Accuracy differences in aboveground woody biomass estimation with terrestrial laser scanning for trees in urban and rural forests and different leaf conditions

Abstract

Both rural and urban forests play an important role in terrestrial carbon cycling. Forest carbon stocks are typically estimated from models predicting the aboveground biomass (AGB) of trees. However, such models are often limited by insufficient data on tree mass, which generally requires felling and weighing parts of trees. In this study, thirty-one trees of both deciduous and evergreen species were destructively sampled in rural and urban forest conditions. Prior to felling, terrestrial laser scanning (TLS) data were used to estimate tree biomass based on volume estimates from quantitative structure models, combined with tree basic density estimates from disks sampled from stems and branches after scanning and felling trees, but also in combination with published basic density values. Reference woody AGB, main stem, and branch biomass were computed from destructive sampling. Trees were scanned in leaf-off conditions, except evergreen and some deciduous trees, to assess effects of a leaf-separation algorithm on TLS-based woody biomass estimates. We found strong agreement between TLS-based and reference woody AGB, main stem, and branch biomass values, using both measured and published basic densities to convert TLS-based volume to biomass, but use of published densities reduced accuracy. Correlations between TLS-based and reference branch biomass were stronger for urban trees, while correlations with stem mass were stronger for rural trees. TLS-based biomass estimates from leaf-off and leaf-removed point clouds strongly agreed with reference biomass data, showing the utility of the leaf-removal algorithm for enhancing AGB estimation.