In the world of nature-based carbon removal, accuracy is everything. To attract investment, pass audits, and command premium prices for carbon credits, project developers need to provide transparent, defensible proof of their impact. At Treeconomy, we are constantly refining our methods with cutting-edge technology to deliver precisely that - the data-driven confidence needed to succeed. 

One of our key measurement tools is LiDAR, which stands for Light Detection and Ranging. Think of it as creating a 3D scan of a forest. It’s a remote sensing technology that pulses lasers at the landscape and records how long they take to bounce back. That information is used to construct a ‘point cloud’—a series of points depicting the locations the laser reflected off the landscape, recreating its 3-D structure. For our work with forestry projects, these point clouds are invaluable, as they can reveal detailed information about the size and health of vegetated environments.

At Treeconomy, we've long understood the importance of high quality LiDAR data. Specifically, we have found that using multi-return LiDAR provides a dramatic improvement in data quality compared to older, single-return systems. 

Many basic instruments only record the first returned laser pulse from an object. In a typical single-return point cloud, this means the top canopy level will be drastically over-represented compared to components closer to the ground, such as the stem. More technical instruments, however, can “see” deeper into the vertical forest structure, particularly in the later returns. This is where the most important data for our modelling lies, making multi-return LiDAR essential for the accuracy that investors and registries demand.

In practical terms, this creates a more complete picture of the vegetation structure, and thus, when we show this data to our models, it makes it that much more easier for them to output a quality result, as well as measure important variables such as Diameter at Breast Height (Dbh), stem volume, and canopy area or volume.

We can flatten this 3-D segmentation output into vectorised files that identify the location and crown shape of all trees in your project. This is the tangible data that strengthens investor reports, guides on-the-ground operational planning, and provides transparent proof of impact for audits. It gives projects an extra “wow” factor, as your stakeholders can link the maps to trees on the ground and see exactly where your project removes carbon. 

Consider the progression of quality across the point cloud tiles below. In the first image, the raw input data is shown, then an image showing only higher return levels used to segment the trees, and then a final image with the complete multi-return data. In the final example we captured a lot of detail of the stem, which noticeably improves the model’s ability to distinguish one tree unit from another. The level of detail achieved was very impressive in some trial runs and we will be incorporating multi-return LiDAR in our upcoming batch of baselining projects.

Another exciting aspect of these tools is the ability to segment not only the tree, but components of the tree itself. We found that we could get detailed enough point clouds of stems to segment and measure stem diameter (Dbh) from the point cloud alone, an activity typically reserved for field studies using a physical tape measure.

This is a powerful capability. It dramatically reduces the time and cost of traditional forest inventories, replacing hours of fieldwork with a single surveyor using multi-return LiDAR (MRL). That benefit goes beyond simple cost savings: moving faster from data collection to analysis means you can move faster from project feasibility to financing. 

In the example below, we obtained a very high quality point cloud of a complex, mature beech tree (Fagus sylvatica) scanned with terrestrial LiDAR in Wytham Woods, Oxfordshire, UK. We attempted to separate the leaves and twigs from the main stem and larger branches with an impressive level of success. While we could not completely capture some of the upper stems (see the second image), by collecting a higher density point cloud, that gap could be closed.

To test the ability to measure Dbh from a tree point cloud, we obtained several segmented pine tree point clouds from southern Finland, and used a tool (shown below) that finds the points at Dbh level (1.3 m above the ground level), and calculates diameter. This is an extremely powerful capability for saving time in obtaining plot samples, wherein crews of people and hours of time can be replaced with just a single person and  to collect the data instead, and process it quickly. 

The move from single to multi-return LiDAR is already overcoming key operational hurdles in forest monitoring. Where we were previously restricted to drone surveys during leaf-on months with single-return LIDAR, multiple return LIDAR has opened the possibility of surveys in any season. It also makes the detection of mature, dense broadleaf trees far more accurate, something that was a significant challenge in the recent past. 

Our vision is to continue integrating the latest technologies into our suite of tools. By constantly pushing boundaries of what’s possible in digital measurement and reporting, we provide our customers with the high quality, accurate information needed to tell the story of their project’s impact and build the trust required to scale their work.

Whether you need a carbon baseline estimate or ongoing monitoring, our cutting-edge tools deliver the accurate, defensible data required for project validation and credit issuance. In a market where credibility is currency, this level of precision helps you build trust, command premium prices, and secure long-term offtake agreements..