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Comparison of Mature Douglas-Firs’ Crown Structures Developed with Two Quantitative Structural Models Using TLS Point Clouds for Neighboring Trees in a Natural Regime Stand
Open AccessArticle

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

College of Forestry, Oregon State University, 3100 Jefferson Way, Corvallis, OR 97333, USA
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Remote Sens. 2019, 11(16), 1877; https://doi.org/10.3390/rs11161877
Received: 4 July 2019 / Revised: 31 July 2019 / Accepted: 8 August 2019 / Published: 11 August 2019
(This article belongs to the Special Issue Lidar for Ecosystem Science and Management)
Accurate and precise location of trees from data acquired under-the-canopy is challenging and time-consuming. However, current forestry practices would benefit tremendously from the knowledge of tree coordinates, particularly when the information used to position them is acquired with inexpensive sensors. Therefore, the objective of our study is to geo-reference trees using point clouds created from the images acquired below canopy. We developed a procedure that uses the coordinates of the trees seen from above canopy to position the same trees seen below canopy. To geo-reference the trees from above canopy we captured images with an unmanned aerial vehicle. We reconstructed the trunk with photogrammetric point clouds built with a structure–from–motion procedure from images recorded in a circular pattern at multiple locations throughout the stand. We matched the trees segmented from below canopy with the trees extracted from above canopy using a non-rigid point-matching algorithm. To ensure accuracy, we reduced the number of matching trees by dividing the trees segmented from above using a grid with 50 m cells. Our procedure was implemented on a 7.1 ha Douglas-fir stand from Oregon USA. The proposed procedure is relatively fast, as approximately 600 trees were mapped in approximately 1 min. The procedure is sensitive to the point density, directly impacting tree location, as differences larger than 2 m between the coordinates of the tree top and the bottom part of the stem could lead to matching errors larger than 1 m. Furthermore, the larger the number of trees to be matched the higher the accuracy is, which could allow for misalignment errors larger than 2 m between the locations of the trees segmented from above and below. View Full-Text
Keywords: unmanned aerial vehicle; structure from motion; photogrammetric point clouds; tree segmentation; point-matching unmanned aerial vehicle; structure from motion; photogrammetric point clouds; tree segmentation; point-matching
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MDPI and ACS Style

Strimbu, B.M.; Qi, C.; Sessions, J. Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices. Remote Sens. 2019, 11, 1877.

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