Development and evaluation of an automated tree detection-delineation algorithm for monitoring regenerating coniferous forests

An algorithm is presented for automated detection-delineation 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 5-10 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 < 23%. Large crown diameters tended to be slightly underestimated. The presence of overtopping woody competition had a negligible effect on detection accuracy and only reduced estimates of crown diameter slightly.