Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 17; p. 2865
• Main Authors: Moe, Kyaw Thu, Owari, Toshiaki, Furuya, Naoyuki, Hiroshima, Takuya, Morimoto, Junko
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2020
• Subjects: Accuracy; Aerial photography; Algorithms; Canopies; Coniferous trees; Data processing; Diameters; Digitization; Forest floor; Forest management; high-value timber species; Image analysis; Image classification; Image processing; Image segmentation; Lidar; Management systems; Mapping; Mixed forests; object-based image analysis; Orthophotography; Photogrammetry; random forest classification; Remote sensing; single-tree management system; Species; Timber; Trees; unmanned aerial vehicle; Unmanned aerial vehicles; Wood; Sweden; Japan
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12172865

High-value timber species play an important economic role in forest management. The individual tree information for such species is necessary for practical forest management and for conservation purposes. Digital aerial photogrammetry derived from an unmanned aerial vehicle (UAV-DAP) can provide fine spatial and spectral information, as well as information on the three-dimensional (3D) structure of a forest canopy. Light detection and ranging (LiDAR) data enable area-wide 3D tree mapping and provide accurate forest floor terrain information. In this study, we evaluated the potential use of UAV-DAP and LiDAR data for the estimation of individual tree location and diameter at breast height (DBH) values of large-size high-value timber species in northern Japanese mixed-wood forests. We performed multiresolution segmentation of UAV-DAP orthophotographs to derive individual tree crown. We used object-based image analysis and random forest algorithm to classify the forest canopy into five categories: three high-value timber species, other broadleaf species, and conifer species. The UAV-DAP technique produced overall accuracy values of 73% and 63% for classification of the forest canopy in two forest management sub-compartments. In addition, we estimated individual tree DBH Values of high-value timber species through field survey, LiDAR, and UAV-DAP data. The results indicated that UAV-DAP can predict individual tree DBH Values, with comparable accuracy to DBH prediction using field and LiDAR data. The results of this study are useful for forest managers when searching for high-value timber trees and estimating tree size in large mixed-wood forests and can be applied in single-tree management systems for high-value timber species.