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Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest7.61 MB
Published VersionCC BY V4.0, Open Access
Journal article
Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest
Sensors, Vol.22(20), pp.1-14
2022
Abstract
This paper investigated the utility of drone based environmental monitoring to assist with forest inventory in Queensland private native forests (PNF). The research aimed to build capabilities to carry out forest inventory more efficiently without the need to rely on laborious field assessments. The use of drone derived images and the subsequent application of digital photogrammetry to obtain information about PNFs are underinvestigated in southeast Queensland vegetation types. In this study, we used image processing to separate individual trees and digital photogrammetry to derive a canopy height model (CHM). The study was supported with tree height data collected in the field for one site. The paper addressed the research question “How well do drone derived point clouds estimate the height of trees in PNF ecosystems?” The study indicated that a drone with a basic RGB camera can estimate tree height with good confidence. The results can potentially be applied across multiple land tenures and similar forest types. This informs the development of drone based and remote sensing image processing methods, which will lead to improved forest inventories, thereby providing forest managers with recent, accurate, and efficient information on forest resources.
Details
- Title
- Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest
- Authors
- Sanjeev Kumar Srivastava (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringKah Phooi Seng (Corresponding Author) - Queensland University of TechnologyLi Minn Ang (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringAnibal ‘Nahuel’ A. Pachas (Author) - Department of Primary IndustriesTom Lewis (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Sensors, Vol.22(20), pp.1-14
- Publisher
- MDPI AG
- Date published
- 2022
- DOI
- 10.3390/s22207872
- ISSN
- 1424-8220; 1424-8239
- Copyright note
- © 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 Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).
- Organisation Unit
- University of the Sunshine Coast, Queensland; Forest Industries Research Centre; School of Science, Technology and Engineering; Forest Research Institute; Engage Research Lab; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 99682998302621
- Output Type
- Journal article
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