Species-specific aboveground biomass estimation using semantic segmentation of UAV photogrammetric point clouds

Accurate, species-specific estimation of aboveground biomass (AGB) at the individual plant level is essential for characterizing forest structure, supporting ecological and wildfire modelling, and enabling fine-scale carbon accounting. This study presents a methodological framework for estimating species-specific AGB at individual plant level in Mediterranean ecosystems using UAV-based digital aerial photogrammetry (UAV-DAP). High-resolution point clouds were processed through a multi-step workflow including object-based segmentation, thirteen species classification and AGB regression modeling. The overall accuracy of species classification across six study areas was 81.6%, with a maximum of 89.9%. The regression models for AGB estimation yielded an average R 2 of 0.69 across all species, highlighting species such as Anthyllis cytisoides (R 2 = 0.83, RMSE = 0.07 kg, n = 47), Juniperus oxycedrus (R 2 = 0.83, RMSE = 3.17 kg, n = 32); or Pinus halepensis (R 2 = 0.77, RMSE = 11.79 kg, n = 20). These findings demonstrate the potential of UAV-DAP for practical estimates of AGB. The study underscores UAV-DAP as a cost-effective tool for forest management, ecological monitoring, and biomass assessments, paving the way for broader applications in environmental science and resource management.