Individual tree crown width detection from unmanned aerial vehicle images using a revised local transect method

Abstract

The width of individual tree crowns is a critical parameter of canopy structure that can indirectly determine the stand diameter at breast height, tree height, competition, and understory light. Additionally, accurate estimation of crown width is gainful in improving forest carbon storage estimations. However, detecting automatic and accurate crown widths based on visible images is still challenging owing to the influence of crown overlap, shadows, and background. Therefore, we proposed a revised local transect method to detect crown widths from three high-spatial-resolution unmanned aerial vehicle images. Two parameters, the boundary point adjustment parameter (α) and percentile parameter (β), were added to remove the outliers for crown width estimate, reducing the influence of crown overlap, background, and shadow on boundary point detection and delineating the crown width more accurately. The results showed that α had a more significant effect on the crown-width detection accuracy than β. A larger smoothing window size was necessary to improve the accuracy of crown width detection for images with very high spatial resolution. With the optimal α (10%) and smoothing window size, the coefficients of determination between the detected crown width estimates and referenced crown width were between 0.75 and 0.78, and the relative root mean square errors were between 7.50% and 14.65%. Overall, the revised local transect method for detecting crown width is feasible and provides technical support for the accurate detection of crown width based on visible images from unmanned aerial vehicles.