This study addresses the use of super resolution mapping (SRM) for precision agriculture. SRM was applied to a high resolution GeoEye image of a vineyard in Iran with the aim to determine the actual evapotranspiration (AET) and potential evapotranspiration (PET). The Surface Energy Balance System applied for that purpose requires the use of a derived by standard calculations at the field scale, but spatial variability was clearly present. The study concluded that modern satellite derived information in combination with recently developed image analysis methods is able to provide reliable AET values at the row level, but not yet for every individual grape tree.
This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale direction provides information about the tree size. A two-step procedure relates the detected blobs to tree objects in the field. First, a Gaussian blob model identifies tree crowns in Gaussian scale space. Second, an improved tree crown model modifies this model in the scale direction. The procedures are tested on the following three representative cases: an area with vitellaria trees in Mali, an orchard with walnut trees in Iran, and one case with oil palm trees in Indonesia. The results show that the refined Gaussian blob model improves upon the traditional Gaussian blob model by effectively discriminating between false and correct detections and accurately identifying size and position of trees. A comparison with existing methods shows an improvement of 10–20% in true positive detections. We conclude that the presented two-step modeling procedure of tree crowns using Gaussian scale space is useful to automatically detect individual trees from VHR satellite images for at least three representative cases.
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