Above-ground biomass references for urban trees from terrestrial laser scanning data.

BACKGROUND AND AIMS: Within extending urban areas, trees serve a multitude of functions (e.g. carbon storage, suppression of air pollution, mitigation of the 'heat island' effect, oxygen, shade and recreation). Many of these services are positively correlated with tree size and structure. The quantification of above-ground biomass (AGB) is of especial importance to assess its carbon storage potential. However, quantification of AGB is difficult and the allometries applied are often based on forest trees, which are subject to very different growing conditions, competition and form. In this article we highlight the potential of terrestrial laser scanning (TLS) techniques to extract highly detailed information on urban tree structure and AGB. METHODS: Fifty-five urban trees distributed over seven cities in Switzerland were measured using TLS and traditional forest inventory techniques before they were felled and weighed. Tree structure, volume and AGB from the TLS point clouds were extracted using quantitative structure modelling. TLS-derived AGB estimates were compared with AGB estimates based on forest tree allometries dependent on diameter at breast height only. The correlations of various tree metrics as AGB predictors were assessed. KEY RESULTS: Estimates of AGB derived by TLS showed good performance when compared with destructively harvested references, with an R2 of 0.954 (RMSE = 556 kg) compared with 0.837 (RMSE = 1159 kg) for allometrically derived AGB estimates. A correlation analysis showed that different TLS-derived wood volume estimates as well as trunk diameters and tree crown metrics show high correlation in describing total wood AGB, outperforming tree height. CONCLUSIONS: Wood volume estimates based on TLS show high potential to estimate tree AGB independent of tree species, size and form. This allows us to retrieve highly accurate non-destructive AGB estimates that could be used to establish new allometric equations without the need for extensive destructive harvesting.

Remote sensing of forest gas exchange: Considerations derived from a tomographic perspective.

The global exchange of gas (CO2 , H2 O) and energy (sensible and latent heat) between forest ecosystems and the atmosphere is often assessed using remote sensing (RS) products. Although these products are essential in quantifying the spatial variability of forest-atmosphere exchanges, large uncertainties remain from a measurement bias towards top of canopy fluxes since optical RS data are not sensitive for the vertically integrated forest canopy. We hypothesize that a tomographic perspective opens new pathways to advance upscaling gas exchange processes from leaf to forest stands and larger scales. We suggest a 3D modelling environment comprising principles of ecohydrology and radiative transfer modelling with measurements of micrometeorological variables, leaf optical properties and forest structure, and assess 3D fields of net CO2 assimilation (An ) and transpiration (T) in a Swiss temperate forest canopy. 3D simulations were used to quantify uncertainties in gas exchange estimates inherent to RS approaches and model assumptions (i.e. a big-leaf approximation in modelling approaches). Our results reveal substantial 3D heterogeneity of forest gas exchange with top of canopy An and T being reduced by up to 98% at the bottom of the canopy. We show that a simplified use of RS causes uncertainties in estimated vertical gas exchange of up to 300% and that the spatial variation of gas exchange in the footprint of flux towers can exceed diurnal dynamics. We also demonstrate that big-leaf assumptions can cause uncertainties up to a factor of 10 for estimates of An and T. Concluding, we acknowledge the large potential of 3D assessments of gas exchange to unravelling the role of vertical variability and canopy structure in regulating forest-atmosphere gas and energy exchange. Such information allows to systematically link canopy with global scale controls on forest functioning and eventually enables advanced understanding of forest responses to environmental change.