Publications of the Spatio-Temporal Modelling Lab Research Group

• 2024
• 2023
• 2022
• 2021
• 2020
• 2019
• 2018
• 2017
• 2016
• 2015
• 2014
• 2013
• 2012
• 2011
• 2010
• 2009
• 2008
• 2007
• 2006
• 2005
• 2004
• 2003
• 2002
• 2001
• 1999
• 1998
• 1997

2024

• Milà, C, Ludwig, M, Pebesma, E, Tonne, C, and 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.
• Schumacher, F, Knoth, C, Ludwig, M, and Meyer, H. 2024. “Estimation of local training data point densities to support the assessment of spatial prediction uncertainty.” EGUsphere doi: 10.5194/egusphere-2024-2730.
• Nalepa, Jakub, Tulczyjew, Lukasz, Saux, Bertrand Le, Longépé, Nicolas, Ruszczak, Bogdan, Wijata, Agata M, Smykala, Krzysztof, Myller, Michal, Kawulok, Michal, Kuzu, Ridvan Salih, Albrecht, Frauke, Arnold, Caroline, Alasawedah, Mohammad, Angeli, Suzanne, Nobileau, Delphine, Ballabeni, Achille, Lotti, Alessandro, Locarini, Alfredo, Modenini, Dario, Tortora, Paolo, and Gumiela, Michal. 2024. “Estimating Soil Parameters From Hyperspectral Images: A benchmark dataset and the outcome of the HYPERVIEW challenge.” IEEE Geoscience and Remote Sensing Magazine, № 2024 doi: 10.1109/MGRS.2024.3394040.
• Pondi, Brian, and Pebesma, Edzer. 2024. “Standardizing Machine Learning APIs for Earth Observation Data Cubes.” contribution to the The 5th Spatial Data Science, Online doi: https://doi.org/10.5281/zenodo.13960237.

2023

• Ziegler, A, Heisig, J, Ludwig, M, Reudenbach, C, Meyer, H, and Nauss, T. 2023. “Using GEDI as training data for an ongoing mapping of landscape-scale dynamics of the plant area index.” Environmental Research Letters, № 18 (7) doi: 10.1088/1748-9326/acde8f.
• Ludwig, M, Moreno-Martinez, A, Hölzel, N, Pebesma, E, and 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, and Kraehnert, K. 2023. “Allocation of humanitarian aid after a weather disaster.” World Development, № 166: 106204. doi: 10.1016/j.worlddev.2023.106204.

2022

• Heisig, Johannes, Olson, Edward, and 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.
• Mila, C, Mateu, J, Pebesma, E, and 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, and 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, and Meyer, H. 2022. “Developing Transferable Spatial Prediction Models: a Case Study of Satellite Based Landcover Mapping.” contribution 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, and Eiselt, B. 2022. “Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data.” Remote Sensing, № 14 (19): 4903. doi: 10.3390/rs14194903.
• Carmelo, Bonannella, Tomislav, Hengl, Johannes, Heisig, Leandro, Parente, Marvin, Wright N., Martin, Herold, and Sytze, Bruin de. 2022. “Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning.” PeerJ, № 10 (e13728) doi: 10.7717/peerj.13728.
• Heisig, J., Olson, E., and 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.
• Nüst, Daniel. 2022. “Infrastructures and Practices for Reproducible Research in Geography, Geosciences, and GIScience.” Dissertation thesis, Universität Münster. doi: 10.5281/zenodo.4768096.

2021

• Meyer, H, and 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.
• Garzón, Sebastian, and Nüst, Daniel. 2021. “Exploring Research Data Repositories with geoextent.” in EarthCube 2021 Notebooks, edited by Turk Matt Shanahan Hugh, Maull Keith Abernathey Ryan, Schreiber Lynne Giglio Donata and McHenry Kenton Katz Daniel S.. Zenodo. doi: 10.5281/zenodo.5496311.
• Ostermann, FO, Nüst, D, Granell, C, Hofer, B, and Konkol, M. 2021. “Reproducible Research and GIScience: An Evaluation Using GIScience Conference Papers.” in 11th International Conference on Geographic Information Science (GIScience 2021) - Part II, Vol. 208 of Leibniz International Proceedings in Informatics (LIPIcs), edited by K Janowicz and JA Verstegen. Wadern: Dagstuhl Publishing. doi: 10.4230/LIPIcs.GIScience.2021.II.2.
• Meyer, H, and 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.
• Eglen, Stephen, and Nüst, Daniel. 2021. CODECHECK: An open science initiative for reproducible computational research,
• Jeremy, Leipzig, Daniel, Nüst, Tapley, Hoyt Charles, Karthik, Ram, and Jane, Greenberg. 2021. “The role of metadata in reproducible computational research.” Patterns, № 2 (9): 100322. doi: 10.1016/j.patter.2021.100322.
• Nüst, Daniel. 2021. preprint, A Web service for executable research compendia enables reproducible publications and transparent reviews in geospatial sciences, Zenodo. doi: 10.5281/zenodo.4818119.
• Christian, Hauschke, Daniel, Nüst, Anette, Cordts, and Svantje, Lilienthal. 2021. Research Ideas and Outcomes, Vol. 7:e66264, OPTIMETA – Strengthening the Open Access publishing system through open citations and spatiotemporal metadata, doi: 10.3897/rio.7.e66264.
• Nüst, Daniel, and Eglen, Stephen J. 2021. F1000Research preprints, CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility, F1000Research. doi: 10.12688/f1000research.51738.1.

2020

• Teickner, H, Knoth, C, Bartoschek, T, Kraehnert, K, Vigh, M, Purevtseren, M, Sugar, M, and 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.
• Nüst, Daniel, Sochat, Vanessa, Marwick, Ben, Eglen, Stephen J, Head, Tim, Hirst, Tony, and Evans, Benjamin D. 2020. “Ten simple rules for writing Dockerfiles for reproducible data science.” PLoS Computational Biology, № 16 (11): e1008316. doi: 10.1371/journal.pcbi.1008316.
• O., Ostermann Frank, Daniel, Nüst, Carlos, Granell, Barbara, Hofer, and Markus, Konkol. 2020. Reproducible Research and GIScience: an evaluation using GIScience conference papers, EarthArXiv. doi: 10.31223/X5ZK5V.
• 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.
• Graupner, Anika, and Nüst, Daniel. 2020. “Serverless GEO Labels for the Semantic Sensor Web.” in 11th International Conference on Geographic Information Science (GIScience 2021) - Part I, Vol. 177 of Leibniz International Proceedings in Informatics (LIPIcs), edited by Verstegen Judith A. Janowicz Krzysztof. Wadern: Dagstuhl Publishing. doi: 10.4230/LIPIcs.GIScience.2021.I.4.
• Nüst, Daniel, Eddelbuettel, Dirk, Bennett, Dom, Cannoodt, Robrecht, Clark, Dav, Daróczi, Gergely, Edmondson, Mark, Fay, Colin, Hughes, Ellis, Kjeldgaard, Lars, Lopp, Sean, Marwick, Ben, Nolis, Heather, Nolis, Jacqueline, Ooi, Hong, Ram, Karthik, Ross, Noam, Shepherd, Lori, Sólymos, Péter, Lee, Swetnam Tyson, Turaga, Nitesh, Charlotte, Van Petegem, Williams, Jason, Willis, Craig, and Xiao, Nan. 2020. “The Rockerverse: Packages and Applications for Containerisation with R.” R Journal, № 12 (1): 437–461. doi: 10.32614/RJ-2020-007.
• Konkol, M, Nüst, D, and Goulier, L. 2020. “Publishing computational research - a review of infrastructures for reproducible and transparent scholarly communication.” Research Integrity and Peer Review, № 5 (10) doi: 10.1186/s41073-020-00095-y.
• Leipzig Jeremy, Nüst Daniel, Hoyt Charles T, Soiland-Reyes Stian, and Ram Karthik, Greenberg Jane. 2020. arXiv.org preprints, The role of metadata in reproducible computational research, arXiv.
• Nüst, Daniel, and Konkol, Markus. 2020. “A Vision for Reproducible Research in Geoinformatics, Geography, and Geosciences.” Generation Research, № 2020 doi: 10.25815/dm8v-2241.
• Granell, C, Hofer, B, Nüst, D, Ostermann, FO, and Sileryte, R. 2020. “Reproducibilidad en AGILE: Experiencias, logros y recomendaciones.” Revista Cartográfica, № 2020 (100): 155–172. doi: 10.35424/rcarto.v0i100.668.
• Alexander, Bigga, Martin, Brändle, Ronny, Bölter, Constanze, Decker, Maria, España, Gerhard, Eilbacher, Matthias, Flasko, Frank, Krabbes, H., Leugers-Scherzberg August, Marianna, Mühlhölzer, Dennis, Müller, Philip, Münch, Tom, Niers, Daniel, Nüst, Adrian, Pachzelt, Maximilian, Plich, Ronald, Steffen, Klaus, Thoden, Hanna, Varachkina, Paul, Warner, Nils, Weiher, Daniela, Wolf, and Dulip, Withanage. 2020. OJS Software Workshop Report, Heidelberg, Germany. doi: 10.11588/heidok.00028242.
• Nüst, Daniel, Sochat, Vanessa, Marwick, Ben, Eglen, Stephen, Head, Tim, Hirst, Tony, and Evans, Benjamin. 2020. Ten Simple Rules for Writing Dockerfiles for Reproducible Data Science, OSF Preprints. doi: 10.31219/osf.io/fsd7t.
• Nüst, D, Eddelbuettel, D, Bennett, D, Cannoodt, R, Clark, D, Daroczi, G, Edmondson, M, Fay, C, Hughes, E, Lopp, S, Marwick, B, Nolis, H, Nolis, J, Ooi, H, Ram, K, Ross, N, Shepherd, L, Turaga, N, Willis, C, Xiao, N, and Petegem, CV. 2020. The Rockerverse: Packages and Applications for Containerization with R, arXiv.
• Konkol, M, Nüst, D, and Goulier, L. 2020. Publishing computational research - A review of infrastructures for reproducible and transparent scholarly communication, arXiv.

2019

• Maus, V, Camara, G, Appel, M, and 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, and Nüst, D. 2019. “Reproducible Research in Geoinformatics: Concepts, Challenges and Benefits (Vision Paper).” in 14th International Conference on Spatial Information Theory (COSIT 2019), Vol. 142 of Leibniz International Proceedings in Informatics (LIPIcs), edited by S Timpf, C Schlieder, M Kattenbeck, B Ludwig and K Stewart. Wadern: Dagstuhl Publishing. doi: 10.4230/LIPIcs.COSIT.2019.8.
• Appel, M, and 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, and Pebesma, E. 2019. “Herders Mobility GPS Tracking:Insights From Novel Trajectory Data.” in Conférence internationale "Systèmes Complexes, Intelligence Territorialeet Mobilité", edited by P Sajous and C Bertelle.
• Eglen, Stephen, and Nüst, Daniel. 2019. “CODECHECK: An open-science initiative to facilitate sharing of computer programs and results presented in scientific publications.” contribution to the The 14th Munin Conference on Scholarly Publishing 2019, UiT The Arctic University of Norway, Tromsø, Norway doi: 10.7557/5.4910.
• Nüst, D, and Hinz, M. 2019. “containerit: Generating Dockerfiles for reproducible research with R.” Journal of Open Source Software, № 4 (40): 1603. doi: 10.21105/joss.01603.
• Nüst, D, Ostermann, FO, Sileryte, R, Hofer, B, Granell, C, Teperek, M, Graser, A, Broman, K, and Hettne, K. 2019. AGILE Reproducible Paper Guidelines, OSF. doi: 10.17605/OSF.IO/CB7Z8.
• Nüst, Daniel, Lohoff, Lukas, Einfeldt, Lasse, Gavish, Nimrod, Götza, Marlena, Tariq, Jaswal Shahzeib, Khalid, Salman, Meierkort, Laura, Mohr, Matthias, Rendel, Clara, and Antonia, van Eek. 2019. “Guerrilla Badges for Reproducible Geospatial Data Science.” in Accepted Short Papers and Posters from the 22nd AGILE Conference on Geo‐information Science, edited by P Kyriakidis, D Hadjimitsis, D Skarlatos and A Mansourian. Heidelberg: Springer. doi: 10.31223/osf.io/xtsqh.
• Giri, S, Hazra, S, Ghosh, P, Ghosh, A, Das, S, Chanda, A, Das, I, Chakraborty, K, Mukhopadhyay, A, and Maity, S. 2019. “Role of lunar phases, rainfall, and wind in predicting Hilsa shad (Tenualosa ilisha) catch in the northern Bay of Bengal.” Fisheries Oceanography, № 28 (5): 567–575. doi: 10.1111/fog.12430.
• Nüst, Daniel. 2019. “Packaging geoscience research for transparency and reproducibility: approaches and examples.” contribution to the EGU General Assembly, Vienna, Austria doi: 10.5281/zenodo.2630483.

2018

• Nüst, Daniel, Boettiger, Carl, and Marwick, Ben. 2018. How to Read a Research Compendium, https://arxiv.org/abs/1806.09525: arXiv.
• Pebesma, E, Appel, M, and Lahn, F. 2018. “R vector and raster data cubes for openEO.” contribution 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, and Kempeneers, P. 2018. “openEO: an open API for cloud-based big Earth Observation processing platforms.” contribution to the EGU General Assembly 2018, Vienna, Austria
• Appel Marius, Lahn Florian, and 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, and 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.
• Ghosh, P, and Mukhopadhyay, A. 2018. “A modified equation to calculate the potential future shoreline position using transects.” contribution to the European Geosciences Union General Assembly 2018, Vienna, Austria
• Lu, M, Appel, M, and 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., and Pebesma, E. 2018. “Running user-defined functions in R on Earth observation data in cloud back-ends.” contribution to the 10th Geomundus conference, Lisbon, Portugal
• Nüst, Daniel, Schutzeichel, Marc, and Konkol, Markus. 2018. Opening Reproducible Research: a research project website and blog, doi: 10.5281/zenodo.1485437.
• Granell Carlos, Nüst Daniel, and Ostermann Frank O, Sileryte Rusne. 2018. “Reproducible Research is like riding a bike.” PeerJ Preprints, № 6 (e27216v1) doi: 10.7287/peerj.preprints.27216v1.
• Nüst, Daniel. 2018. Reproducibility Service for Executable Research Compendia: Technical Specifications and Reference Implementation, doi: 10.5281/zenodo.2203844.
• Chanda, A, Das, S, Mukhopadhyay, A, Ghosh, A, Akhand, A, Ghosh, P, Ghosh, T, Mitra, D, and Hazra, S. 2018. “Sea surface temperature and rainfall anomaly over the Bay of Bengal during the El Niño-Southern Oscillation and the extreme Indian Ocean Dipole events between 2002 and 2016.” Remote Sensing Applications: Society and Environment, № 12: 10–22. doi: 10.1016/j.rsase.2018.08.001.
• Mukhopadhyay, A, Ghosh, P, Chanda, A, Ghosh, A, Ghosh, S, Das, S, Ghosh, T, and Hazra, S. 2018. “Threats to coastal communities of Mahanadi delta due to imminent consequences of erosion – Present and near future.” Science of the Total Environment, № 637-638: 717–729. doi: 10.1016/j.scitotenv.2018.05.076.
• Nüst, Daniel, Granell, Carlos, Hofer, Barbara, Konkol, Markus, Ostermann, Frank O, Sileryte, Ruse, and Cerutti, Valentina. 2018. “Reproducible research and GIScience: an evaluation using AGILE conference papers.” PeerJ, № 6: e5072. doi: 10.7717/peerj.5072.
• Gupta Shivam, Pebesma Edzer, and Mateu Jorge, Degbelo Auriol. 2018. “Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models.” Sustainability, № 10 (5) doi: 10.3390/su10051442.
• Nüst, Daniel, and Bartoschek, Thomas. 2018. “Open Environmental Data Analysis.” contribution to the EGU General Assembly 2018, Vienna, Austria doi: 10.5281/zenodo.1217911.
• Nüst, Daniel, Boettiger, Carl, and Eddelbuettel, Dirk. 2018. “rocker/geospatial: a flexible runtime environment for geoscientific data analysis.” contribution to the EGU General Assembly 2018, Vienna, Austria doi: 10.5281/zenodo.1216750.
• Gupta, Shivam, Mateu, Jorge, Degbelo, Auriol, and 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.
• Nüst, Daniel, Granell, Carlos, Hofer, Barbara, Konkol, Markus, Ostermann, Frank, Sileryte, Rusne, and Cerutti, Valentina. 2018. Peerj Preprints, Vol. 6:e26561v1, Reproducible research and GIScience: an evaluation using AGILE conference papers, doi: 10.7287/peerj.preprints.26561v1.

2017

• Nanki, Sidhu, Edzer, Pebesma, and Yi-Chen, Wang. 2017. “Usability Study to Assess the IGBP Land Cover Classification for Singapore.” Remote Sensing, № 2017 (9(10), 1075) doi: 10.3390/rs9101075.
• Nüst, Daniel. 2017. “Executable research compendia in geoscience research infrastructures.” in Vol. EGU2017-7215 of Geophysical Research Abstracts
• Knoth, C, and Nüst, D. 2017. “Reproducibility and Practical Adoption of GEOBIA with Open-Source Software in Docker Containers.” Remote Sensing, № 9 (3) doi: 10.3390/rs9030290.
• Lu, Meng, Appel, Marius, and Pebesma, Edzer. 2017. “Modelling spatiotemporal change using multidimensional arrays.” contribution to the EGU General Assembly 2017, Vienna, Austria
• Marius, Appel, Daniel, Nüst, and Edzer, Pebesma. 2017. “Reproducible Earth observation analytics: challenges, ideas, and a study case on containerized land use change detection.” contribution to the EGU General Assembly 2017, Vienna, Austria
• gupta, Shivam, Pebesma, Edzer, and Mateu, Jorge. 2017. “Air quality monitoring network location optimization for robust Land Use Regression Model.” contribution to the Spatial Statistics 2017: One World: One Health, Lancaster Elsevier.
• Roy, A., and Pebesma, E. 2017. “A machine learning approach to demographic prediction using geohashes.” in Vol. null of International Workshop on Social Sensing New York, NY: ACM Press. doi: 10.1145/3055601.3055603.
• Mariethoz, G., and 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., and Pebesma, E. 2017. “The GRASS GIS temporal framework.” International Journal of Geographical Information Science, № 31 (7): 1273–1292. doi: 10.1080/13658816.2017.1306862.
• Lu, Meng, Hamunyela, Eliakim, Verbesselt, Jan, and Pebesma, Edzer. 2017. “Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring.” Remote Sensing, № 9 (10) doi: 10.3390/rs9101025.
• Nüst, Daniel, and Schutzeichel, Marc. 2017. “An Architecture for Reproducible Computational Geosciences.” in Societal Geo-Innovation doi: 10.5281/zenodo.1478542.
• Markus, Konkol, Daniel, Nüst, Marc, Schutzeichel, Edzer, Pebesma, Christian, Kray, Holger, Przibytzin, and Jörg, Lorenz. 2017. “Opening reproducible research (o2r).” contribution to the Open Science Conference, Berlin, Germany
• Ghosh, P, Mukhopadhyay, A, Chanda, A, Mondal, P, Akhand, A, Mukherjee, S, Nayak, SK, Ghosh, S, Mitra, D, Ghosh, T, and Hazra, S. 2017. “Application of Cellular automata and Markov-chain model in geospatial environmental modeling- A review.” Remote Sensing Applications: Society and Environment, № 5: 64–77. doi: 10.1016/j.rsase.2017.01.005.
• Nüst, D, Konkol, M, Pebesma, E, Kray, C, Schutzeichel, M, Przibytzin, H, and Lorenz, J. 2017. “Opening the Publication Process with Executable Research Compendia.” D-Lib Magazine, № 23 doi: 10.1045/january2017-nuest.
• Knoth, C, and 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.

2016

• Gräler, B., Pebesma, E., and Heuvelink, G. 2016. “Spatio-temporal interpolation using gstat.” R Journal, № 8 (1): 204–218.
• Knoth Christian, Nüst Daniel. 2016. “Enabling Reproducible OBIA with Open-Source Software in Docker Containers.” contribution to the GEOBIA 2016: Solutions & Synergies, Enschede, The Netherlands doi: 10.3990/2.456.
• Stasch, Christoph, and Nüst Daniel, Pross Benjamin. 2016. “Sharing environmental models: An Approach using GitHub repositories and Web Processing Services.” in Vol. 18 of Geophysical Research Abstracts, edited by European Geophysical Union.
• Nüst, Daniel, Konkol, Markus, Pebesma, Edzer, Kray, Christian, Klötgen, Stephanie, Schutzeichel, Marc, Lorenz, Jörg, Przibytzin, Holger, and Kussmann, Dirk. 2016. “Opening Reproducible Research.” in Vol. 18 of Geophysical Research Abstracts, edited by European Geophysical Union.
• Scheider, S., Gräler, B., Pebesma, E., and Stasch, C. 2016. “Modeling spatiotemporal information generation.” International Journal of Geographical Information Science, № null (null): 1–29. doi: 10.1080/13658816.2016.1151520.
• Pebesma, E., Mailund, T., and Hiebert, J. 2016. “Measurement units in R.” R Journal, № 8 (2): 490–498.
• Marius, Appel, Florian, Lahn, Edzer, Pebesma, Wouter, Buytaert, and Simon, Moulds. 2016. “Scalable Earth-observation Analytics for Geoscientists: Spacetime Extensions to the Array Database SciDB.” contribution to the EGU General Assembly 2016, Vienna, Austria
• Lu, Meng, Pebesma, Edzer, Sanshaz, Alber, and 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)

2015

• Helle, K., and 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., and 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, and Edzer, Pebesma. 2015. “Spatio-temporal change modeling with array data.” contribution to the EGU 2015, Vienna, Austria
• Lu, M., Pebesma, E., Sanchez, A., and 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., and 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.
• Nüst, Daniel, Václavík, Tomáš, and Pross, Benjamin. 2015. “Open and reproducible global land use classification.” in EGU General Assembly Conference Abstracts, Vol. 17 of Geophysical Research Abstracts
• Hengl, T., Pebesma, E., and 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.
• Appel, Marius, Pebesma, Edzer, and Camara, Gilberto. 2015. “Scalable In-Database Regression Analysis of Large Earth-Observation Datasets.” contribution to the EO Open Science 2.0, Frascati, Italy
• Pebesma, E., R., Bivand, and P.J., Ribeiro. 2015. “Software for Spatial Statistics.” Journal of Statistical Software, № 63 (1)
• Hengl, T., Roudier, P., Beaudette, D., and Pebesma, E. 2015. “plotKML: Scientific Visualization of Spatio-Temporal Data.” Journal of Statistical Software, № 63 (5)

2014

• Skoien, J.O., Bloschl, G., Laaha, G., Pebesma, E., Parajka, J., and 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., and 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., and 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, and Pebesma, E. 2014. “Detecting Destruction in Conflict Areas in Darfur.” contribution to the GEOBIA 2014 - Geographic Object Based Image Analysis, Thessaloniki, Greece
• Kilibarda, M, Hengl, T, Heuvelink, GBM, Gräler, B, Pebesma, E, Perčec, Tadić M, and 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.
• B., Graler. 2014. “Modelling skewed spatial random fields through the spatial vine copula.” Spatial Statistics, № na doi: 10.1016/j.spasta.2014.01.001.
• Gebbert, S., and 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., and 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., and Pebesma, E. 2014. “enviroCar – Open car data and open analysis tools for sustainable transportation development.” contribution to the 2nd International Conference ICT for Sustainability, Stockholm, Schweden
• Meng, Lu, and Edzer, Pebesma. 2014. “Modeling change from large-scale high-dimensional spatio-temporal array data.” contribution to the EGU 2014, Vienna, Austria

2013

• Gerharz, LE, and 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, and 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, and 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.
• Stasch, C., Pebesma, E., Graeler, B., and Gerharz, L. 2013. “Error-aware spatio-temporal aggregation in the model web.” in Vol. null of AGILE Conference on Geographic Information Science Dordrecht: 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., and Santos, N.C.P. 2013. “Spatial statistic to assess remote sensing acreage estimates: An analysis of sugarcane in São Paulo State, Brazil.” in Vol. null doi: 10.1109/IGARSS.2013.6723768.
• Lemke, D., Mattauch, V., Heidinger, O., Pebesma, E., and 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.
• García, Millán V E, Müterthies, A, Pakzad, K, Teuwsen, S, Benecke, N, Zimmermann, K, Kateloe, H, Preuße, A, Helle, K, and Knoth, C. 2013. “GMES4Mining: GMES-based Geoservices for Mining Areas.” Markscheidewesen, № 120 (3): 17–24.
• Knoth, C, Klein, B, Prinz, T, and Kleinebecker, T. 2013. “Unmanned Aerial Vehicles as innovative remote sensing platforms for high-resolution infrared imagery to support restoration monitoring in cut-over bogs.” Applied Vegetation Science, № 16 (3): 509–517. doi: 10.1111/avsc.12024.
• Bivand, R., Pebesma, E., and Gómez-Rubio, V. 2013. Applied Spatial Data Analysis with R: Second Edition,, edited by Bivand Roger, Pebesma Edzer and Gomez-Rubio Virgilio. Heidelberg: Springer. doi: 10.1007/978-1-4614-7618-4.
• Mello, M., Risso, J., Atzberger, C., Aplin, P., Pebesma, E., Vieira, C., and 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., and Pebesma, E. 2013. “Measuring allocation errors in land change models in amazonia.” in Vol. null N/A: Selbstverlag / Eigenverlag.
• Brink, Juliane, and Pebesma, Edzer. 2013. “Plume Tracking with a Mobile Sensor Based on Incomplete and Imprecise Information.” Transactions in GIS, № 2013 doi: 10.1111/tgis.12063.
• Gräler, B, vandenBerg, MJ, Vandenberghe, S, Petroselli, A, Grimaldi, S, DeBaets, B, and Verhoest, NEC. 2013. “Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation.” Hydrology and Earth System Sciences, № 17: 1281–1296. doi: 10.5194/hess-17-1281-2013.

2012

• Hengl, T, Heuvelink, GBM, Tadić, MP, and 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, and 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, and 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, and 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, and Stein, A. 2012. “Spatial statistics for mapping the environment.” The ITC Journal
• Pross, B., Gerharz, L., Stasch, C., and Pebesma, E. 2012. “Tools for uncertainty propagation in the model web using Monte Carlo simulation.” in Vol. null
• Tomi, Kauppinen, Jim, Jones, Alber, Sanchez, Giovana, Mira de Espindola, and Thomas, Bartoschek. 2012. “Linked Brazilian Amazon Rainforest Data.” Semantic Web Journal, № 2012
• Pebesma, E. 2012. “spacetime: Spatio-Temporal Data in R.” Journal of Statistical Software, № 51 (7)
• Helle, Kristina, and Pebesma, Edzer. 2012. “Stationary Sampling Designs Based on Plume Simulations.” in Spatio-Temporal Design, edited by Wiley. New York City: John Wiley & Sons. doi: 10.1002/9781118441862.ch14.
• Pross, Benjamin, Gerharz, Lydia, Stasch, Christoph, and 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, edited by R Seppelt, A Voinov, S Lange and D Bankamp. Leipzig.
• Helle, K., and Pebesma, E. 2012. “Stationary Sampling Designs Based on Plume Simulations.” in Spatio-temporal Design: Advances in Efficient Data Acquisition, Vol. null , edited by Mueller Werner Mateu Jorge. New York City: John Wiley & Sons. doi: 10.1002/9781118441862.ch14.
• Schulz, M., Skøien, J., Gerharz, L., Dubois, G., and Pebesma, E. 2012. “Uncertainty propagation between web services - A case study using the eHabitat WPS to identify unique ecosystems.” in Vol. null
• Nüst, Daniel. 2012. Accessing Data from Sensor Observation Services: the sos4R Package,
• Benedikt, Gräler, Lydia, Gerharz, and Edzer, Pebesma. 2012. “Spatio-temporal analysis and interpolation of PM10 measurements in Europe.” online.

2011

• Nust, D, Stasch, C, and 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, and 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, and 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, and 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, and 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, and 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, and 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, and Fonseca, L. 2011. “Agricultural land use dynamics in the Brazilian Amazon based on remote sensing and census data.” Applied Geography, № 32 (2): 240–252.
• Bröring, A, Maué, P, Janowicz, K, Nüst, D, and Malewski, C. 2011. “Semantically-enabled sensor Plug & Play for the Sensor Web.” Sensors, № 11 (8): 7568–7605. doi: 10.3390/s110807568.
• Knoth, C, Prinz, T, and Loef, P. 2011. “Microcopter-Based Colour Infrared (CIR) Close Range Remote Sensing as a Subsidiary Tool for Precision Farming.” in Proceedings of the Workshop on Remote Sensing Methods for Change Detection and Process Modelling
• Helle, Kristina B, Astrup, Poul, Raskob, Wolfgang, and 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, edited by Fonte Cidalia C, Goncalves Luisa and Goncalves Gil.
• Helle Kristina B., Urso Laura, Astrup Poul, Mikkelsen Torben, Kaiser Jan C., Pebesma Edzer, Rojas-Palma Carlos, Holo Eldri, and Dyve Jan E., 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, Vol. 46 (6) of Radioprotection, edited by J-C Barescut, D Lariviere and T Stocki. Les Ulis: EDP Sciences. doi: 10.1051/radiopro/20116628s.
• Gerharz, Lydia, Gräler, Benedikt, and 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.
• Daniel, Nüst, Christoph, Stasch, and Edzer, Pebesma. 2011. “Connecting R to the Sensor Web.” in Advancing Geoinformation Science for a Changing World, Springer Lecture Notes in Geoinformation and Cartography, edited by S Geertman, W Reinhardt and F Toppen. Heidelberg: Springer.
• Fairgrieve, S., Stasch, C., Falke, S., Gerharz, L., and Pebesma, E. 2011. “Error aware near real-time interpolation of air quality observations in GEOSS.” in Vol. 712
• Schwering, A., Pebesma, E., and Behnke, K., eds. 2011. ifgi prints no 41, Conference Proceedings Geoinformatik 2011, Berlin: Akademische Verlagsgesellschaft.
• Knoth, C, Klein, B, Kleinebecker, T, Prinz, T, and Sieg, B. 2011. “UAV-Based Close Range Remote Sensing as a Monitoring Tool for the Restoration of Bog Ecosystems.” in Proceedings of the Joint Meeting of Society of Wetland Scientists, WETPOL and Wetland Biogeochemistry Symposium 2011
• Geipel, J, Knoth, C, Elsässer, O, and Prinz, T. 2011. “DGPS- and INS-Based Orthophotogrammetry on Micro UAV Platforms for Precision Farming Services.” in Proceedings of the GEOINFORMATIK 2011 – GEOCHANGE

2010

• Sluiter, R, and 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, and 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, and M, Heuvelink GB. 2010. “Identifying and removing heterogeneities between monitoring networks.” Environmetrics, № 21 (1): 66–84.
• de Nijs, T, and 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., and Stasch, C. 2010. “The uncertainty enabled model web (UncertWeb).” in Vol. 679
• Helle, Kristina B., and 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, edited by Nicholas J. Tate and Peter F. Fisher.
• Helle, Kristina B., and Pebesma, Edzer. 2010. “Optimizing Spatio-Temporal Sampling Designs of Synchronous, Static, or Clustered Measurements.” contribution to the European Geosciences Union General Assembly, Vienna
• Jirka, S, Nüst, D, Schulte, J, and Houbie, F. 2010. “Integrating the OGC Sensor Web Enablement Framework into the OGC Catalogue.” in WebMGS 2010: 1st International Workshop on Pervasive Web Mapping, Geoprocessing and Services. 26.-27. August 2010. Como, Italy.
• Jirka, S, Bröring, A, and Nüst, D. 2010. OGC Discussion Paper 09-112r1 - Sensor Observable Registry, Open Geospatial Consortium.
• Gerharz, L., Pebesma, E., and Hecking, H. 2010. “Visualizing uncertainty in spatio-temporal data.” in Vol. null of International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences N/A: Selbstverlag / Eigenverlag.
• Helle, K., and Pebesma, E. 2010. “Conservative updating of sampling designs.” in Vol. null of International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences N/A: Selbstverlag / Eigenverlag.
• Gerharz, L, Pebesma, E, and 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, edited by NJ Tate and PF Fisher.
• Gräler, Benedikt, Kazianka, Hannes, and deEspindola, Giovana. 2010. “Copulas, a novel approach to model spatial and spatio-temporal dependence.” GIScience for Environmental Change Symposium Proceedings, № 2010 (40): 49–54.
• Jirka, Simon, and Nüst, Daniel. 2010. OGC Discussion Paper 10-171: Sensor Instance Registry, Open Geospatial Consortium.
• Jirka, Simon, Bröring, Arne, and Nüst, Daniel. 2010. OGC Discussion Paper 09-112r1 - Sensor Observable Registry, Open Geospatial Consortium.

2009

• Hiemstra, PH, Pebesma, EJ, Twenhöfel, CJW, and 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, and 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.
• E.J., Pebesma. 2009. “How we build geostatistical models and deal with their output.” in Interfacing Geostatistics and GIS, Vol. null , edited by Springer. Düsseldorf: Springer VDI Verlag. doi: 10.1007/978-3-540-33236-7_1.
• Dubois, G., De Jesus, J., Doherty, B., Cornford, D., and Pebesma, E. 2009. “Lessons learned from INTAMAP, an interoperable web service for the real-time interpolation of environmental variables.” in Vol. null
• Houbie, F, Skivee, F, Robin, A, Jirka, S, Bröring, A, and Nüst, D. 2009. “OGC Discussion Paper 09-163 - OGC Catalogue Services Specification 2.0 - Extension Package for ebRIM Application Profile: SensorML.” Wayland, MA: Open Geospatial Consortium.
• Gerharz, L, and Pebesma, E. 2009. “Usability of interactive and non-interactive visualisation of uncertain geospatial information.” in Geoinformatik 2009 Konferenzband, Vol. 35 of ifgiprints, edited by W Reinhardt, A Krüger and M Ehlers.
• Houbie, F, Skivee, F, Robin, A, Jirka, S, Bröring, A, and Nüst, D. 2009. OGC Discussion Paper 09-163 - OGC Catalogue Services Specification 2.0 - Extension Package for ebRIM Application Profile: SensorML, Open Geospatial Consortium.

2008

• Skøien, JO, Pebesma, EJ, and Blöschl, G. 2008. “Geostatistics for automatic estimation of environmental variables-some simple solutions.” Georisk, № 2 (4): 257–270.
• ter, Braak C JF, Brus, DJ, and 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, and Gomez-Rubio, V. 2008. UseR!, Applied Spatial Data Analysis with R, Düsseldorf: Springer VDI Verlag.
• Pebesma, Edzer, Bishr, Mohammed, and Bartoschek, Thomas, eds. 2008. ifgiPrints, Vol. 32, Proceedings of the 6th Geographic Information Days, 400. edt. N/A: unbekannt / n.a. / unknown.

2007

• Addink, EA, De Jong, SM, and 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, and 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, and 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, and 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, and 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.

2006

• EJ, Pebesma. 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., and De Jong, K. 2006. “Dynamic visualisation of spatial and spatio-temporal probability distribution functions.” in Vol. null of International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences N/A: Selbstverlag / Eigenverlag.

2005

• Pebesma, EJ, Duin, RNM, and 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.
• EJ, Pebesma. 2005. “Mapping radioactivity from monitoring data: Automating the classical geostatistical approach.” Applied GIS, № 1 (2)
• Pebesma, EJ, Switzer, P, and 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.

2004

• EJ, Pebesma. 2004. “Multivariable geostatistics in S: The gstat package.” Computers and Geosciences, № 30 (7): 683–691. doi: 10.1016/j.cageo.2004.03.012.

2003

• Pfeffer, K, Pebesma, EJ, and 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, and 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.

2002

• Van Horssen, PW, Pebesma, EJ, and 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, and 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.

2001

• De Wit, MJM, and 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, and 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.

1999

• Heuvelink, GBM, Musters, P, and 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, and 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, and 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, and 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, and 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.

1998

• Pebesma, EJ, and 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.

1997

• Pebesma, EJ, and de 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.

• Milà, C, Ludwig, M, Pebesma, E, Tonne, C, and 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, and Pebesma, Edzer. 2024. “Standardizing Machine Learning APIs for Earth Observation Data Cubes.” contribution to the The 5th Spatial Data Science, Online doi: https://doi.org/10.5281/zenodo.13960237.
• Ludwig, M, Moreno-Martinez, A, Hölzel, N, Pebesma, E, and 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, and Kraehnert, K. 2023. “Allocation of humanitarian aid after a weather disaster.” World Development, № 166: 106204. doi: 10.1016/j.worlddev.2023.106204.
• Mila, C, Mateu, J, Pebesma, E, and 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, and 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, and Meyer, H. 2022. “Developing Transferable Spatial Prediction Models: a Case Study of Satellite Based Landcover Mapping.” contribution 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, and 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, and 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.
• Heisig, J., Olson, E., and 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.
• Meyer, H, and 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, and 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.
• Teickner, H, Knoth, C, Bartoschek, T, Kraehnert, K, Vigh, M, Purevtseren, M, Sugar, M, and 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, and Pebesma, Edzer. 2020. “Spatiotemporal multi-resolution approximations for analyzing global environmental data.” Spatial Statistics, № 38 doi: 10.1016/j.spasta.2020.100465.
• Nüst, Daniel, and Pebesma, Edzer. 2020. “Practical reproducibility in geography and geosciences.” Annals of the American Association of Geographers, № 2020 doi: 10.1080/24694452.2020.1806028.
• Maus, V, Camara, G, Appel, M, and 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, and Nüst, D. 2019. “Reproducible Research in Geoinformatics: Concepts, Challenges and Benefits (Vision Paper).” in 14th International Conference on Spatial Information Theory (COSIT 2019), Vol. 142 of Leibniz International Proceedings in Informatics (LIPIcs), edited by S Timpf, C Schlieder, M Kattenbeck, B Ludwig and K Stewart. Wadern: Dagstuhl Publishing. doi: 10.4230/LIPIcs.COSIT.2019.8.
• Appel, M, and 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, and Pebesma, E. 2019. “Herders Mobility GPS Tracking:Insights From Novel Trajectory Data.” in Conférence internationale "Systèmes Complexes, Intelligence Territorialeet Mobilité", edited by P Sajous and C Bertelle.
• Pebesma, E, Appel, M, and Lahn, F. 2018. “R vector and raster data cubes for openEO.” contribution 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, and Kempeneers, P. 2018. “openEO: an open API for cloud-based big Earth Observation processing platforms.” contribution to the EGU General Assembly 2018, Vienna, Austria
• Appel, Marius, Lahn, Florian, Buytaert, Wouter, and 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, and 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, and 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., and Pebesma, E. 2018. “Running user-defined functions in R on Earth observation data in cloud back-ends.” contribution to the 10th Geomundus conference, Lisbon, Portugal
• Gupta Shivam, Pebesma Edzer, and Mateu Jorge, 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, Degbelo, Auriol, and 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.
• Nanki, Sidhu, Edzer, Pebesma, and Yi-Chen, Wang. 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, and Pebesma, Edzer. 2017. “Modelling spatiotemporal change using multidimensional arrays.” contribution to the EGU General Assembly 2017, Vienna, Austria
• Appel, Marius, Nüst, Daniel, and Pebesma, Edzer. 2017. “Reproducible Earth observation analytics: challenges, ideas, and a study case on containerized land use change detection.” contribution to the EGU General Assembly 2017, Vienna, Austria
• gupta, Shivam, Pebesma, Edzer, and Mateu, Jorge. 2017. “Air quality monitoring network location optimization for robust Land Use Regression Model.” contribution to the Spatial Statistics 2017: One World: One Health, Lancaster Elsevier.
• Roy, A., and Pebesma, E. 2017. “A machine learning approach to demographic prediction using geohashes.” in Vol. null of International Workshop on Social Sensing New York, NY: ACM Press. doi: 10.1145/3055601.3055603.
• Mariethoz, G., and 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., and Pebesma, E. 2017. “The GRASS GIS temporal framework.” International Journal of Geographical Information Science, № 31 (7): 1273–1292. doi: 10.1080/13658816.2017.1306862.
• Lu, Meng, Hamunyela, Eliakim, Verbesselt, Jan, and Pebesma, Edzer. 2017. “Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring.” Remote Sensing, № 9 (10) doi: 10.3390/rs9101025.
• Markus, Konkol, Daniel, Nüst, Marc, Schutzeichel, Edzer, Pebesma, Christian, Kray, Holger, Przibytzin, and Jörg, Lorenz. 2017. “Opening reproducible research (o2r).” contribution to the Open Science Conference, Berlin, Germany
• Nüst, D, Konkol, M, Pebesma, E, Kray, C, Schutzeichel, M, Przibytzin, H, and Lorenz, J. 2017. “Opening the Publication Process with Executable Research Compendia.” D-Lib Magazine, № 23 doi: 10.1045/january2017-nuest.
• Gräler, B., Pebesma, E., and Heuvelink, G. 2016. “Spatio-temporal interpolation using gstat.” R Journal, № 8 (1): 204–218.
• Daniel, Nüst, Markus, Konkol, Edzer, Pebesma, Christian, Kray, Stephanie, Klötgen, Marc, Schutzeichel, Jörg, Lorenz, Holger, Przibytzin, and Dirk, Kussmann. 2016. “Opening Reproducible Research.” in Vol. 18 of Geophysical Research Abstracts, edited by European Geophysical Union.
• Scheider, S., Gräler, B., Pebesma, E., and Stasch, C. 2016. “Modeling spatiotemporal information generation.” International Journal of Geographical Information Science, № null (null): 1–29. doi: 10.1080/13658816.2016.1151520.
• Pebesma, E., Mailund, T., and Hiebert, J. 2016. “Measurement units in R.” R Journal, № 8 (2): 490–498.
• Knoth, C, and 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.
• Marius, Appel, Florian, Lahn, Edzer, Pebesma, Wouter, Buytaert, and Simon, Moulds. 2016. “Scalable Earth-observation Analytics for Geoscientists: Spacetime Extensions to the Array Database SciDB.” contribution to the EGU General Assembly 2016, Vienna, Austria
• Lu Meng, Pebesma Edzer, and Sanshaz Alber, 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)
• Meng, Lu, and Edzer, Pebesma. 2015. “Spatio-temporal change modeling with array data.” contribution to the EGU 2015, Vienna, Austria
• Lu, M., Pebesma, E., Sanchez, A., and 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., and 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.
• Hengl, T., Pebesma, E., and 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., and 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., and 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.
• Marius, Appel, Edzer, Pebesma, and Gilberto, Camara. 2015. “Scalable In-Database Regression Analysis of Large Earth-Observation Datasets.” contribution to the EO Open Science 2.0, Frascati, Italy
• Pebesma, E., R., Bivand, and P.J., Ribeiro. 2015. “Software for Spatial Statistics.” Journal of Statistical Software, № 63 (1)
• Hengl, T., Roudier, P., Beaudette, D., and Pebesma, E. 2015. “plotKML: Scientific Visualization of Spatio-Temporal Data.” Journal of Statistical Software, № 63 (5)
• Stasch, C., Nüst, D., Rieke, M., Remke, A., and Pebesma, E. 2014. “enviroCar – Open car data and open analysis tools for sustainable transportation development.” contribution to the 2nd International Conference ICT for Sustainability, Stockholm, Schweden
• Meng, Lu, and Edzer, Pebesma. 2014. “Modeling change from large-scale high-dimensional spatio-temporal array data.” contribution to the EGU 2014, Vienna, Austria
• Skoien, J.O., Bloschl, G., Laaha, G., Pebesma, E., Parajka, J., and 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., and 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., and 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, and Pebesma, E. 2014. “Detecting Destruction in Conflict Areas in Darfur.” contribution to the GEOBIA 2014 - Geographic Object Based Image Analysis, Thessaloniki, Greece
• Kilibarda, M, Hengl, T, Heuvelink, GBM, Gräler, B, Pebesma, E, Perčec, Tadić M, and 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., and 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., and Kuhn, W. 2014. “Meaningful spatial prediction and aggregation.” Environmental Modelling and Software, № 51 (null): 149–165. doi: 10.1016/j.envsoft.2013.09.006.
• Gerharz, LE, and 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, and 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, and 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.
• Bivand, R., Pebesma, E., and Gómez-Rubio, V. 2013. Applied Spatial Data Analysis with R: Second Edition,, edited by Bivand Roger, Pebesma Edzer and Gomez-Rubio Virgilio. Heidelberg: Springer. doi: 10.1007/978-1-4614-7618-4.
• Mello, M., Risso, J., Atzberger, C., Aplin, P., Pebesma, E., Vieira, C., and 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., and Pebesma, E. 2013. “Measuring allocation errors in land change models in amazonia.” in Vol. null N/A: Selbstverlag / Eigenverlag.
• Stasch, C., Pebesma, E., Graeler, B., and Gerharz, L. 2013. “Error-aware spatio-temporal aggregation in the model web.” in Vol. null of AGILE Conference on Geographic Information Science Dordrecht: 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., and Santos, N.C.P. 2013. “Spatial statistic to assess remote sensing acreage estimates: An analysis of sugarcane in São Paulo State, Brazil.” in Vol. null doi: 10.1109/IGARSS.2013.6723768.
• Lemke, D., Mattauch, V., Heidinger, O., Pebesma, E., and 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.
• Brink, Juliane, and Pebesma, Edzer. 2013. “Plume Tracking with a Mobile Sensor Based on Incomplete and Imprecise Information.” Transactions in GIS, № 2013 doi: 10.1111/tgis.12063.
• Hengl, T, Heuvelink, GBM, Tadić, MP, and 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, and 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, and 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, and 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, and Stein, A. 2012. “Spatial statistics for mapping the environment.” The ITC Journal
• Helle, K., and Pebesma, E. 2012. “Stationary Sampling Designs Based on Plume Simulations.” in Spatio-temporal Design: Advances in Efficient Data Acquisition, Vol. null , edited by Mueller Werner Mateu Jorge. New York City: John Wiley & Sons. doi: 10.1002/9781118441862.ch14.
• Schulz, M., Skøien, J., Gerharz, L., Dubois, G., and Pebesma, E. 2012. “Uncertainty propagation between web services - A case study using the eHabitat WPS to identify unique ecosystems.” in Vol. null
• Pross, B., Gerharz, L., Stasch, C., and Pebesma, E. 2012. “Tools for uncertainty propagation in the model web using Monte Carlo simulation.” in Vol. null
• Pebesma, E. 2012. “spacetime: Spatio-Temporal Data in R.” Journal of Statistical Software, № 51 (7)
• Helle, Kristina, and Pebesma, Edzer. 2012. “Stationary Sampling Designs Based on Plume Simulations.” in Spatio-Temporal Design, edited by Wiley. New York City: John Wiley & Sons. doi: 10.1002/9781118441862.ch14.
• Pross, Benjamin, Gerharz, Lydia, Stasch, Christoph, and 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, edited by R Seppelt, A Voinov, S Lange and D Bankamp. Leipzig.
• Gräler, Benedikt, Gerharz, Lydia, and Pebesma, Edzer. 2012. “Spatio-temporal analysis and interpolation of PM10 measurements in Europe.” online.
• Nust, D, Stasch, C, and 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, and 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, and 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, and 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, and 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, and 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, and 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, and Fonseca, L. 2011. “Agricultural land use dynamics in the Brazilian Amazon based on remote sensing and census data.” Applied Geography, № 32 (2): 240–252.
• Gerharz, Lydia, Gräler, Benedikt, and 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, and 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.
• Fairgrieve, S., Stasch, C., Falke, S., Gerharz, L., and Pebesma, E. 2011. “Error aware near real-time interpolation of air quality observations in GEOSS.” in Vol. 712
• Schwering, A., Pebesma, E., and Behnke, K., eds. 2011. ifgi prints no 41, Conference Proceedings Geoinformatik 2011, Berlin: Akademische Verlagsgesellschaft.
• Helle, Kristina B, Astrup, Poul, Raskob, Wolfgang, and 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, edited by Fonte Cidalia C, Goncalves Luisa and Goncalves Gil.
• Helle Kristina B., Urso Laura, Astrup Poul, Mikkelsen Torben, Kaiser Jan C., Pebesma Edzer, Rojas-Palma Carlos, Holo Eldri, and Dyve Jan E., 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, Vol. 46 (6) of Radioprotection, edited by J-C Barescut, D Lariviere and T Stocki. Les Ulis: EDP Sciences. doi: 10.1051/radiopro/20116628s.
• Sluiter, R, and 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, and 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, and M, Heuvelink GB. 2010. “Identifying and removing heterogeneities between monitoring networks.” Environmetrics, № 21 (1): 66–84.
• de Nijs, T, and 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.
• Helle, Kristina B., and 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, edited by Nicholas J. Tate and Peter F. Fisher.
• Helle, Kristina B., and Pebesma, Edzer. 2010. “Optimizing Spatio-Temporal Sampling Designs of Synchronous, Static, or Clustered Measurements.” contribution to the European Geosciences Union General Assembly, Vienna
• Gerharz, L., Pebesma, E., and Hecking, H. 2010. “Visualizing uncertainty in spatio-temporal data.” in Vol. null of International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences N/A: Selbstverlag / Eigenverlag.
• Helle, K., and Pebesma, E. 2010. “Conservative updating of sampling designs.” in Vol. null of International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences N/A: Selbstverlag / Eigenverlag.
• Gerharz, L, Pebesma, E, and 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, edited by NJ Tate and PF Fisher.
• Pebesma, E., Cornford, D., Nativi, S., and Stasch, C. 2010. “The uncertainty enabled model web (UncertWeb).” in Vol. 679
• Hiemstra, PH, Pebesma, EJ, Twenhöfel, CJW, and 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, and 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.
• Gerharz, L, and Pebesma, E. 2009. “Usability of interactive and non-interactive visualisation of uncertain geospatial information.” in Geoinformatik 2009 Konferenzband, Vol. 35 of ifgiprints, edited by W Reinhardt, A Krüger and M Ehlers.
• E.J., Pebesma. 2009. “How we build geostatistical models and deal with their output.” in Interfacing Geostatistics and GIS, Vol. null , edited by Springer. Düsseldorf: Springer VDI Verlag. doi: 10.1007/978-3-540-33236-7_1.
• Dubois, G., De Jesus, J., Doherty, B., Cornford, D., and Pebesma, E. 2009. “Lessons learned from INTAMAP, an interoperable web service for the real-time interpolation of environmental variables.” in Vol. null
• Skøien, JO, Pebesma, EJ, and Blöschl, G. 2008. “Geostatistics for automatic estimation of environmental variables-some simple solutions.” Georisk, № 2 (4): 257–270.
• ter, Braak C JF, Brus, DJ, and 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, and Gomez-Rubio, V. 2008. UseR!, Applied Spatial Data Analysis with R, Düsseldorf: Springer VDI Verlag.
• Pebesma, Edzer, Bishr, Mohammed, and Bartoschek, Thomas, eds. 2008. ifgiPrints, Vol. 32, Proceedings of the 6th Geographic Information Days, 400. edt. N/A: unbekannt / n.a. / unknown.
• Addink, EA, De Jong, SM, and 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, and 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, and 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, and 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, and 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.
• EJ, Pebesma. 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., and De Jong, K. 2006. “Dynamic visualisation of spatial and spatio-temporal probability distribution functions.” in Vol. null of International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences N/A: Selbstverlag / Eigenverlag.
• Pebesma, EJ, Duin, RNM, and 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.
• EJ, Pebesma. 2005. “Mapping radioactivity from monitoring data: Automating the classical geostatistical approach.” Applied GIS, № 1 (2)
• Pebesma, EJ, Switzer, P, and 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.
• EJ, Pebesma. 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, and 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, and 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, and 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, and 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, and 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, and 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, and 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, and 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, and 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, and 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, and 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, and 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, EJ, and de 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.