Professor Dr. Edzer Pebesma

• Geoinformatik
• Geostatistik
• Raumzeitlicher Modellierung

Akademische Ausbildung

02.1991 – 03.1996

Promotion an der Universtaet Utrecht

09.1985 – 02.1991

Studium der Fysische Geografie, Universitaet Utrecht

Beruflicher Werdegang

1998 – 2007

Assistent Professor Universiteit Utrecht

03.1996 – 02.1998

Postdoc an der Universität Amsterdam

Ruf

09.2007

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

• Heisig, Johannes, Milenković, Milutin, und Pebesma, Edzer. 2025. „High-resolution canopy fuel maps based on GEDI: a foundation for wildfire modeling in Germany.“ Environmental Research: Ecology 4 (1): 1–20. doi: 10.1088/2752-664X/adaaf9.
• Pilz, Alexander, Frey, Torsten, und Pebesma, Edzer. 2025. „Analysing a Multi-Criteria Analysis to Prioritise Munition Piles in the German Baltic Sea for Remediation.“ In 28th AGILE Conference on Geographic Information Science “Geographic Information Science responding to Global Challenges”, herausgegeben von Auriol Degbelo, Serena Coetzee, Carsten Keßler, Monika Sester, Sabine Timpf und Lars Bernard. doi: 10.5194/agile-giss-6-43-2025.
• Teickner, Henning, Pebesma, Edzer, und Knorr, Klaus-Holger. 2025. „A synthesis of Sphagnum litterbag experiments: initial leaching losses bias decomposition rate estimates.“ Biogeosciences 22 (2): 417–433. doi: 10.5194/bg-22-417-2025.
• Teickner, Henning, Pebesma, Edzer, und Knorr, Klaus-Holger. 2025. „Peat oxic and anoxic controls of Sphagnum decomposition rates in the Holocene Peatland Model decomposition module estimated from litterbag data.“ Earth System Dynamics 16 (3): 891–914. doi: 10.5194/esd-16-891-2025.
• Milenković, M, Amatulli, G, Sethi, T, Oonk, R, Fouilloux, A, Odaka, T, Claus, M, Premier, V, Fonseca, A, Kempeneers, P, Lovergine, FP, Parente, L, Ho, Y, Heisig, J, Masiliunas, D, Wagner, W, Pebesma, E, Griffiths, P, Verbesselt, J, Hengl, T, McCallum, I, und Fritz, S. 2025. „GEO-OPEN-HACK: An Initiative on Big Geospatial Data Processing with Open Computing Infrastructure and Open Tools.“ In Proceedings of the 2025 Conference on Big Data from Space (BiDS'25), herausgegeben von P. Kempeneers, S. Lumnitz und S. Albani. Riga, Latvia: Publications Office of the European Union. doi: 10.2760/2119408.
• Milà, C, Ludwig, M, Pebesma, E, Tonne, C, und Meyer, H. 2024. „Random forests with spatial proxies for environmental modelling: opportunities and pitfalls.“ Geoscientific Model Development 2024 (17): 6007–603. doi: 10.5194/gmd-17-6007-2024.
• Pondi, Brian, und Pebesma, Edzer. 2024. „Standardizing Machine Learning APIs for Earth Observation Data Cubes.“ Beitrag präsentiert auf der The 5th Spatial Data Science, Online doi: 10.5281/zenodo.13960237.
• Ludwig, M, Moreno-Martinez, A, Hölzel, N, Pebesma, E, und 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.
• Mogge, L, McDonald, M, Knoth, C, Teickner, H, Purevtseren, M, Pebesma, E, und Kraehnert, K. 2023. „Allocation of humanitarian aid after a weather disaster.“ World Development 166: 106204. doi: 10.1016/j.worlddev.2023.106204.
• Heisig, J., Olson, E., und Pebesma, E. 2022. „Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing.“ Fire 5(1) (29). doi: 10.3390/fire5010029.
• Mila, C, Mateu, J, Pebesma, E, und 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, und 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.
• Ludwig, M, Bahlmann, J, Pebesma, E, und Meyer, H. 2022. „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.
• Kleinewillinghöfer, L, Olofsson, P, Pebesma, E, Meyer, H, Buck, O, Haub, C, und Eiselt, B. 2022. „Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data.“ Remote Sensing 14 (19): 4903. doi: 10.3390/rs14194903.
• Heisig, Johannes, Olson, Edward, und Pebesma, Edzer. 2022. „Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing.“ Fire 5 (1). doi: 10.3390/fire5010029.
• Meyer, H, und 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, und Pebesma, E. 2021. „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.
• Nüst, Daniel Pebesma Edzer. 2020. „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, und Pebesma, E. 2020. „Patterns in Mongolian nomadic household movement derived from GPS trajectories.“ Applied Geography 122 (September 2020): 102270. doi: 10.1016/j.apgeog.2020.102270.
• Appel, Marius Pebesma Edzer. 2020. „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, und Pebesma, E. 2019. „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, und Nüst, D. 2019. „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. Dagstuhl, Germany: Dagstuhl Publishing. doi: 10.4230/LIPIcs.COSIT.2019.8.
• Appel, M, und Pebesma, E. 2019. „On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library.“ Data 4 (3). doi: 10.3390/data4030092.
• Kraehnert, K, Vigh, M, Knoth, C, Teickner, H, Purevtseren, M, Sugar, M, und Pebesma, E. 2019. „Herders Mobility GPS Tracking:Insights From Novel Trajectory Data.“ In Conférence internationale "Systèmes Complexes, Intelligence Territorialeet Mobilité", herausgegeben von P Sajous und C Bertelle.
• Gupta, Shivam, Pebesma, Edzer, Mateu, Jorge, und Degbelo, Auriol. 2018. „Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models.“ Sustainability 10 (5). doi: 10.3390/su10051442.
• Gupta, Shivam Mateu Jorge, und Degbelo, Auriol Pebesma Edzer. 2018. „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, und Lahn, F. 2018. „R vector and raster data cubes for openEO.“ Beitrag präsentiert auf der 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, und Kempeneers, P. 2018. „openEO: an open API for cloud-based big Earth Observation processing platforms.“ Beitrag präsentiert auf der EGU General Assembly 2018, Vienna, Austria
• Appel, Marius Lahn Florian, und Buytaert, Wouter Pebesma Edzer. 2018. „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, und Pebesma, E. 2018. „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, und Pebesma, E. 2018. „Multidimensional Arrays for Analysing Geoscientific Data.“ ISPRS International Journal of Geo-Information 7 (8). doi: 10.3390/ijgi7080313.
• Ghosh, P., Lahn, F., Gebbert, S., Mohr, M., und Pebesma, E. 2018. „Running user-defined functions in R on Earth observation data in cloud back-ends.“ Beitrag präsentiert auf der 10th Geomundus conference, Lisbon, Portugal
• Lu, Meng Hamunyela Eliakim, und Verbesselt, Jan Pebesma Edzer. 2017. „Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring.“ Remote Sensing 9 (10). doi: 10.3390/rs9101025.
• Konkol, Markus, Nüst, Daniel, Schutzeichel, Marc, Pebesma, Edzer, Kray, Christian, Przibytzin, Holger, und Lorenz, Jörg. 2017. „Opening reproducible research (o2r).“ Beitrag präsentiert auf der Open Science Conference, Berlin, Germany
• Nüst, D, Konkol, M, Pebesma, E, Kray, C, Schutzeichel, M, Przibytzin, H, und Lorenz, J. 2017. „Opening the Publication Process with Executable Research Compendia.“ D-Lib Magazine 23. doi: 10.1045/january2017-nuest.
• Roy, A., und Pebesma, E. 2017. „A machine learning approach to demographic prediction using geohashes.“ In Bd. null aus International Workshop on Social Sensing ACM Press. doi: 10.1145/3055601.3055603.
• Mariethoz, G., und Pebesma, E. 2017. „Nurturing a growing field: Computers & Geosciences.“ Computers and Geosciences 107 (null): A1–A2. doi: 10.1016/j.cageo.2017.08.006.
• Gebbert, S., und Pebesma, E. 2017. „The GRASS GIS temporal framework.“ International Journal of Geographical Information Science 31 (7): 1273–1292. doi: 10.1080/13658816.2017.1306862.
• Sidhu, Nanki, Pebesma, Edzer, und Wang, Yi-Chen. 2017. „Usability Study to Assess the IGBP Land Cover Classification for Singapore.“ Remote Sensing 2017 (9(10), 1075). doi: 10.3390/rs9101075.
• Lu, Meng, Appel, Marius, und Pebesma, Edzer. 2017. „Modelling spatiotemporal change using multidimensional arrays.“ Beitrag präsentiert auf der EGU General Assembly 2017, Vienna, Austria
• Appel, Marius, Nüst, Daniel, und Pebesma, Edzer. 2017. „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
• Shivam, gupta, Edzer, Pebesma, und Jorge, Mateu. 2017. „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.
• Knoth, C, und Pebesma, E. 2017. „Detecting dwelling destruction in Darfur through object-based change analysis of very high resolution imagery.“ International Journal of Remote Sensing 38 (1): 273–295. doi: 10.1080/01431161.2016.1266105.
• Appel, Marius, Lahn, Florian, Pebesma, Edzer, Buytaert, Wouter, und Moulds, Simon. 2016. „Scalable Earth-observation Analytics for Geoscientists: Spacetime Extensions to the Array Database SciDB.“ Beitrag präsentiert auf der EGU General Assembly 2016, Vienna, Austria
• Lu, Meng, Pebesma, Edzer, Sanshaz, Alber, und Verbesselt, Jan. 2016. „Spatio-temporal change detection from multidimensionalarrays: Detecting deforestation from MODIS time series.“ ISPRS Journal of Photogrammetry and Remote Sensing 117 (227-236).
• Pebesma, E., Mailund, T., und Hiebert, J. 2016. „Measurement units in R.“ R Journal 8 (2): 490–498.
• Gräler, B., Pebesma, E., und Heuvelink, G. 2016. „Spatio-temporal interpolation using gstat.“ R Journal 8 (1): 204–218.
• Nüst, Daniel, Konkol, Markus, Pebesma, Edzer, Kray, Christian, Klötgen, Stephanie, Schutzeichel, Marc, Lorenz, Jörg, Przibytzin, Holger, und Kussmann, Dirk. 2016. „Opening Reproducible Research.“ In Bd. 18 aus Geophysical Research Abstracts, herausgegeben von Geophysical Union European.
• Scheider, S., Gräler, B., Pebesma, E., und Stasch, C. 2016. „Modeling spatiotemporal information generation.“ International Journal of Geographical Information Science null (null): 1–29. doi: 10.1080/13658816.2016.1151520.
• Appel, Marius, Pebesma, Edzer, und Camara, Gilberto. 2015. „Scalable In-Database Regression Analysis of Large Earth-Observation Datasets.“ Beitrag präsentiert auf der EO Open Science 2.0, Frascati, Italy
• Pebesma, E., und R., Bivand P.J. Ribeiro. 2015. „Software for Spatial Statistics.“ Journal of Statistical Software 63 (1).
• Hengl, T., Roudier, P., Beaudette, D., und Pebesma, E. 2015. „plotKML: Scientific Visualization of Spatio-Temporal Data.“ Journal of Statistical Software 63 (5).
• Hengl, T., Pebesma, E., und Hijmans, R. 2015. „Spatial and spatio-temporal modeling of meteorological and climatic variables using Open Source software.“ Spatial Statistics null (null). doi: 10.1016/j.spasta.2015.06.005.
• Helle, K., und Pebesma, E. 2015. „Optimising sampling designs for the maximum coverage problem of plume detection.“ Spatial Statistics 13 (null): 21–44. doi: 10.1016/j.spasta.2015.03.004.
• Lemke, D., Mattauch, V., Heidinger, O., Pebesma, E., und Hense, H. 2015. „Comparing adaptive and fixed bandwidth-based kernel density estimates in spatial cancer epidemiology.“ International Journal of Health Geographics 14 (1). doi: 10.1186/s12942-015-0005-9.
• Meng, Lu, und Edzer, Pebesma. 2015. „Spatio-temporal change modeling with array data.“ Beitrag präsentiert auf der EGU 2015, Vienna, Austria
• Lu, M., Pebesma, E., Sanchez, A., und Verbesselt, J. 2015. „Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series.“ ISPRS Journal of Photogrammetry and Remote Sensing null (null). doi: 10.1016/j.isprsjprs.2016.03.007.
• Lemke, D., Berkemeyer, S., Mattauch, V., Heidinger, O., Pebesma, E., und Hense, H. 2015. „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 (1). doi: 10.1186/s12889-015-2520-9.
• Skoien, J.O., Bloschl, G., Laaha, G., Pebesma, E., Parajka, J., und Viglione, A. 2014. „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., und Pebesma, E. 2014. „Bayesian area-to-point kriging using expert knowledge as informative priors.“ International Journal of Applied Earth Observation and Geoinformation 30 (1): 128–138. doi: 10.1016/j.jag.2014.01.019.
• Jankowski, P., Fraley, G., und Pebesma, E. 2014. „An exploratory approach to spatial decision support.“ Computers, Environment and Urban Systems 45 (null): 101–113. doi: 10.1016/j.compenvurbsys.2014.02.008.
• Knoth, C, und Pebesma, E. 2014. „Detecting Destruction in Conflict Areas in Darfur.“ Beitrag präsentiert auf der GEOBIA 2014 - Geographic Object Based Image Analysis, Thessaloniki, Greece
• Kilibarda, M, Hengl, T, Heuvelink, GBM, Gräler, B, Pebesma, E, Perčec, Tadić M, und Bajat, B. 2014. „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., und Pebesma, E. 2014. „A temporal GIS for field based environmental modeling.“ Environmental Modelling and Software 53 (null): 1–12. doi: 10.1016/j.envsoft.2013.11.001.
• Stasch, C., Scheider, S., Pebesma, E., und Kuhn, W. 2014. „Meaningful spatial prediction and aggregation.“ Environmental Modelling and Software 51 (null): 149–165. doi: 10.1016/j.envsoft.2013.09.006.
• Stasch, C., Nüst, D., Rieke, M., Remke, A., und Pebesma, E. 2014. „enviroCar – Open car data and open analysis tools for sustainable transportation development.“ Beitrag präsentiert auf der 2nd International Conference ICT for Sustainability, Stockholm, Schweden
• Meng, Lu, und Edzer, Pebesma. 2014. „Modeling change from large-scale high-dimensional spatio-temporal array data.“ Beitrag präsentiert auf der EGU 2014, Vienna, Austria
• Gerharz, LE, und Pebesma, E. 2013. „Using geostatistical simulation to disaggregate air quality model results for individual exposure estimation on GPS tracks.“ Stochastic Environmental Research and Risk Assessment 27 (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, und Williams, M. 2013. „Managing uncertainty in integrated environmental modelling: The UncertWeb framework.“ Environmental Modelling and Software 39: 116–134.
• Gerharz, LE, Klemm, O, Broich, AV, und Pebesma, E. 2013. „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. 2013. „Plume Tracking with a Mobile Sensor Based on Incomplete and Imprecise Information.“ Transactions in GIS 2013. doi: 10.1111/tgis.12063.
• Bivand, R., Pebesma, E., und Gómez-Rubio, V. 2013. Applied Spatial Data Analysis with R: Second Edition, herausgegeben von Roger Bivand, Edzer Pebesma und Virgilio Gomez-Rubio. Springer. doi: 10.1007/978-1-4614-7618-4.
• Mello, M., Risso, J., Atzberger, C., Aplin, P., Pebesma, E., Vieira, C., und Rudorff, B. 2013. „Bayesian networks for raster data (BayNeRD): Plausible reasoning from observations.“ Remote Sensing 5 (11): 5999–6025. doi: 10.3390/rs5115999.
• Diniz, L., Buurman, M., Andrade, P., Camara, G., und Pebesma, E. 2013. „Measuring allocation errors in land change models in amazonia.“ In Bd. null National Institute for Space Research, INPE.
• Stasch, C., Pebesma, E., Graeler, B., und Gerharz, L. 2013. „Error-aware spatio-temporal aggregation in the model web.“ In Bd. null aus AGILE Conference on Geographic Information Science Kluwer Academic. doi: 10.1007/978-3-319-00615-4_12.
• Mello, M.P., Aguiar, D.A., Rudorff, B.F.T., Pebesma, E., Jones, J., und Santos, N.C.P. 2013. „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.
• Lemke, D., Mattauch, V., Heidinger, O., Pebesma, E., und Hense, H.-W. 2013. „Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing Methods: A simulation study.“ International Journal of Health Geographics null (null): 54. doi: 10.1186/1476-072X-12-54.
• Hengl, T, Heuvelink, GBM, Tadić, MP, und Pebesma, EJ. 2012. „Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images.“ Theoretical and Applied Climatology 107 (1-2): 265–277. doi: 10.1007/s00704-011-0464-2.
• Pebesma, E, Nüst, D, und Bivand, R. 2012. „The R software environment in reproducible geoscientific research.“ Eos, Transactions American Geophysical Union 93 (16): 163. doi: 10.1029/2012EO160003.
• Senaratne, H, Gerharz, L, Pebesma, E, und Schwering, A. 2012. „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, und Pebesma, E. 2012. „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, und Stein, A. 2012. „Spatial statistics for mapping the environment.“ The ITC Journal.
• Gräler, Benedikt, Gerharz, Lydia, und Pebesma, Edzer. 2012. „Spatio-temporal analysis and interpolation of PM10 measurements in Europe.“ online.
• Helle, K., und Pebesma, E. 2012. „Stationary Sampling Designs Based on Plume Simulations.“ In Spatio-temporal Design: Advances in Efficient Data Acquisition, Bd. null , herausgegeben von Jorge Mateu und Werner Mueller. John Wiley & Sons. doi: 10.1002/9781118441862.ch14.
• Schulz, M., Skøien, J., Gerharz, L., Dubois, G., und Pebesma, E. 2012. „Uncertainty propagation between web services - A case study using the eHabitat WPS to identify unique ecosystems.“ In Bd. null
• Pross, B., Gerharz, L., Stasch, C., und Pebesma, E. 2012. „Tools for uncertainty propagation in the model web using Monte Carlo simulation.“ In Bd. null
• Pebesma, E. 2012. „spacetime: Spatio-Temporal Data in R.“ Journal of Statistical Software 51 (7).
• Helle, Kristina Pebesma Edzer. 2012. „Stationary Sampling Designs Based on Plume Simulations.“ In Spatio-Temporal Design, herausgegeben von Wiley. John Wiley & Sons. doi: 10.1002/9781118441862.ch14.
• Pross, Benjamin Gerharz Lydia, und Stasch, Christoph Pebesma Edzer. 2012. „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.
• Nust, D, Stasch, C, und Pebesma, E. 2011. „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, und Heuvelink, G. 2011. „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, und Raskob, W. 2011. „Planning sensor locations for the detection of radioactive plumes for Norway and the Balkans *.“ Radioprotection 46 (6 SUPPL.): S55–S61.. doi: 10.1051/radiopro/20116628s.
• Fritze, H, Stewart, IT, und Pebesma, E. 2011. „Shifts in western North American snowmelt runoff regimes for the recent warm decades.“ Journal of Hydrometeorology 12 (5): 989–1006. doi: 10.1175/2011JHM1360.1.
• Baume, O, Skøien, JO, Heuvelink, GBM, Pebesma, EJ, und Melles, SJ. 2011. „A geostatistical approach to data harmonization - Application to radioactivity exposure data.“ International Journal of Applied Earth Observation and Geoinformation 13 (3): 409–419. doi: 10.1016/j.jag.2010.09.002.
• Dubois, G, Cornford, D, Hristopulos, D, Pebesma, E, und Pilz, J. 2011. „Introduction to this special issue on geoinformatics for environmental surveillance.“ Computers and Geosciences 37 (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, und Skøien, JO. 2011. „INTAMAP: The design and implementation of an interoperable automated interpolation web service.“ Computers and Geosciences 37 (3): 343–352. doi: 10.1016/j.cageo.2010.03.019.
• De, Espindola GM, De, Aguiar APD, Pebesma, E, Câmara, G, und Fonseca, L. 2011. „Agricultural land use dynamics in the Brazilian Amazon based on remote sensing and census data.“ Applied Geography 32 (2): 240–252.
• Fairgrieve, S., Stasch, C., Falke, S., Gerharz, L., und Pebesma, E. 2011. „Error aware near real-time interpolation of air quality observations in GEOSS.“ In Bd. 712
• Schwering, A., Pebesma, E., und Behnke, K., Hrsg. 2011. ifgi prints no 41, Conference Proceedings Geoinformatik 2011 Akademische Verlagsgesellschaft.
• Helle, Kristina B Astrup Poul, und Raskob, Wolfgang Pebesma Edzer. 2011. „Comparison of Mapping Methods for Plumes Using Prior Knowledge from Simulations.“ In Proceedings of the Seventh International Symposium on Spatial Data Quality, herausgegeben von Cidalia C Fonte, Luisa Goncalves und Gil Goncalves.
• Helle, Kristina B., Urso, Laura, Astrup, Poul, Mikkelsen, Torben, Kaiser, Jan C., Pebesma, Edzer, Rojas-Palma, Carlos, Holo, Eldri, Dyve, Jan E., und Raskob, Wolfgang. 2011. „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. EDP Sciences. doi: 10.1051/radiopro/20116628s.
• Gerharz, Lydia, Gräler, Benedikt, und Pebesma, Edzer. 2011. „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. 2011. „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, und Pebesma, EJ. 2010. „Comparing techniques for vegetation classification using multi- and hyperspectral images and ancillary environmental data.“ International Journal of Remote Sensing 31 (23): 6143–6161. doi: 10.1080/01431160903401379.
• Hiemstra, PH, Pebesma, EJ, Heuvelink, GBM, und Twenhöfel, CJW. 2010. „Using rainfall radar data to improve interpolated maps of dose rate in the Netherlands.“ Science of the Total Environment 409 (1): 123–133. doi: 10.1016/j.scitotenv.2010.08.051.
• Skøien, JO, Baume, OP, Pebesma, EJ, und M, Heuvelink GB. 2010. „Identifying and removing heterogeneities between monitoring networks.“ Environmetrics 21 (1): 66–84.
• de, Nijs T, und Pebesma, E. 2010. „Estimating the influence of the neighbourhood in the development of residential areas in the Netherlands.“ Environment and Planning B: Planning and Design 37 (1): 21–41.
• Pebesma, E., Cornford, D., Nativi, S., und Stasch, C. 2010. „The uncertainty enabled model web (UncertWeb).“ In Bd. 679
• Helle, Kristina B., und Pebesma, Edzer. 2010. „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.
• Helle, Kristina B., und Pebesma, Edzer. 2010. „Optimizing Spatio-Temporal Sampling Designs of Synchronous, Static, or Clustered Measurements.“ Beitrag präsentiert auf der European Geosciences Union General Assembly, Vienna
• Gerharz, L., Pebesma, E., und Hecking, H. 2010. „Visualizing uncertainty in spatio-temporal data.“ In Bd. null aus International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences International Spatial Accuracy Research Association (ISARA).
• Helle, K., und Pebesma, E. 2010. „Conservative updating of sampling designs.“ In Bd. null aus International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences International Spatial Accuracy Research Association (ISARA).
• Gerharz, L, Pebesma, E, und Hecking, H. 2010. „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.
• Hiemstra, PH, Pebesma, EJ, Twenhöfel, CJW, und Heuvelink, GBM. 2009. „Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network.“ Computers and Geosciences 35 (8): 1711–1721. doi: 10.1016/j.cageo.2008.10.011.
• Beelen, R, Hoek, G, Pebesma, E, Vienneau, D, de, Hoogh K, und Briggs, DJ. 2009. „Mapping of background air pollution at a fine spatial scale across the European Union.“ Science of the Total Environment 407 (6): 1852–1867. doi: 10.1016/j.scitotenv.2008.11.048.
• Pebesma, E.J. 2009. „How we build geostatistical models and deal with their output.“ In Interfacing Geostatistics and GIS, Bd. null , herausgegeben von Springer. Springer VDI Verlag. doi: 10.1007/978-3-540-33236-7_1.
• Dubois, G., De, Jesus J., Doherty, B., Cornford, D., und Pebesma, E. 2009. „Lessons learned from INTAMAP, an interoperable web service for the real-time interpolation of environmental variables.“ In Bd. null
• Gerharz, L, und Pebesma, E. 2009. „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.
• Skøien, JO, Pebesma, EJ, und Blöschl, G. 2008. „Geostatistics for automatic estimation of environmental variables-some simple solutions.“ Georisk 2 (4): 257–270.
• ter, Braak C JF, Brus, DJ, und Pebesma, EJ. 2008. „Comparing sampling patterns for kriging the spatial mean temporal trend.“ Journal of Agricultural, Biological, and Environmental Statistics 13 (2): 159–176.
• Bivand, R, Pebesma, E, und Gomez-Rubio, V. 2008. UseR!, Applied Spatial Data Analysis with R New York: Springer VDI Verlag.
• Pebesma, Edzer, Bishr, Mohammed, und Bartoschek, Thomas, Hrsg. 2008. ifgiPrints, Bd. 32, Proceedings of the 6th Geographic Information Days 400. Aufl. unbekannt / n.a. / unknown.
• Addink, EA, De, Jong SM, und Pebesma, EJ. 2007. „The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery.“ Photogrammetric Engineering and Remote Sensing 73 (8): 905–912.
• Pebesma, EJ, de, Jong K, und Briggs, D. 2007. „Interactive visualization of uncertain spatial and spatio-temporal data under different scenarios: An air quality example.“ International Journal of Geographical Information Science 21 (5): 515–527. doi: 10.1080/13658810601064009.
• Pebesma, EJ, Switzer, P, und Loague, K. 2007. „Error analysis for the evaluation of model performance: Rainfall-runoff event summary variables.“ Hydrological Processes 21 (22): 3009–3024. doi: 10.1002/hyp.6529.
• Schuurmans, JM, Bierkens, MFP, Pebesma, EJ, und Uijlenhoet, R. 2007. „Automatic prediction of high-resolution daily rainfall fields for multiple extents: The potential of operational radar.“ Journal of Hydrometeorology 8 (6): 1204–1224. doi: 10.1175/2007JHM792.1.
• Dubois, G, Pebesma, EJ, und Bossew, P. 2007. „Automatic mapping in emergency: A geostatistical perspective.“ International Journal of Emergency Management 4 (3): 455–467. doi: 10.1504/IJEM.2007.014297.
• Pebesma, EJ. 2006. „The role of external variables and GIS databases in geostatistical analysis.“ Transactions in GIS 10 (4): 615–632. doi: 10.1111/j.1467-9671.2006.01015.x.
• Pebesma, E., Karssenberg, D., und De, Jong K. 2006. „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 Instituto Geografica Portugues.
• Pebesma, EJ, Duin, RNM, und Burrough, PA. 2005. „Mapping sea bird densities over the North Sea: Spatially aggregated estimates and temporal changes.“ Environmetrics 16 (6): 573–587. doi: 10.1002/env.723.
• Pebesma, EJ. 2005. „Mapping radioactivity from monitoring data: Automating the classical geostatistical approach.“ Applied GIS 1 (2).
• Pebesma, EJ, Switzer, P, und Loague, K. 2005. „Error analysis for the evaluation of model performance: Rainfall-runoff event time series data.“ Hydrological Processes 19 (8): 1529–1548. doi: 10.1002/hyp.5587.
• Pebesma, EJ. 2004. „Multivariable geostatistics in S: The gstat package.“ Computers and Geosciences 30 (7): 683–691. doi: 10.1016/j.cageo.2004.03.012.
• Pfeffer, K, Pebesma, EJ, und Burrough, PA. 2003. „Mapping alpine vegetation using vegetation observations and topographic attributes.“ Landscape Ecology 18 (8): 759–776. doi: 10.1023/B:LAND.0000014471.78787.d0.
• De, Jong SM, Pebesma, EJ, und Lacaze, B. 2003. „Above-ground biomass assessment of Mediterranean forests using airborne imaging spectrometry: The DAIS Peyne experiment.“ International Journal of Remote Sensing 24 (7): 1505–1520.
• Van, Horssen PW, Pebesma, EJ, und Schot, PP. 2002. „Uncertainties in spatially aggregated predictions from a logistic regression model.“ Ecological Modelling 154 (1-2): 93–101.
• Kros, J, Mol-Dijkstra, JP, und Pebesma, EJ. 2002. „Assessment of the prediction error in a large-scale application of a dynamic soil acidification model.“ Stochastic Environmental Research and Risk Assessment 16 (4): 279–306.
• De, Wit MJM, und Pebesma, EJ. 2001. „Nutrient fluxes at the river basin scale. II: The balance between data availability and model complexity.“ Hydrological Processes 15 (5): 761–775.
• Thorsen, M, Refsgaard, J, Hansen, S, Pebesma, E, Jensen, J, und Kleeschulte, S. 2001. „Assessment of uncertainty in simulation of nitrate leaching to aquifers at catchment scale.“ Journal of Hydrology 242 (3–4): 210–227. doi: 10.1016/S0022-1694(00)00396-6.
• Heuvelink, GBM, Musters, P, und Pebesma, EJ. 1999. „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, und Pebesma, EJ. 1999. „Spatial aggregation and soil process modelling.“ Geoderma 89 (1-2): 47–65. doi: 10.1016/S0016-7061(98)00077-9.
• Pebesma, EJ, und Heuvelink, GBM. 1999. „Latin hypercube sampling of Gaussian random fields.“ Technometrics 41 (4): 303–312. doi: 10.1080/00401706.1999.10485930.
• Kros, J, Pebesma, EJ, Reinds, GJ, und Finke, PA. 1999. „Uncertainty assessment in modelling soil acidification at the European scale: A case study.“ Journal of Environmental Quality 28 (2): 366–377. doi: 10.2134/jeq1999.00472425002800020002x.
• Finke, PA, Wladis, D, Kros, J, Pebesma, EJ, und Reinds, GJ. 1999. „Quantification and simulation of errors in categorical data for uncertainty analysis of soil acidification modelling.“ Geoderma 93 (3-4): 177–194. doi: 10.1016/S0016-7061(99)00056-7.
• Pebesma, EJ, und Wesseling, CG. 1998. „Gstat: A program for geostatistical modelling, prediction and simulation.“ Computers and Geosciences 24 (1): 17–31. doi: 10.1016/S0098-3004(97)00082-4.
• Pebesma, EJde Kwaadsteniet J. 1997. „Mapping groundwater quality in the Netherlands.“ Journal of Hydrology 200 (1–4): 364–386. doi: 10.1016/S0022-1694(97)00027-9.

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