An Unsupervised Canopy-to-Root Pathing (UCRP) Tree Segmentation Algorithm for Automatic Forest Mapping

Abstract

Terrestrial laser scanners, unmanned aerial LiDAR, and unmanned aerial photogrammetry are increasingly becoming the go-to methods for forest analysis and mapping. The three-dimensionality of the point clouds generated by these technologies is ideal for capturing the structural features of trees such as trunk diameter, canopy volume, and biomass. A prerequisite for extracting these features from point clouds is tree segmentation. This paper introduces an unsupervised method for segmenting individual trees from point clouds. Our novel, canopy-to-root, least-cost routing method segments trees in a single routine, accomplishing stem location and tree segmentation simultaneously without needing prior knowledge of tree stem locations. Testing on benchmark terrestrial-laser-scanned datasets shows that we achieve state-of-the-art performances in individual tree segmentation and stem-mapping accuracy on boreal and temperate hardwood forests regardless of forest complexity. To support mapping at scale, we test on unmanned aerial photogrammetric and LiDAR point clouds and achieve similar results. The proposed algorithm’s independence from a specific data modality, along with its robust performance in simple and complex forest environments and accurate segmentation results, make it a promising step towards achieving reliable stem-mapping capabilities and, ultimately, towards building automatic forest inventory procedures.

Vegetation Cover Type Classification Using Cartographic Data for Prediction of Wildfire Behaviour

“…Although traditional methods can yield precise measurements and estimates of cover and biomass, they require intensive and challenging field work to collect data in a highly localised manner (not easily scalable) [24,25]. Conversely, single-tree biomass may be estimated from effective crown data obtained via laser scans [26], while three-dimensional point clouds generated by (terrestrial or aerial) laser scans are ideal for capturing the structural features of trees via tree segmentation [27].…”

“…Their results suggest that feature selection significantly affects model accuracy and that a combination of Recursive Feature Elimination (RFE) and CatB considerably outperforms other models. Recently, Carpenter et al [27] proposed an unsupervised canopy-to-root pathing tree segmentation algorithm as a prerequisite for automatic forest mapping.…”