Forest biophysical variables derived from remote sensing observations are vital for climate research. The combination of structurally and radiometrically accurate 3D “virtual” forests with radiative transfer (RT) models creates a powerful tool to facilitate the calibration and validation of remote sensing data and derived biophysical products by helping us understand the assumptions made in data processing algorithms. We present a workflow that uses highly detailed 3D terrestrial laser scanning (TLS) data to generate virtual forests for RT model simulations. Our approach to forest stand reconstruction from a co-registered point cloud is unique as it models each tree individually. Our approach follows three steps: (1) tree segmentation; (2) tree structure modelling and (3) leaf addition. To demonstrate this approach, we present the measurement and construction of a one hectare model of the deciduous forest in Wytham Woods (Oxford, UK). The model contains 559 individual trees. We matched the TLS data with traditional census data to determine the species of each individual tree and allocate species-specific radiometric properties. Our modelling framework is generic, highly transferable and adjustable to data collected with other TLS instruments and different ecosystems. The Wytham Woods virtual forest is made publicly available through an online repository.

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.

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.

With a wide range of satellite-derived vegetation bio-geophysical products now available to users, validation efforts are required to assess their accuracy and fitness for purpose. Substantial progress in the validation of such products has been made over the last two decades, but quantification of the uncertainties associated with in situ reference measurements is rarely performed, and the incorporation of uncertainties within upscaling procedures is cursory at best. Since current validation practices assume that reference data represent the truth, our ability to reliably demonstrate compliance with product uncertainty requirements through conformity testing is limited. The Fiducial Reference Measurements for Vegetation (FRM4VEG) project, initiated by the European Space Agency, is aiming to address this challenge by applying metrological principles to vegetation and surface reflectance product validation. Following FRM principles, and in accordance with the International Standards Organisation’s (ISO) Guide to the Expression of Uncertainty in Measurement (GUM), for the first time, we describe an end-to-end uncertainty evaluation framework for reference data of two key vegetation bio-geophysical variables: the fraction of absorbed photosynthetically active radiation (FAPAR) and canopy chlorophyll content (CCC). The process involves quantifying the uncertainties associated with individual in situ reference measurements and incorporating these uncertainties within the upscaling procedure (as well as those associated with the high-spatial-resolution imagery used for upscaling). The framework was demonstrated in two field campaigns covering agricultural crops (Las Tiesas–Barrax, Spain) and deciduous broadleaf forest (Wytham Woods, UK). Providing high-spatial-resolution reference maps with per-pixel uncertainty estimates, the framework is applicable to a range of other bio-geophysical variables including leaf area index (LAI), the fraction of vegetation cover (FCOVER), and canopy water content (CWC). The proposed procedures will facilitate conformity testing of moderate spatial resolution vegetation bio-geophysical products in future validation exercises.

Detailed 3D quantification of tree structure plays a crucial role in understanding tree‐ and plot‐level biophysical processes. Light detection and ranging (LiDAR) has led to a revolution in tree structural measurements and its 3D data are increasingly becoming publicly available. Yet, calculating structural metrics from LiDAR data can often be complex and time‐consuming and potentially requires expert knowledge. We present the R package Individual Tree Structural Metrics (ITSMe), a toolbox that works with LiDAR tree point clouds and quantitative structure models (QSMs) derived from LiDAR point clouds to obtain individual tree structural metrics. It serves as a robust synthesis framework for researchers who want to readily obtain structural information from 3D data of individual trees. The package includes functions to determine basic structural metrics (tree height, diameter at breast height, diameter above buttresses, projected crown area, 3D alpha crown volume) from individual tree point clouds, as well as more complex structural metrics (individual tree component volumes, branch angle‐, radius‐ and length‐related metrics) from QSMs. The ITSMe package is an open‐source package hosted on GitHub that will make the use of 3D data more straightforward and transparent for a range of end‐users interested in exploiting tree structure information.

Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large‐scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three‐dimensional vegetation structure at local scales. Here we demonstrate the potential of high‐frequency ground‐based laser scanning to systematically monitor structural changes in vegetation. We present a proof‐of‐concept high‐temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high‐temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.