• Energy Research

AbstractRemote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able – for the first time – to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed – specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large‐scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.

Tropical landscapes are relevant in their contribution to global climate regulation as potential carbon sink or source. The sequestered and emitted carbon is commonly approximated with the aboveground biomass (AGB). However, the estimation of AGB is to date mostly focussed on the estimation of one single point in time, where the accuracy is limited in particular on large spatial scales. The interferometric information (i.e. coherence and height) of high-frequency synthetic aperture radar (SAR) systems are considered particularly useful to estimate vegetation height and AGB. It is frequently assumed that the interfometric height of X-band systems like TanDEM-X represent the canopy surface height. Consequently, these interferometric SAR (InSAR) heights can be combined with terrain information to estimate vegetation canopy height and subsequently AGB. No spaceborne system exists to date to estimate the terrain height consistently on a global scale and thus the combination of TanDEM-X InSAR height and terrain information is normally limited to small spatial coverages. The potential to estimate AGB and in particular its change with such an approach is limited. In contrast, TanDEM-X InSAR heights can be directly compared over time. The differences can be assumed as differences in the canopy height assuming that the TanDEM-X InSAR heights represent the canopy surface height at a single point in time. The canopy height differences estimated by calculating the difference between bi- or multi-temporal TanDEM-X InSAR heights can be related to AGB differences. However, it was frequently found that the X-band signal penetrates into the canopy. This results in the fact that the X-band InSAR height is an approximation of the canopy surface height is not true. More importantly, the penetration depth can differ between different acquisitions depending on the acquisition properties and properties on the ground (e.g. moisture), which would result in pseudo-changes in the difference calculation of InSAR height models at different point in times. In our study, we used two TanDEM-X acquisitions from 2012 and 2019 covering a dynamic tropical area in Sumatra, Indonesia. We derived the InSAR heights for each acquisition date individually and calculated their difference. In addition, we assessed the penetration depth of the individual InSAR heights and modelled the penetration to compensate potential pseudo-changes. The absolute accuracy of the individual TanDEM-X heights was assessed with a LiDAR height model used as a reference. The TanDEM-X height differences were further related to ground-based AGB estimations from 2012 and 2019. This resulted in a significant linear relationship between height model and AGB differences, where the penetration compensated height models had a higher coefficient of determination and accuracy compared to the original InSAR heights. However, the accuracy was generally high in both cases with relative root mean square errors below 15%. This suggests that X-band height from TanDEM-X can be used to estimate canopy height differences and subsequently AGB changes on large spatial scale. However, the differences in penetration depth should not be neglected in order to avoid pseudo-changes and to estimate also small changes like degradation or growth.

AbstractData capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research—from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non‐forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC‐BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology—from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

AbstractOld-growth forests are essential to preserve biodiversity and play an important role in sequestering carbon and mitigating climate change. However, their existence across Europe is vulnerable due to the scarcity of their distribution, logging, and environmental threats. Therefore, providing the current status of old-growth forests across Europe is essential to aiding informed conservation efforts and sustainable forest management. Remote sensing techniques have proven effective for mapping and monitoring forests over large areas. However, relying solely on remote sensing spectral or structural information cannot capture comprehensive horizontal and vertical structure complexity profiles associated with old-growth forest characteristics. To overcome this issue, we combined spectral information from Sentinel-2A multispectral imagery with 3D structural information from high-density point clouds of airborne laser scanning (ALS) imagery to map old-growth forests over an extended area. Four features from the ALS data and fifteen from Sentinel-2A comprising raw band (spectral reflectance), vegetation indices (VIs), and texture were selected to create three datasets used in the classification process using the random forest algorithm. The results demonstrated that combining ALS and Sentinel-2A features improved the classification performance and yielded the highest accuracy for old-growth class, with an F1-score of 92% and producer’s and user’s accuracies of 93% and 90%, respectively. The findings suggest that features from ALS and Sentinel-2A data sensitive to forest structure are essential for identifying old-growth forests. Integrating open-access satellite imageries, such as Sentinel-2A and ALS data, can benefit forest managers, stakeholders, and conservationists in monitoring old-growth forest preservation across a broader spatial extent.