Automated detection of individual tree parameters using terrestrial laser scanning data / Mohamad Amirul Hafiz Md Shukri ... [et al.]

Md Shukri, Mohamad Amirul Hafiz and Abd Latif, Zulkiflee and Mohd Zaki, Nurul Ain and Pradhan, Biswajeet and Omar, Hamdan (2025) Automated detection of individual tree parameters using terrestrial laser scanning data / Mohamad Amirul Hafiz Md Shukri ... [et al.]. Built Environment Journal, 22 (1): 6. pp. 71-85. ISSN 2637-0395

Abstract

The National Forest Inventory aims to provide current information on forest resources for planning, management, development, and maintenance purposes, as well as quantitative and qualitative data on forest resources. Although destructive sampling is the most accurate method for obtaining tree information, it requires substantial resources, is time-consuming, and labour-intensive. This study was undertaken to compare the effectiveness of Terrestrial Laser Scanning (TLS) in extracting tree parameters in comparison to conventional methods. The results revealed a strong positive correlation between field-measured Diameter Breast Height (DBH) and manually extracted DBH from TLS point cloud data, with an r value of 1.0 and a Root Mean Square Error (RMSE) of 1.48 cm. However, the relationship between field-measured height and manually extracted height from TLS point cloud exhibited a weak correlation, with an r value of 0.70 and an RMSE value of 7.9 m. In conclusion, TLS data has a significant impact on enhancing the management and monitoring of the inventory status of tropical forests in Malaysia.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Md Shukri, Mohamad Amirul Hafiz
UNSPECIFIED
Abd Latif, Zulkiflee
zulki721@uitm.edu.my
Mohd Zaki, Nurul Ain
UNSPECIFIED
Pradhan, Biswajeet
UNSPECIFIED
Omar, Hamdan
UNSPECIFIED
Subjects: T Technology > T Technology (General) > Industrial engineering. Management engineering > Automation
Divisions: Universiti Teknologi MARA, Shah Alam > College of Built Environment
Journal or Publication Title: Built Environment Journal
UiTM Journal Collections: Listed > Built Environment Journal (BEJ)
ISSN: 2637-0395
Volume: 22
Number: 1
Page Range: pp. 71-85
Keywords: Tree Detection, LiDAR, Laser scanning, Tropical Forest, Aboveground biomass
Date: January 2025
URI: https://ir.uitm.edu.my/id/eprint/110077
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