Light Detection and Ranging (LiDAR) is a remote sensing technology that measures distance by emitting short pulses of laser light and recording the time it takes for those pulses to return after reflecting off objects.
Because the speed of light is constant, this time-of-flight measurement allows LiDAR systems to calculate distance with extremely high precision—often to within centimetres or better. Repeating this process millions of times per second produces a dense, three-dimensional representation of the observed environment.
In effect, LiDAR does not infer shape or structure. It measures it directly.
Source: https://interpine.nz/lidar-seeing-the-forest-for-the-trees/
When applied to forests, LiDAR pulses penetrate through gaps in the canopy, reflecting off leaves, branches, trunks, understory vegetation, and ultimately the ground. Each returned pulse is recorded as a point in three-dimensional space.
The result is a 3D point cloud that captures:
This vertical information is precisely what most other remote sensing methods lack.
Most Earth observation systems rely on indirect signals:
Both approaches depend heavily on proxies and models to estimate biomass.
LiDAR, by contrast, directly measures forest structure in three dimensions. Because forest biomass is strongly correlated with physical structure—height, volume, and density—LiDAR enables physics-based estimation rather than statistical inference.
This distinction is critical for accuracy, repeatability, and transparency.
Source: https://4sense.medium.com/lidar-vs-radar-detection-tracking-and-imaging-ca528c0e9aae
Carbon stored in forests is not directly visible, but it is physically embodied in biomass. Decades of ecological research have established strong, stable relationships between tree structure and carbon content.
By measuring structure precisely, LiDAR provides a reliable foundation for estimating:
When combined with ground calibration plots, LiDAR dramatically reduces uncertainty compared to methods that rely on surface indicators alone.
LiDAR’s advantages for forest carbon measurement include:
As soon as LiDAR is introduced into forest carbon workflows, uncertainty drops substantially—often from 20–60% to around 10%.
This improvement is not due to better modelling. It is due to better measurement.
LiDAR does not replace ecological understanding or ground truthing. Instead, it provides a measurement backbone upon which models and samples can be anchored.
This distinction matters. Models can diverge. Proxies can saturate. Measurements constrain both.
To fully realise LiDAR’s potential for global forest carbon monitoring, however, scale and repeatability become decisive factors.
That raises the next question: what impact could we have if we could do a global LiDAR scan every year?
The answer lies in the next section.