based big Earth Observation processing platforms.“ contributed to the EGU General Assembly 2018, Vienna, Austria, .
    • Appel Marius, Lahn Florian, Buytaert Wouter, Pebesma Edzer. . ‘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 138: 47–56. doi: 10.1016/j.isprsjprs.2018.01.014.
    • Knoth C, Slimani S, Appel M, Pebesma E. . ‘Combining automatic and manual image analysis in a web-mapping application for collaborative conflict damage assessment.’ Applied Geography 97: 25–34. doi: 10.1016/j.apgeog.2018.05.016.
    • Lu M, Appel M, Pebesma E. . ‘Multidimensional Arrays for Analysing Geoscientific Data.’ ISPRS International Journal of Geo-Information 7, Nr. 8. doi: 10.3390/ijgi7080313.
    • Ghosh, P., Lahn, F., Gebbert, S., Mohr M., Pebesma, E. . ‘Running user-defined functions in R on Earth observation data in cloud back-ends.’ Contributed to the 10th Geomundus conference, Lisbon, Portugal.
    • Lu Meng, Hamunyela Eliakim, Verbesselt Jan, Pebesma Edzer. . ‘Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring.’ Remote Sensing 9, Nr. 10. doi: 10.3390/rs9101025.
    • Konkol Markus, Nüst Daniel, Schutzeichel Marc, Pebesma Edzer, Kray Christian, Przibytzin Holger, Lorenz Jörg.Opening reproducible research (o2r).“ contributed to the Open Science Conference, Berlin, Germany, .
    • Nüst D, Konkol M, Pebesma E, Kray C, Schutzeichel M, Przibytzin, H, Lorenz, J. . ‘Opening the Publication Process with Executable Research Compendia.’ D-Lib Magazine 23. doi: 10.1045/january2017-nuest.
    • Roy A., Pebesma E. . ‘A machine learning approach to demographic prediction using geohashes.’ Contributed to the 2nd International Workshop on Social Sensing, SocialSens 2017, usa. doi: 10.1145/3055601.3055603.
    • Mariethoz G., Pebesma E. . ‘Nurturing a growing field: Computers & Geosciences.’ Computers and Geosciences 107, Nr. null: A1–A2. doi: 10.1016/j.cageo.2017.08.006.
    • Gebbert S., Pebesma E. . ‘The GRASS GIS temporal framework.’ International Journal of Geographical Information Science 31, Nr. 7: 1273–1292. doi: 10.1080/13658816.2017.1306862.
    • Sidhu Nanki, Pebesma Edzer, Wang Yi-Chen. . ‘Usability Study to Assess the IGBP Land Cover Classification for Singapore.’ Remote Sensing 2017, Nr. 9(10), 1075. doi: 10.3390/rs9101075.
    • Lu Meng, Appel Marius, Pebesma Edzer.Modelling spatiotemporal change using multidimensional arrays.“ contributed to the EGU General Assembly 2017, Vienna, Austria, .
    • Appel Marius, Nüst Daniel, Pebesma Edzer.Reproducible Earth observation analytics: challenges, ideas, and a study case on containerized land use change detection.“ contributed to the EGU General Assembly 2017, Vienna, Austria, .
    • Shivam gupta, Edzer Pebesma, Jorge Mateu.Air quality monitoring network location optimization for robust Land Use Regression Model.“ contributed to the Spatial Statistics 2017: One World: One Health, Lancaster, .
    • Knoth C, Pebesma E. . ‘Detecting dwelling destruction in Darfur through object-based change analysis of very high resolution imagery.’ International Journal of Remote Sensing 38, Nr. 1: 273–295. doi: 10.1080/01431161.2016.1266105.
    • Appel Marius, Lahn Florian, Pebesma Edzer, Buytaert Wouter, Moulds Simon.Scalable Earth-observation Analytics for Geoscientists: Spacetime Extensions to the Array Database SciDB.“ contributed to the EGU General Assembly 2016, Vienna, Austria, .
    • Lu Meng, Pebesma Edzer, Sanshaz Alber, Verbesselt Jan. . ‘Spatio-temporal change detection from multidimensionalarrays: Detecting deforestation from MODIS time series.’ ISPRS Journal of Photogrammetry and Remote Sensing 117, Nr. 227-236.
    • Pebesma E., Mailund T., Hiebert J. . ‘Measurement units in R.’ R Journal 8, Nr. 2: 490–498.
    • Gräler B., Pebesma E., Heuvelink G. . ‘Spatio-temporal interpolation using gstat.’ R Journal 8, Nr. 1: 204–218.
    • Nüst Daniel, Konkol Markus, Pebesma Edzer, Kray Christian, Klötgen Stephanie, Schutzeichel Marc, Lorenz Jörg, Przibytzin Holger, Kussmann Dirk.Opening Reproducible Research.“ contributed to the EGU, Vienna, .
    • Scheider S., Gräler B., Pebesma E., Stasch C. . ‘Modeling spatiotemporal information generation.’ International Journal of Geographical Information Science null, Nr. null: 1–29. doi: 10.1080/13658816.2016.1151520. [online first]
    • Appel Marius, Pebesma Edzer, Camara Gilberto.Scalable In-Database Regression Analysis of Large Earth-Observation Datasets.“ contributed to the EO Open Science 2.0, Frascati, Italy, .
    • Pebesma, E., R. Bivand, P.J. Ribeiro. . ‘Software for Spatial Statistics.’ Journal of Statistical Software 63, Nr. 1.
    • Hengl, T., P. Roudier, D. Beaudette, E. Pebesma. . ‘plotKML: Scientific Visualization of Spatio-Temporal Data.’ Journal of Statistical Software 63, Nr. 5.
    • Hengl T., Pebesma E., Hijmans R. . ‘Spatial and spatio-temporal modeling of meteorological and climatic variables using Open Source software.’ Spatial Statistics null, Nr. null. doi: 10.1016/j.spasta.2015.06.005. [online first]
    • Helle K., Pebesma E. . ‘Optimising sampling designs for the maximum coverage problem of plume detection.’ Spatial Statistics 13, Nr. null: 21–44. doi: 10.1016/j.spasta.2015.03.004.
    • Lemke D., Mattauch V., Heidinger O., Pebesma E., Hense H. . ‘Comparing adaptive and fixed bandwidth-based kernel density estimates in spatial cancer epidemiology.’ International Journal of Health Geographics 14, Nr. 1. doi: 10.1186/s12942-015-0005-9.
    • Lu Meng, Pebesma Edzer.Spatio-temporal change modeling with array data.“ contributed to the EGU 2015, Vienna, Austria, .
    • Lu M., Pebesma E., Sanchez A., Verbesselt J. . ‘Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series.’ ISPRS Journal of Photogrammetry and Remote Sensing null, Nr. null. doi: 10.1016/j.isprsjprs.2016.03.007. [online first]
    • Lemke D., Berkemeyer S., Mattauch V., Heidinger O., Pebesma E., Hense H. . ‘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 15, Nr. 1. doi: 10.1186/s12889-015-2520-9.
    • Skoien J.O., Bloschl G., Laaha G., Pebesma E., Parajka J., Viglione A. . ‘rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks.’ Computers and Geosciences 67: 180–190. doi: 10.1016/j.cageo.2014.02.009.
    • Truong P.N., Heuvelink G.B.M., Pebesma E. . ‘Bayesian area-to-point kriging using expert knowledge as informative priors.’ International Journal of Applied Earth Observation and Geoinformation 30, Nr. 1: 128–138. doi: 10.1016/j.jag.2014.01.019.
    • Jankowski P., Fraley G., Pebesma E. . ‘An exploratory approach to spatial decision support.’ Computers, Environment and Urban Systems 45, Nr. null: 101–113. doi: 10.1016/j.compenvurbsys.2014.02.008.
    • Knoth C, Pebesma E. . ‘Detecting Destruction in Conflict Areas in Darfur.’ Contributed to the GEOBIA 2014 - Geographic Object Based Image Analysis, Thessaloniki, Greece.
    • Kilibarda M, Hengl T, Heuvelink GBM, Gräler B, Pebesma E, Perčec Tadić M, Bajat B. . ‘Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution.’ Journal of Geophysical Research: Atmospheres na. doi: 10.1002/2013JD020803.
    • Gebbert S., Pebesma E. . ‘A temporal GIS for field based environmental modeling.’ Environmental Modelling and Software 53, Nr. null: 1–12. doi: 10.1016/j.envsoft.2013.11.001.
    • Stasch C., Scheider S., Pebesma E., Kuhn W. . ‘Meaningful spatial prediction and aggregation.’ Environmental Modelling and Software 51, Nr. null: 149–165. doi: 10.1016/j.envsoft.2013.09.006.
    • Stasch, C., Nüst, D., Rieke, M., Remke, A., Pebesma E. . ‘enviroCar – Open car data and open analysis tools for sustainable transportation development.’ Contributed to the 2nd International Conference ICT for Sustainability, Stockholm, Schweden.
    • Lu Meng, Pebesma Edzer.Modeling change from large-scale high-dimensional spatio-temporal array data.“ contributed to the EGU 2014, Vienna, Austria, .
    • Gerharz LE, Pebesma E. . ‘Using geostatistical simulation to disaggregate air quality model results for individual exposure estimation on GPS tracks.’ Stochastic Environmental Research and Risk Assessment 27, Nr. 1: 223–234. doi: 10.1007/s00477-012-0578-9.
    • Bastin L, Cornford D, Jones R, Heuvelink GBM, Pebesma E, Stasch C, Nativi S, Mazzetti P, Williams M. . ‘Managing uncertainty in integrated environmental modelling: The UncertWeb framework.’ Environmental Modelling and Software 39: 116–134.
    • Gerharz LE, Klemm O, Broich AV, Pebesma E. . ‘Spatio-temporal modelling of individual exposure to air pollution and its uncertainty.’ Atmospheric Environment 64: 56–65. doi: 10.1016/j.atmosenv.2012.09.069.
    • Brink Juliane, Pebesma Edzer. . ‘Plume Tracking with a Mobile Sensor Based on Incomplete and Imprecise Information.’ Transactions in GIS 2013. doi: 10.1111/tgis.12063.
    • Bivand Roger, Pebesma Edzer, Gomez-Rubio Virgilio (Eds.): . Applied Spatial Data Analysis with R: Second Edition. : Springer. doi: 10.1007/978-1-4614-7618-4.
    • Mello M., Risso J., Atzberger C., Aplin P., Pebesma E., Vieira C., Rudorff B. . ‘Bayesian networks for raster data (BayNeRD): Plausible reasoning from observations.’ Remote Sensing 5, Nr. 11: 5999–6025. doi: 10.3390/rs5115999.
    • Diniz L., Buurman M., Andrade P., Camara G., Pebesma E. . ‘Measuring allocation errors in land change models in amazonia.’ Contributed to the 14th Brazilian Symposium on GeoInformatics, GEOINFO 2013, Campos do Jordao, SP, bra.
    • Stasch C., Pebesma E., Graeler B., Gerharz L. . ‘Error-aware spatio-temporal aggregation in the model web.’ Contributed to the 16th AGILE Conference on Geographic Information Science, bel. doi: 10.1007/978-3-319-00615-4_12.
    • Mello M.P., Aguiar D.A., Rudorff B.F.T., Pebesma E., Jones J., Santos N.C.P. . ‘Spatial statistic to assess remote sensing acreage estimates: An analysis of sugarcane in São Paulo State, Brazil.’ Contributed to the 2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013, Melbourne, VIC, aus. doi: 10.1109/IGARSS.2013.6723768.
    • Lemke D., Mattauch V., Heidinger O., Pebesma E., Hense H.-W. . ‘Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing Methods: A simulation study.’ International Journal of Health Geographics null, Nr. null: 54. doi: 10.1186/1476-072X-12-54.
    • Hengl T, Heuvelink GBM, Tadić MP, Pebesma EJ. . ‘Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images.’ Theoretical and Applied Climatology 107, Nr. 1-2: 265–277. doi: 10.1007/s00704-011-0464-2.
    • Pebesma E, Nüst D, Bivand R. . ‘The R software environment in reproducible geoscientific research.’ Eos, Transactions American Geophysical Union 93, Nr. 16: 163. doi: 10.1029/2012EO160003.
    • Senaratne H, Gerharz L, Pebesma E, Schwering A. . ‘Usability of Spatio-Temporal Uncertainty Visualisation Methods.’ BRIDGING THE GEOGRAPHIC INFORMATION SCIENCES : 3–23. doi: 10.1007/978-3-642-29063-3_1.
    • Stasch C, Foerster T, Autermann C, Pebesma E. . ‘Spatio-temporal aggregation of European air quality observations in the Sensor Web.’ Computers and Geosciences 47: 111–118. doi: 10.1016/j.cageo.2011.11.008.
    • Heuvelink GBM, Pebesma E, Stein A. . ‘Spatial statistics for mapping the environment.’ The ITC Journal .
    • Gräler Benedikt, Gerharz Lydia, Pebesma Edzer. . Spatio-temporal analysis and interpolation of PM10 measurements in Europe . online, .
    • Helle K., Pebesma E. . ‘Stationary Sampling Designs Based on Plume Simulations.’ In Spatio-temporal Design: Advances in Efficient Data Acquisition, edited by Mateu Jorge, Mueller Werner, 319–344. John Wiley & Sons. doi: 10.1002/9781118441862.ch14.
    • Schulz M., Skøien J., Gerharz L., Dubois G., Pebesma E. . ‘Uncertainty propagation between web services - A case study using the eHabitat WPS to identify unique ecosystems.’ Contributed to the 6th Biennial Meeting of the International Environmental Modelling and Software Society: Managing Resources of a Limited Planet, iEMSs 2012, Leipzig, deu.
    • Pross B., Gerharz L., Stasch C., Pebesma E. . ‘Tools for uncertainty propagation in the model web using Monte Carlo simulation.’ Contributed to the 6th Biennial Meeting of the International Environmental Modelling and Software Society: Managing Resources of a Limited Planet, iEMSs 2012, Leipzig, deu.
    • Pebesma E. . ‘spacetime: Spatio-Temporal Data in R.’ Journal of Statistical Software 51, Nr. 7.
    • Helle Kristina, Pebesma Edzer. . ‘Stationary Sampling Designs Based on Plume Simulations.’ In Spatio-Temporal Design, edited by Wiley, 319–344. John Wiley & Sons. doi: 10.1002/9781118441862.ch14.
    • Pross Benjamin, Gerharz Lydia, Stasch Christoph, Pebesma Edzer. . ‘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, edited by Seppelt R, Voinov A, Lange S, Bankamp D. Leipzig.
    • Nust D, Stasch C, Pebesma E. . ‘Connecting R to the Sensor Web.’ ADVANCING GEOINFORMATION SCIENCE FOR A CHANGING WORLD : 227–246. doi: 10.1007/978-3-642-19789-5_12.
    • Stein A, Pebesma E, Heuvelink G. . ‘Procedia Environmental Sciences: Editorial.’ Procedia Environmental Sciences 3: 1. doi: 10.1016/j.proenv.2011.02.001.
    • Helle KB, Urso L, Astrup P, Mikkelsen T, Kaiser JC, Pebesma E, Rojas-Palma C, Holo E, Dyve JE, Raskob W. . ‘Planning sensor locations for the detection of radioactive plumes for Norway and the Balkans *.’ Radioprotection 46, Nr. 6 SUPPL.: S55–S61. doi: 10.1051/radiopro/20116628s.
    • Fritze H, Stewart IT, Pebesma E. . ‘Shifts in western North American snowmelt runoff regimes for the recent warm decades.’ Journal of Hydrometeorology 12, Nr. 5: 989–1006. doi: 10.1175/2011JHM1360.1.
    • Baume O, Skøien JO, Heuvelink GBM, Pebesma EJ, Melles SJ. . ‘A geostatistical approach to data harmonization - Application to radioactivity exposure data.’ International Journal of Applied Earth Observation and Geoinformation 13, Nr. 3: 409–419. doi: 10.1016/j.jag.2010.09.002.
    • Dubois G, Cornford D, Hristopulos D, Pebesma E, Pilz J. . ‘Introduction to this special issue on geoinformatics for environmental surveillance.’ Computers and Geosciences 37, Nr. 3: 277–279. doi: 10.1016/j.cageo.2010.06.002.
    • Pebesma E, Cornford D, Dubois G, Heuvelink GBM, Hristopulos D, Pilz J, Stöhlker U, Morin G, Skøien JO. . ‘INTAMAP: The design and implementation of an interoperable automated interpolation web service.’ Computers and Geosciences 37, Nr. 3: 343–352. doi: 10.1016/j.cageo.2010.03.019.
    • De Espindola GM, De Aguiar APD, Pebesma E, Câmara G, Fonseca L. . ‘Agricultural land use dynamics in the Brazilian Amazon based on remote sensing and census data.’ Applied Geography 32, Nr. 2: 240–252.
    • Fairgrieve S., Stasch C., Falke S., Gerharz L., Pebesma E. . ‘Error aware near real-time interpolation of air quality observations in GEOSS.’ Contributed to the Workshop on Integrating Sensor Web and Web-Based Geoprocessing, ISW 2011 - At AGILE 2011 Conference, Utrecht, nld.
    • Schwering, A.; Pebesma, E.; Behnke, K. (Eds.): . Conference Proceedings Geoinformatik 2011. : Akademische Verlagsgesellschaft.
    • Helle Kristina B., Astrup Poul, Raskob Wolfgang, Pebesma Edzer. . ‘Comparison of Mapping Methods for Plumes Using Prior Knowledge from Simulations.’ In Proceedings of the Seventh International Symposium on Spatial Data Quality, edited by Fonte Cidalia C, Goncalves Luisa, Goncalves Gil, 15–20.
    • Helle Kristina B., Urso Laura, Astrup Poul, Mikkelsen Torben, Kaiser Jan C., Pebesma Edzer, Rojas-Palma Carlos, Holo Eldri, Dyve Jan E., Raskob Wolfgang. . ‘Planning sensor locations for the detection of radioactive plumes for Norway and the Balkans.’ In Proceedings of the International Conference on Radioecology & Environmental Radioactivity, edited by Barescut J-C, Lariviere D, Stocki T, 55–61.: EDP Sciences. doi: 10.1051/radiopro/20116628s.
    • Gerharz Lydia, Gräler Benedikt, Pebesma Edzer. . ‘Disaggregating gridded air quality data for individual exposure modelling.’ Procedia Environmental Sciences 7: 146–151. doi: 10.1016/j.proenv.2011.07.026.
    • Gräler Benedikt, Pebesma Edzer. . ‘The pair-copula construction for spatial data: a new approach to model spatial dependency.’ Procedia Environmental Sciences 7: 206–211. doi: 10.1016/j.proenv.2011.07.036.
    • Sluiter R, Pebesma EJ. . ‘Comparing techniques for vegetation classification using multi- and hyperspectral images and ancillary environmental data.’ International Journal of Remote Sensing 31, Nr. 23: 6143–6161. doi: 10.1080/01431160903401379.
    • Hiemstra PH, Pebesma EJ, Heuvelink GBM, Twenhöfel CJW. . ‘Using rainfall radar data to improve interpolated maps of dose rate in the Netherlands.’ Science of the Total Environment 409, Nr. 1: 123–133. doi: 10.1016/j.scitotenv.2010.08.051.
    • Skøien JO, Baume OP, Pebesma EJ, M Heuvelink GB. . ‘Identifying and removing heterogeneities between monitoring networks.’ Environmetrics 21, Nr. 1: 66–84.
    • de Nijs T, Pebesma E. . ‘Estimating the influence of the neighbourhood in the development of residential areas in the Netherlands.’ Environment and Planning B: Planning and Design 37, Nr. 1: 21–41.
    • Pebesma E., Cornford D., Nativi S., Stasch C. . ‘The uncertainty enabled model web (UncertWeb).’ Contributed to the Workshop on "Environmental Information Systems and Services - Infrastructures and Platforms", envip 2010 - At EnviroInfo 2010, Bonn, deu.
    • Helle Kristina B., Pebesma Edzer. . ‘Conservative Updating of Sampling Designs.’ In Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, edited by Tate Nicholas J., Fisher Peter F., 181–184.
    • Helle Kristina B., Pebesma Edzer.Optimizing Spatio-Temporal Sampling Designs of Synchronous, Static, or Clustered Measurements.“ contributed to the European Geosciences Union General Assembly, Vienna, .
    • Gerharz L., Pebesma E., Hecking H. . ‘Visualizing uncertainty in spatio-temporal data.’ Contributed to the 9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Accuracy 2010, gbr.
    • Helle K., Pebesma E. . ‘Conservative updating of sampling designs.’ Contributed to the 9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Accuracy 2010, gbr.
    • Gerharz L, Pebesma E, Hecking H. . ‘Visualizing uncertainty in spatio-temporal data.’ In Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, edited by Tate NJ, Fisher PF, 169–172.
    • Hiemstra PH, Pebesma EJ, Twenhöfel CJW, Heuvelink GBM. . ‘Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network.’ Computers and Geosciences 35, Nr. 8: 1711–1721. doi: 10.1016/j.cageo.2008.10.011.
    • Beelen R, Hoek G, Pebesma E, Vienneau D, de Hoogh K, Briggs DJ. . ‘Mapping of background air pollution at a fine spatial scale across the European Union.’ Science of the Total Environment 407, Nr. 6: 1852–1867. doi: 10.1016/j.scitotenv.2008.11.048.
    • Pebesma E.J. . ‘How we build geostatistical models and deal with their output.’ In Interfacing Geostatistics and GIS, edited by Springer, 3–15. Springer VDI Verlag. doi: 10.1007/978-3-540-33236-7_1.
    • Dubois G., De Jesus J., Doherty B., Cornford D., Pebesma E. . ‘Lessons learned from INTAMAP, an interoperable web service for the real-time interpolation of environmental variables.’ Contributed to the 33rd International Symposium on Remote Sensing of Environment, ISRSE 2009, Stresa, ita.
    • Gerharz L, Pebesma E. . ‘Usability of interactive and non-interactive visualisation of uncertain geospatial information.In Geoinformatik 2009 Konferenzband, edited by Reinhardt W, Krüger A, Ehlers M, 223–230.
    • Skøien JO, Pebesma EJ, Blöschl G. . ‘Geostatistics for automatic estimation of environmental variables-some simple solutions.’ Georisk 2, Nr. 4: 257–270.
    • ter Braak CJF, Brus DJ, Pebesma EJ. . ‘Comparing sampling patterns for kriging the spatial mean temporal trend.’ Journal of Agricultural, Biological, and Environmental Statistics 13, Nr. 2: 159–176.
    • Bivand R, Pebesma E, Gomez-Rubio V. . Applied Spatial Data Analysis with R. New York: Springer VDI Verlag.
    • Pebesma Edzer, Bishr Mohammed, Bartoschek Thomas (Eds.): . Proceedings of the 6th Geographic Information Days. 400. Aufl. : unbekannt / n.a. / unknown.
    • Addink EA, De Jong SM, Pebesma EJ. . ‘The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery.’ Photogrammetric Engineering and Remote Sensing 73, Nr. 8: 905–912.
    • Pebesma EJ, de Jong K, Briggs D. . ‘Interactive visualization of uncertain spatial and spatio-temporal data under different scenarios: An air quality example.’ International Journal of Geographical Information Science 21, Nr. 5: 515–527. doi: 10.1080/13658810601064009.
    • Pebesma EJ, Switzer P, Loague K. . ‘Error analysis for the evaluation of model performance: Rainfall-runoff event summary variables.’ Hydrological Processes 21, Nr. 22: 3009–3024. doi: 10.1002/hyp.6529.
    • Schuurmans JM, Bierkens MFP, Pebesma EJ, Uijlenhoet R. . ‘Automatic prediction of high-resolution daily rainfall fields for multiple extents: The potential of operational radar.’ Journal of Hydrometeorology 8, Nr. 6: 1204–1224. doi: 10.1175/2007JHM792.1.
    • Dubois G, Pebesma EJ, Bossew P. . ‘Automatic mapping in emergency: A geostatistical perspective.’ International Journal of Emergency Management 4, Nr. 3: 455–467. doi: 10.1504/IJEM.2007.014297.
    • Pebesma EJ. . ‘The role of external variables and GIS databases in geostatistical analysis.’ Transactions in GIS 10, Nr. 4: 615–632. doi: 10.1111/j.1467-9671.2006.01015.x.
    • Pebesma E., Karssenberg D., De Jong K. . ‘Dynamic visualisation of spatial and spatio-temporal probability distribution functions.’ Contributed to the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, ACCURACY 2006, prt.
    • Pebesma EJ, Duin RNM, Burrough PA. . ‘Mapping sea bird densities over the North Sea: Spatially aggregated estimates and temporal changes.’ Environmetrics 16, Nr. 6: 573–587. doi: 10.1002/env.723.
    • Pebesma EJ. . ‘Mapping radioactivity from monitoring data: Automating the classical geostatistical approach.’ Applied GIS 1, Nr. 2.
    • Pebesma EJ, Switzer P, Loague K. . ‘Error analysis for the evaluation of model performance: Rainfall-runoff event time series data.’ Hydrological Processes 19, Nr. 8: 1529–1548. doi: 10.1002/hyp.5587.
    • Pebesma EJ. . ‘Multivariable geostatistics in S: The gstat package.’ Computers and Geosciences 30, Nr. 7: 683–691. doi: 10.1016/j.cageo.2004.03.012.
    • Pfeffer K, Pebesma EJ, Burrough PA. . ‘Mapping alpine vegetation using vegetation observations and topographic attributes.’ Landscape Ecology 18, Nr. 8: 759–776. doi: 10.1023/B:LAND.0000014471.78787.d0.
    • De Jong SM, Pebesma EJ, Lacaze B. . ‘Above-ground biomass assessment of Mediterranean forests using airborne imaging spectrometry: The DAIS Peyne experiment.’ International Journal of Remote Sensing 24, Nr. 7: 1505–1520.
    • Van Horssen PW, Pebesma EJ, Schot PP. . ‘Uncertainties in spatially aggregated predictions from a logistic regression model.’ Ecological Modelling 154, Nr. 1-2: 93–101.
    • Kros J, Mol-Dijkstra JP, Pebesma EJ. . ‘Assessment of the prediction error in a large-scale application of a dynamic soil acidification model.’ Stochastic Environmental Research and Risk Assessment 16, Nr. 4: 279–306.
    • De Wit MJM, Pebesma EJ. . ‘Nutrient fluxes at the river basin scale. II: The balance between data availability and model complexity.’ Hydrological Processes 15, Nr. 5: 761–775.
    • Thorsen M, Refsgaard J, Hansen S, Pebesma E, Jensen J, Kleeschulte S. . ‘Assessment of uncertainty in simulation of nitrate leaching to aquifers at catchment scale.’ Journal of Hydrology 242, Nr. 3–4: 210–227. doi: 10.1016/S0022-1694(00)00396-6.
    • Heuvelink GBM, Musters P, Pebesma EJ. . ‘Spatio-temporal kriging of soil water content.’ Geostatistics Wollongong 96 - Proceedings of the Fifth International Geostatistics Congress, Wollongong, Australia, September 1996 : 1020–1030.
    • Heuvelink GBM, Pebesma EJ. . ‘Spatial aggregation and soil process modelling.’ Geoderma 89, Nr. 1-2: 47–65. doi: 10.1016/S0016-7061(98)00077-9.
    • Pebesma EJ, Heuvelink GBM. . ‘Latin hypercube sampling of Gaussian random fields.’ Technometrics 41, Nr. 4: 303–312. doi: 10.1080/00401706.1999.10485930.
    • Kros J, Pebesma EJ, Reinds GJ, Finke PA. . ‘Uncertainty assessment in modelling soil acidification at the European scale: A case study.’ Journal of Environmental Quality 28, Nr. 2: 366–377. doi: 10.2134/jeq1999.00472425002800020002x.
    • Finke PA, Wladis D, Kros J, Pebesma EJ, Reinds GJ. . ‘Quantification and simulation of errors in categorical data for uncertainty analysis of soil acidification modelling.’ Geoderma 93, Nr. 3-4: 177–194. doi: 10.1016/S0016-7061(99)00056-7.
    • Pebesma EJ, Wesseling CG. . ‘Gstat: A program for geostatistical modelling, prediction and simulation.’ Computers and Geosciences 24, Nr. 1: 17–31. doi: 10.1016/S0098-3004(97)00082-4.
    • Pebesma EJ, de Kwaadsteniet J. . ‘Mapping groundwater quality in the Netherlands.’ Journal of Hydrology 200, Nr. 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