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Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612065/ https://www.ncbi.nlm.nih.gov/pubmed/36298223 http://dx.doi.org/10.3390/s22207872 |
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author | Srivastava, Sanjeev Kumar Seng, Kah Phooi Ang, Li Minn Pachas, Anibal ‘Nahuel’ A. Lewis, Tom |
author_facet | Srivastava, Sanjeev Kumar Seng, Kah Phooi Ang, Li Minn Pachas, Anibal ‘Nahuel’ A. Lewis, Tom |
author_sort | Srivastava, Sanjeev Kumar |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9612065 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96120652022-10-28 Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest Srivastava, Sanjeev Kumar Seng, Kah Phooi Ang, Li Minn Pachas, Anibal ‘Nahuel’ A. Lewis, Tom Sensors (Basel) Article 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. MDPI 2022-10-17 /pmc/articles/PMC9612065/ /pubmed/36298223 http://dx.doi.org/10.3390/s22207872 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/). |
spellingShingle | Article Srivastava, Sanjeev Kumar Seng, Kah Phooi Ang, Li Minn Pachas, Anibal ‘Nahuel’ A. Lewis, Tom Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest |
title | Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest |
title_full | Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest |
title_fullStr | Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest |
title_full_unstemmed | Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest |
title_short | Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest |
title_sort | drone-based environmental monitoring and image processing approaches for resource estimates of private native forest |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612065/ https://www.ncbi.nlm.nih.gov/pubmed/36298223 http://dx.doi.org/10.3390/s22207872 |
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