{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T07:10:49Z","timestamp":1780384249384,"version":"3.54.1"},"reference-count":27,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Queensland Department of Agriculture and Fisheries"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u201cHow well do drone-derived point clouds estimate the height of trees in PNF ecosystems?\u201d 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.<\/jats:p>","DOI":"10.3390\/s22207872","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T05:08:02Z","timestamp":1665983282000},"page":"7872","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8402-6467","authenticated-orcid":false,"given":"Sanjeev Kumar","family":"Srivastava","sequence":"first","affiliation":[{"name":"School of Science Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kah Phooi","family":"Seng","sequence":"additional","affiliation":[{"name":"School of AI and Advanced Computing, Xi\u2019an Jiaotong Liverpool University, Suzhou 215000, China"},{"name":"School of Computer Science, Queensland University of Technology, Brisbane City, QLD 4000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li Minn","family":"Ang","sequence":"additional","affiliation":[{"name":"School of Science Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5261-677X","authenticated-orcid":false,"given":"Anibal \u2018Nahuel\u2019 A.","family":"Pachas","sequence":"additional","affiliation":[{"name":"Department of Agriculture and Fisheries, Queensland Government, 1 Cartwright Road, Gympie, QLD 4570, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Lewis","sequence":"additional","affiliation":[{"name":"School of Science Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"ref_1","unstructured":"Lewis, T., Menzies, T., Brawner, J.T., Venn, T., Francis, B., Ryan, S., Schulke, B., Cameron, N., and Kelly, A. 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