Publication Date:
2005-10-01
Description:
An algorithm is presented for automated detectiondelineation of coniferous tree regeneration that combines strategies of several existing algorithms, including image processing to isolate conifer crowns, optimal image scale determination, initial crown detection, and crown boundary segmentation and refinement. The algorithm is evaluated using 6-cm pixel airborne imagery in operational regeneration conditions typically encountered in the boreal forest 510 years after harvest. Detection omission and commission errors as well as an accuracy index combining both error types were assessed on a tree by tree basis, on an aggregated basis for each study area, in relation to tree size and the amount of woody competition present. Delineation error was assessed in a similar manner using field-measured crown diameters as a reference. The individual tree detection accuracy index improved with increasing tree size and was 〉70% for trees larger than 30 cm crown diameter. Crown diameter absolute error measured from automated delineations was
Print ISSN:
0045-5067
Electronic ISSN:
1208-6037
Topics:
Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
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