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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

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.

Original languageEnglish
Article number7872
JournalSensors
Volume22
Issue number20
DOIs
Publication statusPublished - Oct 2022

Keywords

  • digital photogrammetry
  • drone-based monitoring
  • forest resource estimation
  • image analysis
  • private native forests
  • remote sensing

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