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Why LiDAR for forests

The central challenge in forest carbon accounting is not estimating how much carbon exists in forests, but how that amount changes over time. This change, forest carbon flux, determines whether a forest is acting as a net carbon sink or source.

Accurately measuring flux is inherently difficult. Forests grow unevenly, respond differently to climate stress, and are affected by disturbances such as fire, drought, pests, and land-use change. These dynamics vary across space and time, making precise, repeatable measurement essential.

How forest carbon flux is measured today

Most forest carbon accounting systems rely on a combination of:

• Ground-based sample plots, where tree diameter, height, and species are measured and extrapolated over large areas
• Optical satellite imagery, used to infer vegetation cover, greenness, and change
• Synthetic Aperture Radar (SAR), which can penetrate cloud cover and provide limited structural information

These methods are typically integrated into statistical or process-based models to estimate biomass and carbon change.

While effective for broad-scale monitoring, they suffer from fundamental limitations when used for high-confidence accounting.

Source: https://www.ctpublic.org/environment/2018-12-11/how-do-you-measure-the-amount-of-carbon-in-a-tree

Sources of uncertainty

Uncertainty in forest carbon flux arises from multiple compounding factors:

• Sparse sampling: Ground plots cover tiny fractions of total forest area, requiring large extrapolations
• Indirect inference: Optical and radar data rely on proxies rather than direct measurement of biomass
• Saturation effects: Optical indices and SAR signals saturate in dense forests, losing sensitivity where most carbon is stored
• Model divergence: Different models and assumptions produce materially different results from the same inputs
• Temporal inconsistency: Year-to-year changes are often smaller than measurement error

As a result, peer-reviewed studies consistently report uncertainty ranges of 20% to 60% in estimates of forest carbon change, even in well-studied regions.

Why this uncertainty matters

High uncertainty has systemic consequences:

• Credits must be discounted or buffered, reducing their climate value
• Buyers lose confidence, depressing market prices and demand
• Policy decisions rely on noisy signals, increasing the risk of misallocation
• Systematic bias can go undetected, leading to cumulative over-crediting

Crucially, uncertainty is not symmetrical. Overestimation of removals carries far greater climate risk than underestimation, yet current systems struggle to detect or correct persistent bias.

Uncertainty compounds over time

Forest carbon projects operate over decades. A small annual error, if repeated over time, can accumulate into a large divergence between reported and actual climate outcomes.

As carbon budgets shrink and temperature thresholds approach, this compounding effect becomes increasingly dangerous. The climate system responds to physical reality, not reported estimates.

In this context, uncertainty is no longer a tolerable cost of doing business. It is an active source of risk.

The need for direct observability

Reducing uncertainty requires a shift from indirect inference toward direct observation of forest structure at scale. Without this, improvements will remain incremental, constrained by the same fundamental limitations.

Understanding what that shift looks like, and why it is now technically feasible, requires first understanding the measurement technology capable of delivering it.

That technology is LiDAR.