From point clouds to biomass: comparing QSM algorithms for mature Mediterranean trees with destructive validation

Abstract

Accurate biomass estimation in large, mature trees is essential for understanding their critical roles in carbon sequestration, biodiversity conservation, and ecosystem function. However, destructive sampling of large and ecologically valuable trees is rarely feasible. Here, we took advantage of an opportunity provided by scheduled forestry operations, in which 35 mature Mediterranean tree individuals (Pinus halepensis, Pinus pinaster, and Quercus pyrenaica) were already planned for removal independently of this study. Using this rare destructive reference dataset, we developed and compared detailed workflows for biomass estimation based on terrestrial laser scanning (TLS) and four quantitative structure modelling (QSM) algorithms: TreeQSM, AdQSM, aRchi, and SimpleForest. For each algorithm, we established rigorous, species-specific protocols for calibration and parameter tuning, and systematically evaluated their relative strengths through novel comparative metrics. Our results showed strong correlations between TLS-derived estimates and actual biomass measurements. They also highlight clear method-specific algorithmic advantages. TreeQSM reliably estimated stem volumes (particularly in conifers), while AdQSM and aRchi better captured complex crown structures, with aRchi being particularly effective for broadleaf species. In contrast, SimpleForest consistently produced the poorest predictions across species and variables, underscoring its limited suitability under the conditions evaluated. Beyond validating algorithm performance, our methodological approach provides robust guidelines and objective metrics for future comparative assessments of QSM-based biomass estimation workflows. Ultimately, this study demonstrates that precise, reproducible, and non-destructive biomass quantification of mature trees is achievable, offering new pathways for accurate forest carbon accounting, and ecosystem monitoring without compromising irreplaceable large trees.