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Enhancing methods for under-canopy unmanned aircraft system based photogrammetry in complex forests for tree diameter measurement

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Krisanski, S ORCID: 0000-0003-0689-0051, Sadegh Taskhiri, M ORCID: 0000-0002-9871-361X and Turner, P ORCID: 0000-0003-4504-2338 2020 , 'Enhancing methods for under-canopy unmanned aircraft system based photogrammetry in complex forests for tree diameter measurement' , Remote Sensing, vol. 12, no. 10 , pp. 1-21 , doi: 10.3390/rs12101652.

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Abstract

The application of Unmanned Aircraft Systems (UAS) beneath the forest canopy provides a potentially valuable alternative to ground-based measurement techniques in areas of dense canopy cover and undergrowth. This research presents results from a study of a consumer-grade UAS flown under the forest canopy in challenging forest and terrain conditions. This UAS was deployed to assess under-canopy UAS photogrammetry as an alternative to field measurements for obtaining stem diameters as well as ultra-high-resolution (~400,000 points/m2) 3D models of forest study sites. There were 378 tape-based diameter measurements collected from 99 stems in a native, unmanaged eucalyptus pulchella forest with mixed understory conditions and steep terrain. These measurements were used as a baseline to evaluate the accuracy of diameter measurements from under-canopy UAS-based photogrammetric point clouds. The diameter measurement accuracy was evaluated without the influence of a digital terrain model using an innovative tape-based method. A practical and detailed methodology is presented for the creation of these point clouds. Lastly, a metric called the Circumferential Completeness Index (CCI) was defined to address the absence of a clearly defined measure of point coverage when measuring stem diameters from forest point clouds. The measurement of the mean CCI is suggested for use in future studies to enable a consistent comparison of the coverage of forest point clouds using different sensors, point densities, trajectories, and methodologies. It was found that root-mean-squared-errors of diameter measurements were 0.011 m in Site 1 and 0.021 m in the more challenging Site 2. The point clouds in this study had a mean validated CCI of 0.78 for Site 1 and 0.7 for Site 2, with a mean unvalidated CCI of 0.86 for Site 1 and 0.89 for Site 2. The results in this study demonstrate that under-canopy UAS photogrammetry shows promise in becoming a practical alternative to traditional field measurements, however, these results are currently reliant upon the operator’s knowledge of photogrammetry and his/her ability to fly manually in object-rich environments. Future work should pursue solutions to autonomous operation, more complete point clouds, and a method for providing scale to point clouds when global navigation satellite systems are unavailable.

Item Type: Article
Authors/Creators:Krisanski, S and Sadegh Taskhiri, M and Turner, P
Keywords: drone, UAS, UAV, below-canopy, under-canopy, photogrammetry, structure from motion, point cloud, diameter, forest
Journal or Publication Title: Remote Sensing
Publisher: MDPIAG
ISSN: 2072-4292
DOI / ID Number: 10.3390/rs12101652
Copyright Information:

Copyright 2020 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

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