Abstract
Grapevine canopy characteristics as determined from remotely sensed imagery have been shown to be effective in forecasting grape composition parameters that can be used to estimate the quality of wine made from those grapevines. Maps of canopy characteristics are therefore valuable tools for precision viticulture practice. In a case of extracting reflectance data at the scale of individual vines from vineyard imagery with a pixel resolution of ca. 0.5 m, simple use of sample point location data provided by a GPS (the GPS points) projected onto a georectified image proved too inaccurate for the desired analysis. At the individual vine scale, the spatial error between the GPS point and the corresponding location in a georectified image was great enough to result in clearly incorrect pixels being identified as representative of the sample grapevine canopy. The sample GPS point locations were, however, sufficiently close to the correct vine canopy in the georectified image to act as a seed point for a computer search algorithm.
The VineClipper algorithm was developed to identify a more representative set of vine pixels using the sample GPS points as seed points in a proximal spatial search. The procedure automatically recognises vine rows in an image close to each GPS point and then determines the local central line of the vine row closest to the GPS point location. The central point of the canopy pixels selected to correspond to the GPS field data point was selected as the point on the centre line closest to the GPS point. Reflectance data of the pixels surrounding this point were then extracted from inside a rectangle (equivalent to the area of one vine) aligned with the row direction. The methodology and the novel algorithm that was developed to automatically perform this procedure are presented.
Interested readers may obtain a copy of the algorithm code or further details by contacting Andrew Hall (email: ahall@csu.edu.au).
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Acknowledgements
This work was supported by Australia’s grapegrowers and winemakers through their investment body the Grape and Wine Research and Development Corporation, with matching funds from the Federal government, and by the Commonwealth Cooperative Research Centre Program. The work was conducted by the National Wine and Grape Industry Centre, Charles Sturt University, and forms part of the research program of the Cooperative Research Centre for Viticulture. The authors appreciate ongoing support provided by Charles Sturt University’s Spatial Analysis Unit (CSU-SPAN).
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Hall, A., Louis, J. (2009). Vineclipper: A Proximal Search Algorithm to Tie Gps Field Locations to High Resolution Grapevine Imagery. In: Jones, S., Reinke, K. (eds) Innovations in Remote Sensing and Photogrammetry. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93962-7_28
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