Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest

Sanjeev Kumar Srivastava; Kah Phooi Seng; Li Minn Ang; Anibal ‘Nahuel’ A. Pachas; Tom Lewis

doi:10.3390/s22207872

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Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest

Journal article

Open access

Peer reviewed

Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest

Sanjeev Kumar Srivastava, Kah Phooi Seng, Li Minn Ang, Anibal ‘Nahuel’ A. Pachas and Tom Lewis

Sensors, Vol.22(20), pp.1-14

2022

DOI: https://doi.org/10.3390/s22207872

PMCID: PMC9612065

PMID: 36298223

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Abstract

digital photogrammetry

drone-based monitoring

forest resource estimation

image analysis

private native forests

remote sensing

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 Engineering
Kah Phooi Seng (Corresponding Author) - Queensland University of Technology
Li Minn Ang (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Anibal ‘Nahuel’ A. Pachas (Author) - Department of Primary Industries
Tom 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
PMID: 36298223; PMC9612065
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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