If volume equals value in forestry terms, the lower stem is one of the most important, yet least accessible parts of the tree for airborne remote sensing. Until now, that is.
Flying above the canopy, unmanned aerial vehicle (UAV) scanners cannot reliably map tree stems. On the ground, GPS error prevents conventional laser mapping. SLAM technology (Simultaneous Location and Mapping) is the solution. It can create 3D maps from sensors without GPS. Attached to a UAV, this technology can navigate around a forest unpiloted. All the while, the sensors are collecting a highly detailed point cloud dataset of the forest environment that is immediately available after the scan, avoiding expensive and time-consuming data processing.
Over the past 18 months, Scion has conducted forestry trials with the manufacturers of a range of mobile SLAM scanners, including GreenValley International and GeoSLAM. And in November 2019, Scion and Emesent successfully completed a world-first and trialled Hovermap technology beneath the forest canopy. Results from the trial show that there is tremendous potential for SLAM technology to detect tree stems and take detailed measurements including location, diameter, height, stem volume, branching and stem defects, all of which determine the value of wood in the trees.
Forward thinking forest industry consultants, Interpine, have now taken up the technology and have begun to offer it as a service.
“We look forward to working alongside Scion to provide a pathway to implement SLAM-based LiDAR technology in the forestry sector. Together our teams will continue to extend what can be done with this game changing technology,” says David Herries, Director and General Manager of Interpine.
Potential uses for the accurate data created by this technology stretch far and wide. Precision forest management activities to benefit include tree thinning operations, targeted application of fertilisers and tree harvest and marketing.
The ability to measure exceptional individual trees for inclusion in tree improvement programmes and improving descriptions of tree characteristics will help researchers make better tree breeding selections, resulting in better trees shaping tomorrow’s forests.
Scion also aims to develop use of autonomous flying. This will speed up the data gathering process and make it safer, reducing health and safety risks presented by manual forest measurement, as well as solving issues with skills shortages in the industry and the subjective nature of manual measurements. Areas that have been completely unreachable will also be able to be surveyed.
This safe, efficient and effective way to gather under-canopy data is the last piece in the puzzle of representing a forest digitally and creating a complete picture of the forest from above and below that identifies and characterises individual trees.