Remote Sensing Techniques for Forest Biomass Assessment

Remote sensing has transformed forest biomass estimation by providing scalable, repeatable and spatially explicit observations of vegetation structure. Multispectral optical imagery offers canopy reflectance indices correlated with leaf area and photosynthetic activity. Microwave sensors, including synthetic aperture radar (SAR), penetrate canopy layers to infer canopy height and moisture content. Airborne and spaceborne LiDAR generate three-dimensional point clouds that capture tree height, crown dimensions and vertical structure crucial for allometric biomass models. Emerging nanosatellite constellations deliver very high-resolution daily imagery, enabling wall-to-wall mapping of individual trees outside traditional forest boundaries. Integration of these datasets with field-derived allometric equations and advanced machine-learning algorithms improves above-ground biomass (AGB) estimation. Such synergistic approaches underpin accurate carbon accounting, guide sustainable forest management and inform climate mitigation and restoration initiatives worldwide.

Research from Nature Portfolio

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

A prototype continental-scale map of tree cover in Africa utilised a nanosatellite constellation to capture very high-resolution daily imagery, enabling detection of forest and non-forest trees at the individual level. This study showed that 29 percent of Africa’s tree cover lies outside previously classified forest areas, highlighting the importance of inclusive mapping for natural climate solutions and land-use planning.

Satellite chronosequence analysis of smallholder farmland in Rwanda quantified the carbon contributions of newly planted trees over a decade. Results indicated moderate on-farm carbon sinks compared with restored natural forests and underscored the potential of integrating agroforestry monitoring into national greenhouse gas inventories to support net-zero ambitions.

Remote Sensing Techniques for Forest Biomass Assessment publication trend

The graph below shows the total number of articles in remote sensing techniques for forest biomass assessment 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, expressed per unit area and typically including stems, branches and leaves.

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

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

Point cloud: A dataset of spatial points in three dimensions produced by LiDAR, representing the geometric surface of trees and other objects.

Synthetic aperture radar (SAR): An active microwave imaging technique capable of penetrating canopy layers to retrieve structural information on vegetation and ground.

Spectral saturation: A phenomenon in optical sensors where reflectance values plateau at high biomass levels, limiting sensitivity to further increases in vegetation density.

Nanosatellite constellation: A network of small satellites operating in concert to provide frequent, high-resolution imagery for environmental monitoring.

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).
6. Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation. Remote Sensing (2016).