The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Development of a UAV-LiDAR System with Application to Forest Inventory
Open accessAbstract
We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m2) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points perm2, to 0.15mwhen the higher density data was used. Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
The unscented Kalman filter for nonlinear estimation
Wan, E. A., & Van Der Merwe, R. (2002) · Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373) · doi:10.1109/asspcc.2000.882463 · Cited by 4,001
Photo tourism: exploring photo collections in 3D
Snavely, N., Seitz, S. M., & Szeliski, R. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141964 · Cited by 2,866
An efficient solution to the five-point relative pose problem
Nister, D. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.17 · Cited by 2,009
Lidar Remote Sensing for Ecosystem Studies
LEFSKY, M. A., COHEN, W. B., PARKER, G. G., & HARDING, D. J. (2002) · BioScience · doi:10.1641/0006-3568(2002)052[0019:lrsfes]2.0.co;2 · Cited by 1,824
LiDAR remote sensing of forest structure
Lim, K., Treitz, P., Wulder, M., St-Onge, B., & Flood, M. (2003) · Progress in Physical Geography: Earth and Environment · doi:10.1191/0309133303pp360ra · Cited by 1,103
Hyyppä, J., Hyyppä, H., Leckie, D., Gougeon, F., Yu, X., & Maltamo, M. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701736489 · Cited by 636
Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring
Hunt, E. R., Hively, W. D., Fujikawa, S. J., Linden, D. S., Daughtry, C. S. T., & McCarty, G. W. (2010) · Remote Sensing · doi:10.3390/rs2010290 · Cited by 488
Morsdorf, F., Kötz, B., Meier, E., Itten, K. I., & Allgöwer, B. (2006) · Remote Sensing of Environment · doi:10.1016/j.rse.2006.04.019 · Cited by 470
Sigma-point Kalman filtering for integrated GPS and inertial navigation
Crassidis, J. L. (2006) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2006.1642588 · Cited by 358
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HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection
Suo, J., Wang, T., Zhang, X., Chen, H., Zhou, W., & Shi, W. (2023) · Scientific Data · doi:10.1038/s41597-023-02066-6 · Cited by 291
Calibration and Accuracy Analysis of a Low-Cost Mapping-Grade Mobile Laser Scanning System
Hauser, D., Glennie, C., & Brooks, B. (2016) · Journal of Surveying Engineering · doi:10.1061/(asce)su.1943-5428.0000178 · Cited by 29
Metadata
- Authors
- Wallace, L., Lucieer, A., Watson, C., & Turner, D.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2012-05-25
- Volume
- 4
- Issue
- 6
- Pages
- 1519-1543
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W1983818779
- Cited by
- 710
- References
- 27
- FWCI
- 19.15
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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