Quantifying climate mitigation benefits of biosphere protection or restoration requires accurate assessment of forest above‐ground biomass (AGB). This is usually estimated using tree size‐to‐mass allometric models calibrated with harvested biomass data. Using three‐dimensional laser measurements across the full range of tree size and shape in a typical UK temperate forest, we show that its AGB is 409.9 t ha⁻¹, 1.77 times more than current allometric model estimates. This discrepancy arises partly from the bias towards small trees in allometric model calibration: 50% of AGB in this forest was in less than 7% of the largest trees (stem diameter > 53.1 cm), all larger than the trees used to calibrate the widely used allometric model. We present new empirical evidence that the fundamental assumption of tree size‐to‐mass scale‐invariance is not well‐justified for this kind of forest. This leads to substantial biases in current biomass estimates of broadleaf forests, not just in the UK, but elsewhere where the same or similar allometric models are applied, due to overdependence on non‐representative calibration data, and the departure of observed tree size‐to‐mass from simple size‐invariant relationships. We suggest that testing the underlying assumptions of allometric models more generally is an urgent priority as this has wider implications for climate mitigation through carbon sequestration. Forests currently act as a carbon sink in the UK. However, the anticipated increase in forest disturbances makes the trajectory and magnitude of this terrestrial carbon sink uncertain. We make recommendations for prioritizing measurements with better characterized uncertainty to address this issue.

Amid a worldwide increase in tree mortality, mountain pine beetles (Dendroctonus ponderosae Hopkins) have led to the death of billions of trees from Mexico to Alaska since 2000. This is predicted to have important carbon, water and energy balance feedbacks on the Earth system. Counter to current projections, we show that on a decadal scale, tree mortality causes no increase in ecosystem respiration from scales of several square metres up to an 84 km² valley. Rather, we found comparable declines in both gross primary productivity and respiration suggesting little change in net flux, with a transitory recovery of respiration 6–7 years after mortality associated with increased incorporation of leaf litter C into soil organic matter, followed by further decline in years 8–10. The mechanism of the impact of tree mortality caused by these biotic disturbances is consistent with reduced input rather than increased output of carbon.

Urban trees have long been valued for providing ecosystem services (mitigation of the “heat island” effect, suppression of air pollution, etc.); more recently the potential of urban forests to store significant above ground biomass (AGB) has also be recognised. However, urban areas pose particular challenges when assessing AGB due to plasticity of tree form, high species diversity as well as heterogeneous and complex land cover. Remote sensing, in particular light detection and ranging (LiDAR), provide a unique opportunity to assess urban AGB by directly measuring tree structure. In this study, terrestrial LiDAR measurements were used to derive new allometry for the London Borough of Camden, that incorporates the wide range of tree structures typical of an urban setting. Using a wall-to-wall airborne LiDAR dataset, individual trees were then identified across the Borough with a new individual tree detection (ITD) method. The new allometry was subsequently applied to the identified trees, generating a Borough-wide estimate of AGB. Camden has an estimated median AGB density of 51.6 Mg ha^–1 where maximum AGB density is found in pockets of woodland; terrestrial LiDAR-derived AGB estimates suggest these areas are comparable to temperate and tropical forest. Multiple linear regression of terrestrial LiDAR-derived maximum height and projected crown area explained 93% of variance in tree volume, highlighting the utility of these metrics to characterise diverse tree structure. Locally derived allometry provided accurate estimates of tree volume whereas a Borough-wide allometry tended to overestimate AGB in woodland areas. The new ITD method successfully identified individual trees; however, AGB was underestimated by ≤ 25% when compared to terrestrial LiDAR, owing to the inability of ITD to resolve crown overlap. A Monte Carlo uncertainty analysis identified assigning wood density values as the largest source of uncertainty when estimating AGB. Over the coming century global populations are predicted to become increasingly urbanised, leading to an unprecedented expansion of urban land cover. Urban areas will become more important as carbon sinks and effective tools to assess carbon densities in these areas are therefore required. Using multi-scale LiDAR presents an opportunity to achieve this, providing a spatially explicit map of urban forest structure and AGB.

Above‐ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. Here we presentTLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck.TLS2treesconsists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. A total of 10,557 trees are segmented withTLS2trees: these are compared to 1281 manually segmented trees. Results indicate thatTLS2treesperforms well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot‐wise tree volume bias is ±0.4 m³and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step. The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such asTLS2trees, is required. To facilitate improvements toTLS2trees, as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning),TLS2treesis a free and open‐source software.