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Remote Sens. 2016, 8(2), 123;

Accuracy of Reconstruction of the Tree Stem Surface Using Terrestrial Close-Range Photogrammetry

Faculty of Forestry and Wood Sciences, Czech University of Life Sciences , Kamýcká 129, Praha 165 21, Czech Republic
The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Author to whom correspondence should be addressed.
Academic Editors: Guangxing Wang, Erkki Tomppo, Ronald E. McRorberts, Dengsheng Lu, Huaiqing Zhang, Qi Chen, Richard Gloaguen and Prasad S. Thenkabail
Received: 8 September 2015 / Revised: 20 January 2016 / Accepted: 25 January 2016 / Published: 5 February 2016
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Airborne laser scanning (ALS) allows for extensive coverage, but the accuracy of tree detection and form can be limited. Although terrestrial laser scanning (TLS) can improve on ALS accuracy, it is rather expensive and area coverage is limited. Multi-view stereopsis (MVS) techniques combining computer vision and photogrammetry may offer some of the coverage benefits of ALS and the improved accuracy of TLS; MVS combines computer vision research and automatic analysis of digital images from common commercial digital cameras with various algorithms to reconstruct three-dimensional (3D) objects with realistic shape and appearance. Despite the relative accuracy (relative geometrical distortion) of the reconstructions available in the processing software, the absolute accuracy is uncertain and difficult to evaluate. We evaluated the data collected by a common digital camera through the processing software (Agisoft PhotoScan ©) for photogrammetry by comparing those by direct measurement of the 3D magnetic motion tracker. Our analyses indicated that the error is mostly concentrated in the portions of the tree where visibility is lower, i.e., the bottom and upper parts of the stem. For each reference point from the digitizer we determined how many cameras could view this point. With a greater number of cameras we found increasing accuracy of the measured object space point positions (as expected), with a significant positive change in the trend beyond five cameras; when more than five cameras could view this point, the accuracy began to increase more abruptly, but eight cameras or more provided no increases in accuracy. This method allows for the retrieval of larger datasets from the measurements, which could improve the accuracy of estimates of 3D structure of trees at potentially reduced costs. View Full-Text
Keywords: stem surface; stem volume; terrestrial photogrammetry stem surface; stem volume; terrestrial photogrammetry

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Surový, P.; Yoshimoto, A.; Panagiotidis, D. Accuracy of Reconstruction of the Tree Stem Surface Using Terrestrial Close-Range Photogrammetry. Remote Sens. 2016, 8, 123.

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