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Open AccessArticle

Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring

1
Department of Mathematics and Statistics, Washington State University, Vancouver, WA 98686, USA
2
USDA Forest Service, Pacific Northwest Research Station, Portland, OR 97205, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1562; https://doi.org/10.3390/rs10101562
Received: 7 September 2018 / Revised: 24 September 2018 / Accepted: 26 September 2018 / Published: 29 September 2018
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Abstract

Forest inventories are constrained by resource-intensive fieldwork, while unmanned aerial systems (UASs) offer rapid, reliable, and replicable data collection and processing. This research leverages advancements in photogrammetry and market sensors and platforms to incorporate a UAS-based approach into existing forestry monitoring schemes. Digital imagery from a UAS was collected, photogrammetrically processed, and compared to in situ and aerial laser scanning (ALS)-derived plot tree counts and heights on a subsample of national forest plots in Oregon. UAS- and ALS-estimated tree counts agreed with each other (r2 = 0.96) and with field data (ALS r2 = 0.93, UAS r2 = 0.84). UAS photogrammetry also reasonably approximated mean plot tree height achieved by the field inventory (r2 = 0.82, RMSE = 2.92 m) and by ALS (r2 = 0.97, RMSE = 1.04 m). The use of both nadir-oriented and oblique UAS imagery as well as the availability of ALS-derived terrain descriptions likely sustain a robust performance of our approach across classes of canopy cover and tree height. It is possible to draw similar conclusions from any of the methods, suggesting that the efficient and responsive UAS method can enhance field measurement and ALS in longitudinal inventories. Additionally, advancing UAS technology and photogrammetry allows diverse users access to forest data and integrates updated methodologies with traditional forest monitoring.
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Keywords: remote sensing; point cloud; unmanned aerial system (UAS); structure from motion (SfM); forest 3D models; oblique imagery remote sensing; point cloud; unmanned aerial system (UAS); structure from motion (SfM); forest 3D models; oblique imagery
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Fankhauser, K.E.; Strigul, N.S.; Gatziolis, D. Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring. Remote Sens. 2018, 10, 1562.

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