Automatic Separation of Photosynthetic Components in a LiDAR Point Cloud Data Collected from a Canadian Boreal Forest

Abstract

Terrestrial LiDAR has emerged as a promising technology for accurate forest assessment. LiDAR can provide a 3D image composed of a cloud of points using a rotary laser scanner. The point cloud data (PCD) contain information on the (x, y, z) coordinates of every single scanned point and a raw intensity parameter. This study introduces an algorithm for the automatic and accurate separation of the photosynthetic features of a PCD. It is shown that the recorded raw intensity is not a suitable parameter for the separation of photosynthetic features. Instead, for the first time, the absorption intensity is developed for every point based on its raw intensity and distance from the scanner, using proper scaling functions. Then, the absorption intensity is utilized as the only criterion for the classification of the points between photosynthetic and non-photosynthetic features. The proposed method is applied to the scans from a Canadian Boreal Forest and successfully extracted the photosynthetic features with minimal average type I and type II error rates of 5.7% and 4.8%. The extracted photosynthetic PCD can be readily used for calculating important forest parameters such as the leaf area index (LAI) and the green biomass. In addition, it can be used for estimating forest carbon storage and monitoring temporal changes in vegetation structure and ecosystem health.