Automated Individual-Tree Species Recognition Using Mobile Laser Scanner Point Clouds, Random Forest and 2D Convolutional Neural Networks

Acquiring information about tree species has special relevance for the management and preservation of forests. Remote sensing has been widely used for this task, especially at landscape scales. However, less research has been done to acquire this information at the single-tree level. In recent decades, mobile laser scanner (MLS) platforms have progressively attracted interest in the forestry sector in regard to obtaining single-tree information. In this study, we examine the feasibility of differentiating between seven different tree species (Castanea sativa, Eucalyptus globulus, Eucalyptus nitens, Pinus pinaster, Pinus radiata, Quercus robur and Chamaecyparis lawsoniana) using MLS point clouds. The classifications were performed using a machine learning algorithm, random forest (RF), and a deep learning algorithm, namely, a 2D Convolutional Neural Network (CNN). The RF algorithm was used to identify tree species at the pixel level using raster layers of statistics that reflect the vertical distribution of the points within single-tree point clouds. The 2D CNN algorithm constructed multi-view 2D profiles of single-tree point clouds by rotating the point clouds around a single axis. Different 2D image sizes were tested. The 2D CNN algorithm outperformed the RF algorithm and yielded an Overall Accuracy of 93%. We also found that image size affected both the accuracy metrics obtained and the amount of training time needed. Of the tree species studied, Pinus radiata and Quercus robur had the lowest classification accuracies (with F-Scores of 83% and 87%, respectively). According to these results, MLS point clouds can be efficiently used in the forest sector to perform individual-tree species recognition through fully automated procedures.