Individual tree crown delineation in high resolution aerial RGB imagery using StarDist-based model

The availability of high spatial resolution remote sensing imagery has facilitated forestry attribute estimation at the individual tree level. However, producing accurate tree crown delineations for practical applications remains challenging, particularly in mixed forests with overlapping tree crowns. In this study, we propose an individual tree crown delineation method leveraging the StarDist model to improve the delineation accuracy in mixed forests. The StarDist model captures tree crown shapes uniquely through star-convex polygons, which are predicted by the U-Net architecture. The final tree crowns are determined by applying non-maximum suppression (NMS) to all identified star-convex polygons. Performance evaluation on two mixed forest areas reveals a delineation accuracy exceeding 92%, notably outperforming the widely used deep learning model MASK R-CNN by over 6%. In terms of tree crown areas estimation, the R 2 for both testing areas is higher than 0.85 for both testing areas. Moreover, the evaluations on precision, recall, and F1-score demonstrate that the proposed model can generate tree crowns fitting well with the true crowns. This study marks the first utilization of the StarDist model for tree crown delineation in mixed forests. Our findings demonstrate the effectiveness of the StarDist model for accurately delineating individual tree crowns, thereby advancing the field of forestry research. • We mark the first utilization of the StarDist model for the tree crown delineation task. • The star-convex polygon is used to describe the shape of tree crowns. • The algorithm was tested in two mix-forest areas. • A delineation accuracy exceeding 92% is achieved with a small training set. • The StarDist model notably surpasses the commonly-used deep learning model MASK R-CNN.