Above Ground Biomass Estimation of Individual Urban Trees With Hyperspectral and Lidar Data

Urban trees are crucial in climate change mitigation through carbon storage in their Aboveground Biomass (AGB). Our study explores the effectiveness of UAV-borne hyperspectral and LiDAR data in assessing AGB in tropical urban trees. We gathered structural data, including diameter at breast height (DBH), total height, and wood density, from 205 individual trees to create a detailed AGB dataset. Our approach combined these structural attributes with canopy height and the spectral characteristics of the trees. The findings indicate that incorporating tree height and crown area enhances AGB estimation, with the R
²
value increasing from 0.59±0.08 to 0.81±0.07. However, adding hyperspectral data to LiDAR did not markedly improve AGB estimation for individual trees, which are predominantly influenced by structural factors like tree height and crown area. This study highlights the potential and limitations of integrated UAV-based sensing techniques in urban forestry biomass evaluations.