Forestry Biomass and Bioproducts

Forestry biomass encompasses all woody material from living trees, forest residues and by-products of wood-processing industries. When harvested and converted, it can yield a spectrum of bioproducts, from energy carriers—heat, power and transport fuels—to high-value chemicals and materials. Recent advances in high-resolution mapping and machine-learning‐enabled sensing have transformed how we quantify carbon stocks, helping to inform sustainable management, climate mitigation and landscape restoration. Parallel innovations in remote‐sensing inversion models and coupled allometric frameworks have sharpened our ability to estimate biomass at tree and stand scales, underpinning robust carbon accounting. At the same time, emerging utilisation pathways—ranging from lignin valorisation in biorefinery schemes to next-generation biofuels—promise to extend the traditional forest sector into a circular bioeconomy, creating new markets for forest owners and processors while safeguarding ecosystem services.

Research from Nature Portfolio

Sub‐continental mapping of individual tree carbon stocks in African drylands combined over 300,000 satellite images with field data, machine learning and high‐performance computing to attribute wood, foliage and root carbon to nearly 10 billion trees. The work revealed spatial gradients in per‐tree carbon density across arid and sub‐humid zones and established a linked database for policy and restoration planners, demonstrating the power of high‐resolution mapping for dryland carbon accounting.

A nanosatellite‐based continental tree‐cover map for Africa harnessed daily very high‐resolution imagery to detect individual trees both inside and outside conventional forest boundaries. The prototype 2019 map showed that 29 percent of Africa’s tree cover lies beyond previously classified forest areas, underscoring the importance of inclusive monitoring for natural climate solutions and land‐use planning.

Landscape‐level monitoring of smallholder farmland in Rwanda used satellite chronosequences to quantify the carbon contributions of newly planted trees over a decade. Results indicated that on‐farm trees form moderate carbon sinks compared with restored natural forests, highlighting the potential of integrating agroforestry monitoring into national greenhouse‐gas inventories to support net‐zero ambitions.

Forestry Biomass and Bioproducts publication trend

The graph below shows the total number of articles in forestry biomass and bioproducts across all publications each year (not limited to Nature Index journals).

Technical terms

Above‐ground biomass (AGB): The total mass of living vegetation above the soil, including stems, branches and leaves, per unit area.

Allometry: The study of relationships between tree dimensions (e.g. diameter, height, crown width) used to infer biomass from measurable structural variables.

LiDAR (Light Detection and Ranging): A remote‐sensing technique that emits laser pulses to generate three‐dimensional point clouds representing canopy and terrain structure.

Minkowski convolutional neural network: A deep‐learning architecture designed to perform convolution in three‐dimensional space, well suited to dense point‐cloud regression tasks.

Nanosatellite constellation: A coordinated network of small satellites providing frequent very‐high‐resolution imagery for environmental monitoring.

Chronosequence analysis: A remote‐sensing method comparing imagery from different years to infer temporal changes in land cover or biomass over time.

References

1. Sub-continental-scale carbon stocks of individual trees in African drylands. Nature (2023).
2. More than one quarter of Africa’s tree cover is found outside areas previously classified as forest. Nature Communications (2023).
3. Trees on smallholder farms and forest restoration are critical for Rwanda to achieve net zero emissions. Communications Earth & Environment (2024).
4. Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR. Remote Sensing of Environment (2024).
5. Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Global Change Biology (2016).