Comparison of a commercial and home-assembled fixed-wing UAV for terrain mapping of a post-mining site under leaf-off conditions

Unmanned aerial vehicle (UAV) platforms are rapidly becoming popular in many research and industry sectors. Due to their relatively low purchase price and the fact they can be used to monitor areas that are difficult or even unsafe to access, they have been increasingly used in land surveying and mapping of smaller areas. Numerous UAV platforms equipped with various cameras are increasingly available on the market, differing in their suitability for environmental mapping. Surveyors therefore face a question whether to buy or assemble their own UAV. The objective of this study is to assess the performance of two fixed-wing UAV systems for land survey and mapping applications. In particular, we: (1) compared a commercial eBee platform equipped with a Sony Cybershot DSC-WX220 camera with zoom lens and a home assembled EasyStar II equipped with Nikon Coolpix A with a lens of fixed focal length to find out if a home-assembled solution can compete with specialized commercial platform; (2) investigated the utilization of UAV images acquired under leaf-off conditions for digital terrain model (DTM) generation with respect to vegetation cover (steppes and forests); (3) assessed whether an increase in the image quantity can compensate for a lower quality of images; and (4) compared the DTM derived from UAV imagery with the official Czech Republic airborne laser scanning (ALS)-derived DTM. One flight with Easystar II and two perpendicular flights with eBee were performed. From these three flights, four point clouds were derived (one from each flight, and one resulting from a combination of two eBee flights), supplemented with four ground filtered point clouds. The accuracy of point clouds and DTM was assessed through a comparison with a conventional GNSS survey. We successfully identified the bare ground during the leaf-off period in the deciduous forest using images from both platforms. Point densities of point clouds acquired with Easystar II exceeded the densities of those acquired with eBee even after combining images from two eBee flights. Root mean square error of all derived point clouds ranged between 0.11 and 0.19 m, exceeding the accuracy of a nationwide ALS-derived DTM in both forest and open steppe areas. The most accurate point cloud was acquired using Easystar II. This is likely due to a combined effect of the quality of onboard cameras, camera settings and environmental conditions during the flight. For users who prefer to have greater control over their options rather than being dependent on the commercially available kit solution, home-assembled kits utilizing drones capable of carrying any camera available on the market may be an advantage.