Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach

• Published in: International journal of applied earth observation and geoinformation Vol. 107; p. 102686
• Main Authors: Li, Linyuan, Mu, Xihan, Chianucci, Francesco, Qi, Jianbo, Jiang, Jingyi, Zhou, Jiaxin, Chen, Ling, Huang, Huaguo, Yan, Guangjian, Liu, Shouyang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2022; Elsevier
• Subjects: boreal forests; Canopy mapping; coniferous forests; Crown cover; deforestation; forest canopy; image analysis; Image overlap; landscapes; leaf area index; lidar; lighting; overstory; photogrammetry; Self-supervised deep learning; SfM point cloud; spatial data; statistical analysis; tree crown; UAV imagery; understory; UAV imagery; CNN; nDSM; SNFP; AGL; LiDAR; DL; CHM; Self-supervised deep learning; MVS; UAV; Image overlap; FOV; Canopy mapping; IoU; DCP; OA; SfM point cloud; Crown cover; GSD; SfM; DTM; DSM

•Self-supervised deep learning for forest canopy mapping using UAVs is proposed.•2D images and 3D SfM point clouds are combined for automated training set generation.•The method is evaluated by annotated images and classical segmentation algorithms.•The method is compared with UAV LiDAR and DCP benc...