Integrating Terrestrial and Airborne LiDAR for Estimation Carbon Stock from Aboveground Biomass

Estimating the carbon stock is one method of implementing precise data-based mitigation strategies for the worldwide problem of climate change brought on by growing greenhouse gas (GHG) emissions. Carbon stock estimation from Aboveground Biomass (AGB) requires precise and comprehensive information on vegetation structure. This study aims to integrate Terrestrial and Airborne LiDAR data to improve the accuracy of AGB and carbon stock estimation at the individual tree level. Data were acquired through 65 TLS scanning points as well as UAV-based ALS acquisition with a direct georeferencing system. The point cloud integration results showed high geometric accuracy with an RMSE value of 0.064 m. Individual tree segmentation successfully identified 48 trees with an F-score of 0.98. Evaluation of structural parameters showed that diameter at breast height (DBH) and tree height extracted from LiDAR data had an RMSE of 0.65 cm and 1.24 cm, respectively, against manual field measurements. Interparameter correlations of DBH, height and AGB showed significant positive relationships, with very strong correlation coefficients (r > 0.99) between LiDAR results and field reference data. These results show that the integration of ALS and TLS is an effective and high-precision approach to support biomass and carbon stock estimation, and can be an alternative solution for carbon inventory activities at large scales and difficult to reach manually as part of climate change mitigation initiatives.