Development of Species-Specific Allometric Models for Aboveground Woody Biomass Estimation of Urban Trees Using Terrestrial Laser Scanning

Precise estimation of aboveground biomass in urban forests is crucial for quantifying urban carbon stocks and supporting climate change mitigation efforts. However, the availability of allometric equations tailored for urban trees is limited. Existing equations often rely on data from harvested trees with restricted sample sizes and small diameters, thereby introducing substantial uncertainty into biomass assessments. This study utilized terrestrial laser scanning (TLS) in conjunction with a leaf-wood separation algorithm and a tree quantitative structure model (TreeQSM) as a non-destructive approach to develop new species-specific allometric models for four predominant evergreen broadleaved tree species in the urban forests of Shanghai, based on 10 sample plots and 303 trees. The results showed that TLS-derived multivariate models, which incorporated diameter at breast height (DBH), tree height, and crown diameter, consistently outperformed models that only included DBH. Compared with the TLS-derived biomass, the previously published models showed varying degrees of deviation. Notably, there was a substantial overestimation for Camphora officinarum, with a bias of +31.5%. In contrast, Elaeocarpus decipiens, Ligustrum lucidum, and Magnolia grandiflora demonstrated smaller underestimations, with biases of −9.4%, −0.9%, and −4.5%, respectively. These discrepancies were primarily attributed to the extrapolation beyond the calibration diameter at DBH ranges of the published equations. These findings highlight the critical need for urban-specific models. Because destructive harvesting was not feasible in the urban environment, the TLS-derived biomass estimates were not validated against destructively measured biomass. The equations developed in this study provide improved tools for estimating urban forest biomass and carbon accounting for the four studied species under the sampled conditions in Shanghai.