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Forest structural complexity tool - an open source, fully-automated tool for measuring forest point clouds

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Krisanski, S ORCID: 0000-0003-0689-0051, Taskhiri, MS ORCID: 0000-0002-9871-361X, Gonzalez Aracil, S, Herries, D, Muneri, A, Gurung, MB, Montgomery, J ORCID: 0000-0002-5360-7514 and Turner, P ORCID: 0000-0003-4504-2338 2021 , 'Forest structural complexity tool - an open source, fully-automated tool for measuring forest point clouds' , Remote Sensing, vol. 13, no. 22 , pp. 1-31 , doi: 10.3390/rs13224677.

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Abstract

Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed forest measurements rapidly and cheaply, however, they have been held back from large-scale implementation due to the complex and time-consuming workflows required to utilize them. This work is focused on describing and evaluating an approach to create a robust, sensor-agnostic and fully automated forest point cloud measurement tool called the Forest Structural Complexity Tool (FSCT). The performance of FSCT is evaluated using 49 forest plots of terrestrial laser scanned (TLS) point clouds and 7022 destructively sampled manual diameter measurements of the stems. FSCT was able to match 5141 of the reference diameter measurements fully automatically with mean, median and root mean squared errors (RMSE) of 0.032 m, 0.02 m, and 0.103 m respectively. A video demonstration is also provided to qualitatively demonstrate the diversity of point cloud datasets that the tool is capable of measuring. FSCT is provided as open source, with the goal of enabling plot scale remote sensing techniques to replace most structural forest mensuration in research and industry. Future work on this project will seek to make incremental improvements to this methodology to further improve the reliability and accuracy of this tool in most high-resolution forest point clouds.

Item Type: Article
Authors/Creators:Krisanski, S and Taskhiri, MS and Gonzalez Aracil, S and Herries, D and Muneri, A and Gurung, MB and Montgomery, J and Turner, P
Keywords: deep learning, segmentation, forest, point cloud, lidar, photogrammetry, terrestrial laser scanning, structure from motion, automated, digital terrain model
Journal or Publication Title: Remote Sensing
Publisher: MDPI
ISSN: 2072-4292
DOI / ID Number: 10.3390/rs13224677
Copyright Information:

Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

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