| Abstract: | In modeling forest stand growth and yield,crown width,a measure for stand density,is among the parameters that allows for estimating stand timber volumes.However,accurately measuring tree crown size in the field,in particu-lar for mature trees,is challenging.This study demonstrated a novel method of applying machine learning algorithms to aerial imagery acquired by an unmanned aerial vehi-cle (UAV) to identify tree crowns and their widths in two loblolly pine plantations in eastern Texas,USA.An ortho mosaic image derived from UAV-captured aerial photos was acquired for each plantation (a young stand before canopy closure,a mature stand with a closed canopy).For each site,the images were split into two subsets:one for training and one for validation purposes.Three widely used object detection methods in deep learning,the Faster region-based convolutional neural network (Faster R-CNN),You Only Look Once version 3 (YOLOv3),and single shot detection(SSD),were applied to the training data,respectively.Each was used to train the model for performing crown recogni-tion and crown extraction.Each model output was evaluated using an independent test data set.All three models were successful in detecting tree crowns with an accuracy greater than 93％,except the Faster R-CNN model that failed on the mature site.On the young site,the SSD model performed the best for crown extraction with a coefficient of determination(R2) of 0.92,followed by Faster R-CNN (0.88) and YOLOv3(0.62).As to the mature site,the SSD model achieved a R2 as high as 0.94,follow by YOLOv3 (0.69).These deep leaning algorithms,in particular the SSD model,proved to be successfully in identifying tree crowns and estimat-ing crown widths with satisfactory accuracy.For the pur-pose of forest inventory on loblolly pine plantations,using UAV-captured imagery paired with the SSD object deten-tion application is a cost-effective alternative to traditional ground measurement. |
