3D forest semantic segmentation using multispectral LiDAR and 3D deep learning

Conservation and decision-making regarding forest resources necessitate regular forest inventory. Light detection and ranging (LiDAR) in laser scanning systems has gained significant attention over the past two decades as a remote and non-destructive solution to streamline the labor-intensive and time-consuming procedure of forest inventory. Advanced multispectral (MS) LiDAR systems simultaneously acquire three-dimensional (3D) spatial and spectral information across multiple wavelengths of the electromagnetic spectrum. Consequently, MS-LiDAR technology enables the estimation of both the biochemical and biophysical characteristics of forests. Forest component segmentation is crucial for forest inventory. The synergistic use of spatial and spectral laser information has proven to be beneficial for achieving precise forest semantic segmentation. Thus, this study aims to investigate the potential of MS-LiDAR data, captured by the HeliALS system, providing high-density multispectral point clouds to segment forests into six components: ground, low vegetation, trunks, branches, foliage, and woody debris. Three point-wise 3D deep learning models and one machine learning model, including kernel point convolution, superpoint transformer, point transformer V3, and random forest, are implemented. Our experiments confirm the superior accuracy of the KPConv model. Additionally, various geometric and spectral feature vector scenarios are examined. The highest accuracy is achieved by feeding all three wavelengths (1550 nm, 905 nm, and 532 nm) as the initial features into the deep learning model, resulting in improvements of 33.73% and 32.35% in mean intersection over union (mIoU) and in mean accuracy (mAcc), respectively. This study highlights the excellent potential of multispectral LiDAR for improving the accuracy in fully automated forest component segmentation.