Machine learning for estimation of tree inventory parameters using terrestrial laser scanning, photogrammetry and UAV data

Poster


Kuznetsov, Igor, Zhang, Zhenyu, Liu, Xiaoye and Subedi, Nab Raj. 2023. "Machine learning for estimation of tree inventory parameters using terrestrial laser scanning, photogrammetry and UAV data ." 2023 Asian Conference on Remote Sensing (ACRS2023). Taipei, Taiwan 30 Oct - 03 Nov 2023 Taiwan.
Paper/Presentation Title

Machine learning for estimation of tree inventory parameters using terrestrial laser scanning, photogrammetry and UAV data

Presentation TypePoster
AuthorsKuznetsov, Igor, Zhang, Zhenyu, Liu, Xiaoye and Subedi, Nab Raj
Journal or Proceedings TitleProceedings of the 2023 Asian Conference on Remote Sensing (ACRS 2023)
Article NumberACRS2023309
Number of Pages9
Year2023
Place of PublicationTaiwan
Web Address (URL) of Conference Proceedingshttps://acrs2023.tw/conference_proceedings.php
Conference/Event2023 Asian Conference on Remote Sensing (ACRS2023)
Event Details
2023 Asian Conference on Remote Sensing (ACRS2023)
Delivery
In person
Event Date
30 Oct 2023 to end of 03 Nov 2023
Event Location
Taipei, Taiwan
Event Venue
Nangang International Exhibition Center, Hall2
Event Web Address (URL)
Abstract

Accurate estimation of tree inventory parameters is crucial for promoting sustainable forest management,
conserving native forests, and developing precise biomass models. However, traditional methods for measuring these tree
parameters are both time-consuming and labour-intensive. Moreover, the results obtained from these traditional methods
lack the required accuracy for precision forest management and above-ground biomass modelling. To address these
challenges, this study aims to leverage remotely sensed data from various sources, including terrestrial laser scanning
(TLS), photogrammetry, and UAV data, and combine them with machine learning techniques to estimate tree parameters
more effectively. The research was conducted within the Australian Native Woodland Reserve in southeast Queensland,
Australia. The TLS data and photogrammetry data provide detailed information not only from the ground but also from
within the canopy, while the UAV data offer a top-down view of the forests. The machine learning methods employed in
this study included the random forest method and density-based spatial clustering of applications with noise (DBSCAN)
method. By using these techniques, the researchers were able to extract native tree parameters with improved efficiency
and accuracy. The results demonstrated the success of integrating TLS, photogrammetry, and UAV data for estimating
tree inventory parameters in the Australian Native Woodland Reserve. The study found that TLS point clouds were
particularly well-suited for extracting most tree parameters, while terrestrial photogrammetry data proved quite accurate
in determining the diameter at breast height (DBH) of trees. The UAV data performed well in estimating height-related
parameters of the trees. Overall, the integration of machine learning methods with multiple remote sensing data sources
enhanced both the efficiency and accuracy of tree parameter estimation. This research contributes valuable insights that
can aid in better forest management and conservation efforts in the future.

KeywordsTree inventory parameter, Laser scanning, UAV, Photogrammetry, Machine learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020370499. Geoinformatics not elsewhere classified
460502. Data mining and knowledge discovery
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Byline AffiliationsSchool of Surveying and Built Environment
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