Site index determination in young Norway spruce forests using branch whorls detected from mobile laser scanning data by deep learning

Abstract The site index (SI) describes a site’s potential to produce wood volume. Accurate information on SI in young forests is essential for planning thinning operations and projecting future growth and yield. For tree species that form annual branch whorls, information on interwhorl distances along the stem may be used to determine the SI in young forests. Branch whorls, and consequently tree height growth trajectories, can be detected automatically using deep learning on very dense laser scanning data. In the current study, we demonstrate this approach in a case study in a young Norway spruce forest. We trained a pose estimation Convolutional Neural Network and detected branch whorls of 97 dominant trees in 54 plots scanned with mobile laser scanning data. We predicted SI determined from detected branch whorls in three different sections of each tree, selected in the stem height range between 2.5 and 8 m: all whorls, the lowest six whorls, and whorls selected with an automatic selection procedure. We compared the obtained SI to the SI determined from field-measured branch whorls. Obtained values of precision, recall, and F1 score for the branch whorl detection were 0.66, 0.58, and 0.62, respectively. Values of root mean square error and mean differences between reference and predicted SI ranged between 19.8%–20.9% and −3.6%–4.0%, respectively. Although the tested approach showed potential for SI determination in young forests, the obtained errors were large. This was due to detection errors and high sensitivity to small changes in height increment. These issues highlight the need for further research to improve branch whorl detection accuracy and address challenges associated with determining the SI in young forests.