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.

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Light Detection and Ranging, as an active Remote Sensing Technology, enables gathering accurate, three-dimensional point cloud of scanned objects. Laser scanning might be provided on the terrestrial level for specific, defined constructions, as well as on the airborne level for aerial or linear objects. Using a laser sensor mounted on a moving platform is currently the most efficient way of obtaining in a short period, accurate positions of billions of points as a representation of a scanned area. Based on this kind of dataset it is possible to perform three-dimensional analysis of the safety of scanned objects without additional measurements in the field. This article presents the analysis performed in vMatic software on data from Airborne Laser Scanning for medium voltage power line verification of obstacles with the buildings. The analysis took less than 20 seconds for the detection of buildings points that are closer than 5m from conductors for seven spans wit a total length of almost 400m. Providing distance verification on 3D point cloud data is the fastest way to obtain a hazard awareness in a short time. Once acquired by LiDAR data can be used for other various analyses for any construction, depending on current, expected and future needs.