Mapping Tree Crown Dynamics and Biomass Accumulation Using LiDAR-Derived Canopy Metrics Atikah Razaki(a), Nurul Ain Mohd Zaki(b)(e)*,Zulkiflee Abd Latif(c)(e), Mohd Zainee Zainal(b), Hamdan Omar(f) and Mohd Nazip Suratman(d)(e)
(a)Students, Faculty of Built Environment, Universiti Teknologi MARA, Perlis Branch, Arau Campus, 02600, Arau, Perlis, Malaysia
(b)Senior Lecturer, Faculty of Built Environment, Universiti Teknologi MARA, Perlis Branch, Arau Campus, 02600, Arau, Perlis, Malaysia
(c)Senior Lecturer, Faculty of Built Environment, Universiti Teknologi MARA, Shah Alam, 40450, Shah Alam, Malaysia
(d)Senior Lecturer, Faculty of Applied Sciences, Universiti Teknologi MARA, Shah Alam, 40450, Shah Alam, Malaysia
(e)Associate Fellow, Institute for Biodiversity and Sustainable Development, Universiti Teknologi MARA, Shah Alam, 40450, Malaysia
(f)Forest Research Institute Malaysia (FRIM), 68100 Kuala Lumpur, Federal Territory of Kuala Lumpur
Abstract
Tropical forests are critical carbon reservoirs that contribute significantly to climate regulation. Accurate monitoring of above-ground biomass (AGB) and carbon stock changes is essential for understanding forest dynamics and supporting climate change mitigation policies. This study evaluates the temporal changes in AGB and carbon stock at the Forest Research Institute Malaysia (FRIM), Kepong, by utilizing high-resolution airborne Light Detection and Ranging (LiDAR) datasets acquired in 2009 and 2014. The methodology involved three main phases: LiDAR data pre-processing, generation of Canopy Height Models (CHMs), and individual tree crown (ITC) delineation using a watershed segmentation algorithm. Local maxima detection was applied to the CHM raster to identify tree tops, which served as seeds for watershed transformation. The delineated crowns enabled the extraction of tree height and crown projection area (CPA) for individual trees. Statistical analysis, including a paired sample t-test, revealed a significant increase in both tree height and CPA between 2009 and 2014. Mapping outputs visualized spatial distribution and changes in carbon stock, highlighting areas with the most significant growth. The integration of multitemporal LiDAR and remote sensing techniques proved effective for non-destructive, large-scale forest monitoring. This research underscores the value of LiDAR technology in enhancing the accuracy of forest biomass assessments and contributes to the development of robust methodologies for carbon accounting in tropical forest ecosystems.
