Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Propagation of positional error in 3D GIS : estimation of the solar irradiation of building roofs
    Biljecki, Filip ; Heuvelink, Gerard B.M. ; Ledoux, Hugo ; Stoter, Jantien - \ 2015
    International Journal of Geographical Information Science 29 (2015)12. - ISSN 1365-8816 - p. 2269 - 2294.
    3D GIS - CityGML - error propagation - photovoltaic potential - uncertainty

    While error propagation in GIS is a topic that has received a lot of attention, it has not been researched with 3D GIS data. We extend error propagation to 3D city models using a Monte Carlo simulation on a use case of annual solar irradiation estimation of building rooftops for assessing the efficiency of installing solar panels. Besides investigating the extension of the theory of error propagation in GIS from 2D to 3D, this paper presents the following contributions. We (1) introduce varying XY/Z accuracy levels of the geometry to reflect actual acquisition outcomes; (2) run experiments on multiple accuracy classes (121 in total); (3) implement an uncertainty engine for simulating acquisition positional errors to procedurally modelled (synthetic) buildings; (4) perform the uncertainty propagation analysis on multiple levels of detail (LODs); and (5) implement Solar3Dcity – a CityGML-compliant software for estimating the solar irradiation of roofs, which we use in our experiments. The results show that in the case of the city of Delft in the Netherlands, a 0.3/0.6 m positional uncertainty yields an error of 68 kWh/m2/year (10%) in solar irradiation estimation. Furthermore, the results indicate that the planar and vertical uncertainties have a different influence on the estimations, and that the results are comparable between LODs. In the experiments we use procedural models, implying that analyses are carried out in a controlled environment where results can be validated. Our uncertainty propagation method and the framework are applicable to other 3D GIS operations and/or use cases. We released Solar3Dcity as open-source software to support related research efforts in the future.

    Effect of DEM Uncertainty on the Positional Accuracy of Airborne Imagery
    Beekhuizen, J. ; Heuvelink, G.B.M. ; Biesemans, J. ; Reusen, I. - \ 2011
    IEEE Transactions on Geoscience and Remote Sensing 49 (2011)5. - ISSN 0196-2892 - p. 1567 - 1577.
    digital elevation models - imaging spectrometry data - environmental variables - error propagation - c-band - geostatistics - gstat
    The geometric and atmospheric processing of airborne imagery is a complex task that involves many correction steps. Geometric correction is particularly challenging because slight movements of the aircraft and small changes in topography can have a great impact on the geographic positioning of the processed imagery. This paper focused on how uncertainty in topography, represented by a digital elevation model (DEM), propagates through the geometric correction process. We used a Monte Carlo analysis, in which, first, a geostatistical uncertainty model of the DEM was developed to simulate a large number of DEM realizations. Next, geometric correction was run for each of the simulated DEMs. The analysis of the corrected images and their variability provided valuable information about the positional accuracy of the corrected image. The method was applied to a hyperspectral image of a mountainous area in Calabria, Italy, by using the Shuttle Radar Topography Mission-DEM as the topographic information source. We found out that the uncertainty varies greatly over the whole terrain and is substantial at large off-nadir viewing angles in the across-track direction. Also, positional uncertainty is larger in rugged terrains. We conclude that Monte Carlo uncertainty propagation analysis is a valuable technique in deriving quality layers that inform end users about the positional accuracy of airborne imagery, and we recommend that it is integrated in the operational processing steps of the Processing and Archiving Facilities.
    A probabilistic framework for representing and simulating uncertain environmental variables
    Heuvelink, G.B.M. ; Brown, J.D. ; Loon, E.E. van - \ 2007
    International Journal of Geographical Information Science 21 (2007). - ISSN 1365-8816 - p. 497 - 513.
    error propagation - modeling error - gis - information - maps
    Understanding the limitations of environmental data is important for managing environmental systems effectively and for encouraging the responsible use of uncertain data. Explicit assessment of the uncertainties associated with environmental data, and their storage in a database, are therefore important. This paper presents a statistical framework for representing and simulating uncertain environmental variables. In general terms, an uncertain variable is completely specified by its probability distribution function (pdf). Pdfs are developed for objects with uncertain locations ('positional uncertainty') and uncertain attribute values ('attribute uncertainty'). Objects comprising multiple space-time locations are separated into 'rigid objects', where positional uncertainty cannot alter the internal geometry of the object, and 'deformable' objects, where positional uncertainty can vary between locations in one object. Statistical dependence is allowed between uncertainties in multiple locations in one object. The uncertainties associated with attribute values are also modelled with pdfs. The type and complexity of these pdfs depend upon the measurement scale and the space-time variability of the uncertain attribute. The framework is illustrated with examples. A prototype software tool for assessing uncertainties in environmental data, storing them within a database, and for generating realizations for use in Monte Carlo studies is also presented.
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