Abstract
Spatio-temporal aggregation of observed or predicted values for environmental phenomena is needed for fusing sensor data or coupling sensors and environmental models. However, estimates from sensors or environmental models can never represent our world precisely and are subject to errors. Hence, there is uncertainty in the estimates that needs to be considered in environmental model workflows. This chapter presents an approach for an error-aware spatio-temporal aggregation in the Web, where probabilistic uncertainties are used within a Monte Carlo simulation. The approach is applied in a Web-based model chain that provides uncertain crop yield predictions on field parcel level that are aggregated to larger regions.
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Notes
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The classes are provided under the GNU General Public Licence (GPL) v2 licence as part of the STAS implementation at https://svn.52north.org/svn/geostatistics/main/uncertweb/stas/trunk
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The input and output extension of the 52N WPS framework is acccessible as a separate package at https://svn.52north.org/svn/geostatistics/main/uncertweb/52n-wps-io-uncertweb/trunk and can also be used by other WPS implementations for uncertain data.
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Acknowledgments
The research leading to these results has received funding from the European Union Seventh Framework Programme [FP7/2007-2013] under grant agreement no 248488. We are thankful to Jill Johnson and Sarah Knight from the Food and Environment Research Agency and Richard Jones from Aston University for the support during the integration of our approach in the yield prediction workflow.
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Stasch, C., Pebesma, E., Graeler, B., Gerharz, L. (2013). Error-Aware Spatio-Temporal Aggregation in the Model Web. In: Vandenbroucke, D., Bucher, B., Crompvoets, J. (eds) Geographic Information Science at the Heart of Europe. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-00615-4_12
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