期刊
REMOTE SENSING
卷 10, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/rs10020187
关键词
image-based point clouds; light detection and ranging (LiDAR); mixed conifer-broadleaf forest; structure from motion (SfM); unmanned aerial vehicle
类别
资金
- JSPS KAKENHI [16H04946]
- Grants-in-Aid for Scientific Research [17H01516, 16H04946] Funding Source: KAKEN
Unmanned aerial vehicles (UAVs) and digital photogrammetric techniques are two recent advances in remote sensing (RS) technology that are emerging as alternatives to high-cost airborne laser scanning (ALS) data sources. Despite the potential of UAVs in forestry applications, very few studies have included detailed analyses of UAV photogrammetric products at larger scales or over a range of forest types, including mixed conifer-broadleaf forests. In this study, we assessed the performance of fixed-wing UAV photogrammetric products of a mixed conifer-broadleaf forest with varying levels of canopy structural complexity. We demonstrate that fixed-wing UAVs are capable of efficiently collecting image data at local scales and that UAV imagery can be effectively utilized with digital photogrammetric techniques to provide detailed automated reconstruction of the three-dimensional (3D) canopy surface of mixed conifer-broadleaf forests. When combined with an accurate digital terrain model (DTM), UAV photogrammetric products are promising for producing reliable structural measurements of the forest canopy. However, the performance of UAV photogrammetric products is likely to be influenced by the structural complexity of the forest canopy. Furthermore, we highlight the potential of fixed-wing UAVs in operational forest management at the forest management compartment level, for acquiring high-resolution imagery at low cost. A future direction of this research would be to address the issue of how well the photogrammetric products can predict the actual structure of mixed conifer-broadleaf forests.
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