Measuring loblolly pine crowns with drone imagery through deep learning  

作　　者：Xiongwei Lou Yanxiao Huang Luming Fang Siqi Huang Haili Gao Laibang Yang Yuhui Weng I.-K.uai Hung 

机构地区：[1]School of Information Engineering,Zhejiang A&F University,Lin’an 311300,Zhejiang,People’s Republic of China [2]Key Laboratory of State Forestry and Grassland Administration On Forestry Sensing Technology and Intelligent Equipment,Zhejiang A&F University,Lin’an 311300,Zhejiang,People’s Republic of China [3]Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province,Zhejiang A&F University,Lin’an 311300,Zhejiang,People’s Republic of China [4]College of Forestry and Agriculture,Stephen F.Austin State University,Nacogdoches,TX 75962,USA [5]Jiyang College of Zhejiang A&F University,Zhuji 311800,Zhejiang,People’s Republic of China [6]College of Forestry and Biotechnology,Zhejiang A&F University,Lin’an 311300,Zhejiang,People’s Republic of China

出　　处：《Journal of Forestry Research》2022年第1期227-238,共12页林业研究（英文版）

基　　金：supported by the Mc IntireStennis program and East Texas Pine Plantation Research Project at Stephen F.Austin State University;Part of the research was also supported by Zhejiang Provincial Key Science and Technology Project(2018C02013)。

摘　　要：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 particular for mature trees,is challenging.This study demonstrated a novel method of applying machine learning algorithms to aerial imagery acquired by an unmanned aerial vehicle(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 recognition 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(R^(2))of 0.92,followed by Faster R-CNN(0.88)and YOLOv3(0.62).As to the mature site,the SSD model achieved a R^(2)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 estimating crown widths with satisfactory accuracy.For the purpose of forest inventory on loblolly pine plantations,using UAV-captured imagery paired with the SSD object detention application is a cost-effective alternative to traditional ground measurement.

关 键 词：UAV image Crown recognition Object detection Crown width measurement 

分 类 号：S791.255[农业科学—林木遗传育种]