Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( $R^2$ = 0.92–0.93) than traditional pantropical models ( $R^2$ = 0.85–0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( $R^2$ = 0.89) and predicted AGB accurately across all size classes—which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees.

Southern Africa experiences a great number of wildfires, but the dependence on low-resolution products to detect and quantify fires means both that there is a time lag and that many small fire events are never identified. This is particularly relevant in miombo woodlands, where fires are frequent and predominantly small. We developed a cutting-edge deep-learning-based approach that uses freely available Sentinel-2 data for near-real-time, high-resolution fire detection in Mozambique. The importance of Sentinel-2 main bands and their derivatives was evaluated using TreeNet, and the top five variables were selected to create three training datasets. We designed a UNet architecture, including contraction and expansion paths and a bridge between them with several layers and functions. We then added attention gate units (AUNet) and residual blocks and attention gate units (RAUNet) to the UNet architecture. We trained the three models with the three datasets. The efficiency of all three models was high (intersection over union (IoU) > 0.85) and increased with more variables. This is the first time an RAUNet architecture has been used to detect fire events, and it performed better than the UNet and AUNet models—especially for detecting small fires. The RAUNet model with five variables had IoU = 0.9238 and overall accuracy = 0.985. We suggest that others test the RAUNet model with large datasets from different regions and other satellites so that it may be applied more broadly to improve the detection of wildfires.

Most people in rural sub-Saharan Africa lack access to electricity and rely on traditional, inefficient, and polluting cooking solutions that have adverse impacts on both human health and the environment. Here, we propose a novel integrated agroforestry-bioenergy system that combines sustainable biomass production in sequential agroforestry systems with biomass-based cleaner cooking solutions and rural electricity production in small-scale combined heat and power plants and estimate the biophysical system outcomes. Despite conservative assumptions, we demonstrate that on-farm biomass production can cover the household’s fuelwood demand for cooking and still generate a surplus of woody biomass for electricity production via gasification. Agroforestry and biochar soil amendments should increase agricultural productivity and food security. In addition to enhanced energy security, the proposed system should also contribute to improving cooking conditions and health, enhancing soil fertility and food security, climate change mitigation, gender equality, and rural poverty reduction.

Summary: Wood density is central for estimating vegetation carbon storage and a plant functional trait of great ecological and evolutionary importance. However, the global extent of wood density variation is unclear, especially at the intraspecific level. We assembled the most comprehensive wood density collection to date, including 109 626 records from 16 829 plant species across woody life forms and biomes (GWDD v.2, available here: doi: 10.5281/zenodo.16919509 ). Using the GWDD v.2, we explored the sources of wood density variation within individuals, within species and across environmental gradients. Intraspecific variation accounted for c . 15% of overall wood density variation (SD = 0.068 g cm ⁻³ ). Variance was 50% smaller in sapwood than heartwood, and 30% smaller in branchwood than trunkwood. Individuals in extreme environments (dry, hot and acidic soils) had higher wood density than conspecifics elsewhere (+0.02 g cm ⁻³ , c . 4% of the mean). Intraspecific environmental effects strongly tracked interspecific patterns ( r = 0.83) but were 70–80% smaller and varied considerably among taxa. Individual plant wood density was difficult to predict (root mean square error > 0.08 g cm ⁻³ ; single‐measurement R ² = 0.59). We recommend: (1) systematic sampling of multiple individuals and tissues for local applications; and (2) expanded taxonomic coverage combined with integrative models for robust estimates across ecological scales.

Wood density influences how quickly woody plants grow, how long they live and how much carbon they store, yet its global variation remains poorly mapped. Here we combined 109,626 wood density measurements from 16,829 species with 300,949 vegetation plots to produce a km-scale map of community-weighted wood density for every woody biome. Our model led to a prediction accuracy 32–51 % higher than previous global products, and a 1.8–3.7-fold wider wood density range (0.28–1.00 g cm ⁻³ ; global mean: 0.57 g cm ⁻³ ) than previously assumed. Spatial cross-validation showed low bias (±2.5 % of the mean), and uncertainties decreased from 20% in poorly sampled drylands and boreal regions to 5% in data-rich temperate forests. Mean annual temperature was the best predictor of community-weighted mean wood density, increasing by 0.01 g cm ⁻³ for every 1°C change. We deliver a low-bias, high-resolution wood density layer for Earth system models, together with spatially explicit error maps. This study represents a major step forward for carbon accounting and trait-based forecasts of vegetation change.

This study assessed the structure and composition of a Miombo woodland stand subjected to selective logging through a forest inventory, measuring all trees with DBH ≥ 10 cm across 34 plots (1 ha each) for diameter, height, stem quality, and health status. The stand had a mean stem density of 255 stems/ha, basal area of 15 m²/ha, above ground biomass of 110 Mg/ha, and total volume of 145 m³/ha. The Fabaceae family, particularly Brachystegia spiciformis, dominated the composition. Diversity indices revealed moderate diversity (Shannon = 2.3, Simpson = 0.8, Pielou = 0.6), with a few dominant species. The diameter distribution followed a reverse J-shaped pattern typical of Miombo woodlands. The study (LevasFlor. (2024). Plano De Maneio Da LevasFlor, LDA) highlighted common features of selectively logged woodlands, including a low occurrence of large-diameter individuals from high-value commercial species, prevalence of disturbance-tolerant species, and limited regeneration for some species. These findings underscore the need for management strategies that balance ecological and socio-economic factors, mitigate logging impacts, promote regeneration, and ensure long-term sustainability. Effective policies are crucial for maintaining the ecological integrity and economic value of Miombo woodlands while addressing climate resilience and biodiversity conservation.