A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers

Remote sensing techniques, developed over the past four decades, have enabled large-scale forest inventory. Light Detection and Ranging (LiDAR), as an active remote sensing technology, allows for the acquisition of three-dimensional point clouds of scanned areas, as well as a range of features allowing for increased performance of object extraction and classification approaches. As many publications have shown, multiple LiDAR-derived metrics, with the assistance of classification algorithms, contribute to the high accuracy of tree species discrimination based on data obtained by laser scanning. The aim of this article is to review studies in the species classification literature which used data collected by Airborne Laser Scanning. We analyzed these studies to figure out the most efficient group of LiDAR-derived features in species discrimination. We also identified the most powerful classification algorithm, which maximizes the advantages of the derived metrics to increase species discrimination performance. We conclude that features extracted from full-waveform data lead to the highest overall accuracy. Radiometric features with height information are also promising, generating high species classification accuracies. Using random forest and support vector machine as classifiers gave the best species discrimination results in the reviewed publications.

Automated summary

Remote sensing techniques, especially Light Detection and Ranging (LiDAR), have greatly improved large-scale forest inventory by providing three-dimensional point cloud data for object extraction and classification. Various LiDAR-derived metrics, combined with classification algorithms, contribute to high accuracy in tree species discrimination. Full-waveform data extraction and the use of random forest or support vector machine classifiers have shown to be most effective in increasing species discrimination performance.