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The potential of low-cost 3D imaging technologies for forestry applications: Setting a research agenda for low-cost remote sensing inventory tasks

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McGlade, J, Wallace, L ORCID: 0000-0002-4642-8374, Reinke, K and Jones, S 2022 , 'The potential of low-cost 3D imaging technologies for forestry applications: Setting a research agenda for low-cost remote sensing inventory tasks' , Forests, vol. 13, no. 2 , pp. 1-27 , doi: 10.3390/f13020204.

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Abstract

Limitations with benchmark light detection and ranging (LiDAR) technologies in forestry have prompted the exploration of handheld or wearable low-cost 3D sensors (<2000 USD). These sensors are now being integrated into consumer devices, such as the Apple iPad Pro 2020. This study was aimed at determining future research recommendations to promote the adoption of terrestrial low-cost technologies within forest measurement tasks. We reviewed the current literature surrounding the application of low-cost 3D remote sensing (RS) technologies. We also surveyed forestry professionals to determine what inventory metrics were considered important and/or difficult to capture using conventional methods. The current research focus regarding inventory metrics captured by low-cost sensors aligns with the metrics identified as important by survey respondents. Based on the literature review and survey, a suite of research directions are proposed to democratise the access to and development of low-cost 3D for forestry: (1) the development of methods for integrating standalone colour and depth (RGB-D) sensors into handheld or wearable devices; (2) the development of a sensor-agnostic method for determining the optimal capture procedures with low-cost RS technologies in forestry settings; (3) the development of simultaneous localisation and mapping (SLAM) algorithms designed for forestry environments; and (4) the exploration of plot-scale forestry captures that utilise low-cost devices at both terrestrial and airborne scales.

Item Type: Article
Authors/Creators:McGlade, J and Wallace, L and Reinke, K and Jones, S
Keywords: RGB-D, SfM, remote sensing, forest inventory, point cloud
Journal or Publication Title: Forests
Publisher: MDPI
ISSN: 1999-4907
DOI / ID Number: 10.3390/f13020204
Copyright Information:

Copyright: © 2022 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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