D. A. Pouliot

TL;DR: A tree detection–delineation algorithm designed specifically for high-resolution digital imagery of 6-year-old trees is presented and rigorously evaluated, showing that tree-detection accuracy was better than that using commonly applied fixed-window local maximum filters and crown-diameter accuracy was more sensitive to image resolution.

TL;DR: 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 de- termination, initial crown detection, and crown boundary segmentation and refinement.

TL;DR: Results indicate strong potential for identification and counting of conifer trees when competing vegetation cover is low or in leaf-off condition, however, systematic decreases in class separability and conifer count accuracy were observed with increasing competition.

TL;DR: In this article, the authors evaluated the potential of automated methods for assessment of woody stem competition using very high-resolution (2 cm) leaf-off imagery and found that the best approach may be to combine them to optimize processing time and achieve the highest possible precision.