Low-cost drones offer breakthrough for community-based forest carbon monitoring

A peer-reviewed study has validated the use of low-cost consumer drones and open-source software for accurately measuring aboveground carbon density (ACD) in tropical forest restoration areas, offering a scalable solution for community-based conservation. The research, titled “Simplifying Drone-Based Aboveground Carbon Density Measurements to Support Community Forestry,” was published in PLOS One. It presents a simplified workflow that compares drone-derived measurements with traditional field-based methods, emphasizing the importance of regionally calibrated models and addressing practical barriers to community use.

Conducted at a 2-hectare restoration site in Sabah, Malaysia, the study demonstrates that canopy height data captured via drones and processed using structure-from-motion (SfM) photogrammetry can produce carbon density estimates comparable to those derived from manual surveys. Despite some uncertainties, especially in complex topographies, the results affirm the potential for this method to support climate finance, carbon offsetting, and sustainable forest management, particularly in regions where communities lack access to high-end technology or remote sensing expertise.

Can drones replace traditional carbon measurement methods?

The study set out to determine whether consumer-grade drones could offer a practical and accurate alternative to labor-intensive ground-based surveys. Using a DJI Phantom 4 Pro V2.0, researchers captured hundreds of aerial images over the restoration site and processed them using OpenDroneMap (ODM) to produce 3D canopy height models (CHMs). These were then compared with measurements from a 50×50-meter botanical plot where trees had been manually measured.

Results showed that drone-based methods systematically produced higher ACD estimates than field methods, but the magnitude of discrepancy varied significantly depending on which allometric equations were applied. When the team used general global equations not specific to the region, ACD values were up to four times higher than field-derived values. In contrast, regionally calibrated models produced more accurate and comparable results, albeit with broader uncertainty ranges due to drone height measurement errors.

These discrepancies highlight the critical role of model selection in drone-based carbon accounting. Field plots used individual tree measurements, which excluded smaller trees, low vegetation, and overhanging crowns. Drone methods, by contrast, include all visible biomass, which can lead to higher estimates. However, with appropriate calibration, the accuracy gap can be narrowed, making drones a promising tool for rapid, scalable monitoring across tens to hundreds of hectares.

What are the technical and practical limits of this drone-based approach?

While promising, the methodology is not without limitations. One key issue is the error margin associated with drone-derived canopy heights. Even with optimized flight plans and high-resolution imagery, the lack of ground control points (GCPs) led to uncertainty in height measurements, modelled at ±1.5 to 4 meters. These height errors can significantly skew carbon estimates, especially in low-stature forests like those in recently cleared or secondary growth areas.

To address this, the researchers adopted a conservative processing strategy, including generating a flat digital elevation model based on elevation percentiles from open ground, thereby avoiding terrain-based distortions. The authors note that while this technique worked in the relatively flat Kaboi Lake site, it would be unsuitable in areas with significant topographic relief.

Another constraint is data processing. Generating canopy height models from drone imagery requires high-performance computing resources and software expertise. The study team needed multiple attempts and more than three hours of processing time for a single 2-hectare plot. Community groups adopting this method must be trained not only in drone piloting but also in GIS, photogrammetry, and error propagation techniques.

Cost is also a factor. Although drones like the Phantom 4 are affordable relative to airborne LiDAR or satellite tasking, they still require smartphones or tablets for flight control, high-spec computers for image processing, and possibly subscriptions to commercial software for more advanced outputs. While the use of open-source platforms like ODM reduces some of these costs, practical barriers to entry remain for under-resourced communities.

How can this method support community forestry and carbon finance?

The significance of this study extends beyond methodological innovation. By enabling reliable ACD estimates at scales from 1 to 100 hectares, drone-based workflows align well with the needs of community forestry and restoration projects that form the backbone of grassroots climate solutions. Such projects are often overlooked in global datasets due to their limited spatial footprint, yet they are vital for biodiversity, resilience, and livelihoods.

With international climate finance increasingly tied to measurable outcomes, such as verifiable carbon sequestration, communities must provide evidence of their environmental impact. This study offers a credible, accessible method for doing so, provided local stakeholders receive appropriate training and technical support.

Furthermore, the visual data produced by drones can have secondary benefits. Orthomosaic images can validate restoration activities, engage donors, and serve as advocacy tools in land rights disputes. In some regions, these images have even become income-generating products through eco-tourism or promotional merchandise.

The study warns against over-reliance on drone contractors or outsourcing. While third-party services may reduce startup costs, they introduce risks around data ownership, consent, and community empowerment. Instead, the authors advocate for NGO partnerships and capacity-building models that ensure local autonomy over data and decision-making.