A “global LiDAR scan” sounds straightforward - fly sensors over the Earth and stitch the data together - but aircraft and drones run into hard constraints of cost, logistics, and regulatory scalability. These constraints become overwhelming once you move from “national program” scale to “planetary, repeatable, and decision-grade” scale.
Even if we focus only on forests (rather than all land), the required coverage is enormous. FAO’s Global Forest Resources Assessment reports a total forest area of ~4.06 billion hectares (≈ 40.6 million km²).
If your goal is annual (or even multi-year) repeat coverage, the question becomes: can aircraft or drones economically re-fly ~40.6 million km² with the point density and QA needed for carbon accounting? In practice, this is where feasibility breaks.
Source: https://lidarmag.com/2024/12/30/airborne-lidar-a-tutorial-for-2025/
The Hancock et al. (2021) feasibility study is a good “anchor” reference because it explicitly compares spaceborne approaches to airborne alternatives at global scale. They estimate that producing a 5m global map within 5 years using airborne laser scanning (ALS) would cost hundreds of billions of dollars (they report ~$318B for that scenario), and note that this already exceeds their rough estimate of the cost of a “global ALS survey.”
That matters because forest carbon monitoring needs repeatability, not a one-off map. And 1m-class structural mapping is more demanding than 5m, increasing flight lines, data rates, processing, and QA overhead.
At global scale, aircraft LiDAR becomes a project of continuous aviation logistics:
Hancock et al explicitly treat the airborne global survey as cost-prohibitive relative to satellites at global repeat scale (that’s a key premise of why the paper studies spaceborne LiDAR architectures).
Even if one heroic global airborne campaign were funded, carbon accounting requires re-measurement (annual or near-annual in many use cases). Re-flying the world’s forests repeatedly becomes an ongoing “aviation megaprogram,” with cost that scales roughly linearly with frequency.
Bottom line for aircraft: ALS is excellent for regional/national mapping, but as you push to global + repeatable + decision-grade, cost and logistics explode into an unmanageable regime, consistent with the Hancock et al global cost estimates.
Source: https://bavovna.ai/lidar-uav/
Drones are often assumed to be a cheaper way to scale mapping. In practice, they are best suited to local areas, and become infeasible when you ask them to behave like a planetary observation system.
A core limitation is that UAVs generally lack the range and endurance needed for large-area forest remote sensing campaigns. This is explicitly stated in the forestry remote sensing literature (UAVs are cost/flexibility winners for small areas, but “lack range and endurance” for larger ones).
More broadly, UAV reviews repeatedly identify limited endurance as a primary constraint, especially for battery-powered platforms (endurance is power-supply-limited).
You can partially mitigate this with fixed-wing UAVs, refuel/relays, or solar concepts, but the operational complexity rises sharply. And you still face airspace and safety limitations at scale.
Even when UAV LiDAR is technically excellent, the area per mission remains small compared to manned aircraft. Conference and technical literature on UAV-borne LiDAR vs airborne (manned) LiDAR notes that manned aircraft missions cover vastly larger areas than UAVs.
To put “hundreds of hectares” in context: forests are billions of hectares.
Bridging that gap with drones implies an industrial-scale fleet, global basing, maintenance, staffing, and continuous operations - i.e., you re-create an airline, but with far more operational touch labour per km².
At large-area mapping scale, drones must operate BVLOS (Beyond Visual Line of Sight). In Australia, CASA makes clear BVLOS requires specific approvals and a formal process (application, documentation, fees), and broad-area BVLOS has explicit prerequisites (operator certification, pilot licensing/exams) and can take months.
Similarly, in the US, the FAA’s Part 107 framework requires waivers to deviate from certain operational limitations—indicating that scaling operations beyond standard constraints requires case-by-case regulatory approval and safety justification.
This regulatory friction is manageable for targeted projects, but becomes a major barrier for a “global continuous LiDAR survey” concept.
Bottom line for drones: UAV LiDAR is powerful for local, flexible, high-detail acquisitions, but it is structurally mismatched to global, repeatable wall-to-wall measurement due to endurance/coverage limits and BVLOS regulatory scaling constraints.
Aircraft and drones can produce superb LiDAR data, and in many cases they can outperform satellites in raw per-pass detail. The problem is that global forest carbon monitoring is a scale-and-repeatability requirement: billions of hectares, regularly revisited, with consistent QA.
The literature-based global cost comparisons (Hancock et al.) and the practical limits of UAV endurance + BVLOS operations together imply that the only architecture that plausibly closes the loop on global, repeatable coverage is spaceborne LiDAR.