Assessing the stability of canopy maps produced from UAV-LiDAR data

The use of Unmanned Aerial Vehicles (UAVs) as a remote sensing platform offers a unique combination of high resolution data collected within relatively low cost targeted missions. This paper investigates the use of UAV-borne Light Detecting and Ranging (LIDAR) systems (UAVL) as a platform to gain knowledge of the canopy structure within forested environments. Repeat datasets were collected with a UAVL system over six Eucalyptus Globulus plots with varying levels of canopy cover. The relative stability of four metrics for estimating canopy structure, First Cover Index (FCI), Last Cover Index (LCI), a Grid based method (GCI) and an Alpha shape based method (ACI) were assessed using these repeat datasets. It is shown that the repeatability of the GCI metric is subject to variations in plot level point density (standard deviation of 4.06 %). Instabilities in the FCI (1.91 %) and LCI (2.28 %) metrics were found to be related to the properties of the sensor and the lasers interaction with the canopy. The ACI metric (1.86 %) was found to be the most stable.