Temporal Analysis of Forest Biomass and Carbon Sequestration Using Bi-LiDAR: A 12-Year Case Study in Lake Broadwater Forest, Queensland, Australia.

Forest biomass estimation plays a vital role in quantifying and measuring carbon sequestered from the atmosphere as a mitigation to global climate change. Light detection and ranging (LiDAR) a remote sensing technology, offers detailed 3D structural forest metrics such as tree height (H), canopy volume (CV), canopy diameter (CD), canopy area (CA) and tree stand density (ρ), which act as inputs into allometric equations to estimate biomass. The primary objective of this study is to compare biomass and carbon sequestration data using bi-temporal LiDAR data from 2012 and 2022 from Lake Broadwater Forest in Southeast Queensland, Australia. This study utilised the Jucker Model, one of the global pantropical models, to estimate diameter at breast height (DBH) and the Chave Model to estimate biomass. As expected, after 10 years, the LiDAR-derived tree metrics doubled to trebled, and AGB estimation went up ten times between 2012 and 2022. The Lake Broadwater Forest was estimated to have an AGB of 235.7 Mg ha⁻¹in 2024, 161.5 Mg ha⁻¹ in 2022, and 16.5 Mg ha⁻¹ in 2012. Carbon dioxide (CO₂) sequestered was 376.6 Mg ha⁻¹ in 2024, 257.8 Mg ha⁻¹ in 2022 and 26.3 Mg ha⁻¹ in 2012. These findings demonstrate the effectiveness of LiDAR-based remote sensing technology for long-term biomass monitoring and highlight the role of forests in achieving net-zero targets. This study also provides a pathway for farmers and resource developers, including coal seam gas (CSG) companies, to engage in carbon farming and benefit from carbon credit schemes such as the Australian Carbon Credit Unit (ACCU) program. The findings can also inform the development of forest management strategies and support policymakers' decision-making.