Automated Detection of Individual Tree Parameters using Terrestrial Laser Scanning Data

Authors

  • Mohamad Amirul Hafiz Md Shukri Studies for Surveying Science and Geomatics, College of Built Environment, Universiti Teknologi MARA, Cawangan Perlis, Kampus Arau, 02600 Arau, Perlis, Malaysia
  • Zulkiflee Abd Latif Studies for Surveying Science and Geomatics, College of Built Environment, Universiti Teknologi MARA, Cawangan Perlis, Kampus Arau, 02600 Arau, Perlis, Malaysia
  • Nurul Ain Mohd Zaki Studies for Surveying Science and Geomatics, College of Built Environment, Universiti Teknologi MARA, Cawangan Perlis, Kampus Arau, 02600 Arau, Perlis, Malaysia
  • Biswajeet Pradhan Centre for Advanced Modelling and Geospatial Information Systems, School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia
  • Hamdan Omar Geoinformation Programme, Division of Forestry & Environment, Forest Research Institute Malaysia (FRIM), 52109 Kepong, Selangor, Malaysia

DOI:

https://doi.org/10.24191/bej.v22i1.2265

Keywords:

Laser Scanning, Tropical Forest, LiDAR, Aboveground biomass, Tree detection

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.

Author Biographies

Mohamad Amirul Hafiz Md Shukri, Studies for Surveying Science and Geomatics, College of Built Environment, Universiti Teknologi MARA, Cawangan Perlis, Kampus Arau, 02600 Arau, Perlis, Malaysia

Mohamad Amirul Hafiz Md Shukri holds a degree in Surveying Science & Geomatics from College of Built Environment, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia. Currently he is pursuing Masters of Built Environment by research. His research interests are primarily in geospatial technology for forest management. He can be contacted by email at mohamadamirul.hafi7@gmail.com.

Zulkiflee Abd Latif, Studies for Surveying Science and Geomatics, College of Built Environment, Universiti Teknologi MARA, Cawangan Perlis, Kampus Arau, 02600 Arau, Perlis, Malaysia

Zulkiflee Abd Latif received his PhD in Remote Sensing from Lancaster University, Lancaster, U.K., specialising in Remote Sensing. He is a Full Professor at the College of Built Environment, Universiti Teknologi MARA, Shah Alam, Selangor. His research interests are primarily Remote sensing & GIS techniques in forestry and environmental related applications. He is currently Head of Applied Remote Sensing & Geospatial Research Group, UiTM and a Fellow to Royal Institute of Surveyors, Malaysia (RISM). He can be  contacted by email at zulki721@uitm.edu.my.

Nurul Ain Mohd Zaki, Studies for Surveying Science and Geomatics, College of Built Environment, Universiti Teknologi MARA, Cawangan Perlis, Kampus Arau, 02600 Arau, Perlis, Malaysia

Nurul Ain Mohd Zaki obtained his PhD in Bulit Environment from Universiti Teknologi MARA, Shah Alam, Selangor. She is currently a Senior Lecturer at the the College of Built Environment, Universiti Teknologi MARA, Arau Campus, Perlis. Her main interests include Geomatics, GIS and Remote Sensing for forestry application. Her email is nurulain86@uitm.edu.my.

Biswajeet Pradhan, Centre for Advanced Modelling and Geospatial Information Systems, School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia

Biswajeet Pradhan (born 1975) is a spatial scientist, modeller, author and who is now working as a Distinguished Professor and the founding Director of the Centre for Advanced Modelling and Geo-spatial Information Systems (CAMGIS),[1] Faculty of Engineering and IT [2] at the University of Technology Sydney, Australia. He is working primarily in the fields of remote sensing, geographic information systems (GIS), complex modelling, machine learning and Artificial intelligence (AI) based algorithms and their application to natural hazards, natural resources and environmental problems. Many of his research outputs were put into practice. His research platform is mainly Asia and Australia, and he has been sharing his findings worldwide. He is also a permanent resident of Australia and Malaysia.

Hamdan Omar, Geoinformation Programme, Division of Forestry & Environment, Forest Research Institute Malaysia (FRIM), 52109 Kepong, Selangor, Malaysia

Hamdan Omar is currently a Research Officer at Forest Research Institute Malaysia (FRIM), Malaysia. He obtained his PhD in Forestry (Remote Sensing) from Universiti Putra Malaysia, Serdang, Selangor. His main interests include Geomatics, GIS and Remote Sensing for forestry application. His email is hamdanomar@frim.gov.my.

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Published

01-01-2025

How to Cite

Md Shukri, M. A. H. ., Abd Latif, Z., Mohd Zaki, N. A. ., Pradhan, B., & Omar, H. (2025). Automated Detection of Individual Tree Parameters using Terrestrial Laser Scanning Data . Built Environment Journal, 22(1). https://doi.org/10.24191/bej.v22i1.2265

Issue

Vol. 22 No. 1 (2025): Built Environment Journal

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Articles

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Copyright (c) 2024 Mohamad Amirul Hafiz Md Shukri, Zulkiflee Abd Latif, Nurul Ain Mohd Zaki, Biswajeet Pradhan, Hamdan Omar

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