Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions

In this article, six individual tree crown (ITC) detection/delineation algorithms are evaluated, using an image data set containing six diverse forest types at different geographical locations in three European countries. The algorithms use fundamentally different techniques, including local maxima detection, valley following (VF), region-growing (RG), template matching (TM), scale-space (SS) theory and techniques based on stochastic frameworks. The structurally complexity of the forests in the aerial images used ranges from a homogeneous plantation and an area with isolated tree crowns to an extremely dense deciduous forest type. None of the algorithms alone could successfully analyse all different cases. The study shows that it is important to partition the imagery into homogeneous forest stands prior to the application of individual tree detection algorithms. It furthermore suggests a need for a common, publicly available suite of test images and common test procedures for evaluation of individual tree detection/delineation algorithms. Finally, it highlights that, for complex forest types, monoscopic images are insufficient for consistent tree crown detection, even by human interpreters.