A comparison of three methods for automatic tree crown detection and delineation from high spatial resolution imagery

Abstract

This article compares the performance of three algorithms representative of published methods for tree crown detection and delineation from high spatial resolution imagery, and demonstrates a standardized accuracy assessment framework. The algorithms – watershed segmentation, region growing and valley-following – were tested on softwood and hardwood sites using Emerge natural colour vertical aerial imagery with 60 cm ground sampled distance and QuickBird panchromatic imagery with an 11˚ look angle. The evaluation considered both plot-level and individual tree crown detection and delineation results. The study shows that while all three methods reasonably delineate crowns in the softwood stand on the Emerge image, region growing provided the highest accuracies, with producer's and user's accuracy for tree detection reaching 70% and root mean square error for crown diameter estimation of 15%. Crown detection accuracies were lower on the QuickBird image. No algorithm proved accurate for the hardwood stand on either image set (both producer's and user's accuracies < 30%).