However, UAV–LiDAR sensors are undergoing rapid developments, and the characteristics are expected to keep changing over the years, which will introduce data inter-operability issues. Therefore, it is important to determine whether datasets acquired by different UAV–LiDAR sensors can be interchanged and if changes through time can accurately be derived from UAV–LiDAR time series. With this study,the Laboratory of Geo-Information Science and Remote Sensing of Wageningen University presents insights into the magnitude of differences in derived forest metrics in savanna woodland when three different UAV–LiDAR systems are being used for data acquisition.

UAV–LiDAR point clouds were acquired with three different UAV-systems, among them was the RiCOPTER with fully integrated RIEGL VUX-SYS, which consisted of a RIEGL VUX-1UAV laser scanner combined with an Applanix APX-20 inertial navigation system (INS), weighing about 7 kg including the control box and wiring.

Their findings show that all three systems can be used to derive plot characteristics such as canopy height, canopy cover, and gap fractions. However, there are clear differences between the metrics derived with different sensors, which are most apparent in the lower parts of the canopy. On an individual tree level, all UAV–LiDAR systems are able to accurately capture the tree height in a savanna woodland system, but significant differences occur when crown parameters are measured with different systems. Less precise systems result in underestimations of crown areas and crown volumes.

When comparing UAV–LiDAR data of forest areas through time, it is important to be aware of these differences and ensure that data inter-operability issues do not influence the change analysis. In this paper, they wanted to stress that it is of utmost importance to realise this and take it into consideration when combining datasets obtained with different sensors.

The full article was published in the Remote Sensing Special Issue Innovative Belgian Earth Observation Research for the Environment (2022, 14(23), 5992), publishing house: MDPI, and can be found here.