To ensure that the data collected by a LiDAR UAV is properly archived and stored for future use or reference purposes, it is important to use a joint platform of unmanned aerial vehicles and apply hybrid georeferencing. Downloading the sample dataset from the ArcUser website and unzipping it on a local computer is the first step. The file includes a pre-designed ArcGIS Pro project, two LIDAR surfaces, and four georeferenced drone images. It is essential to follow the specified naming conventions.
Nowadays, there are several LiDAR models available on the market for use in a UAV system that have an adequate load capacity in terms of payload and dimensions. Studies have compared the potentials of unmanned aerial vehicles with the capabilities of Landsat 7 and 8 satellite observations, providing evidence in a case study on the potential of unmanned aerial vehicles in monitoring ponds near Pardubice (Czechia) using visible light sensors. Sankey T, McVay J, Swetnam TL, McClaran MP, Heilman P, Nichols M (201): hyperspectral and LiDAR data from unmanned aerial vehicles and their fusion for monitoring arid and semiarid terrestrial vegetation. Sankey T, Donager J, McVay J, Sankey JB (201) UAV LiDAR and hyperspectral fusion for forest monitoring in the southwestern United States.
Point clouds are the “bread and butter” of terrestrial LiDAR studies (TLS) and, while the results of UAV photogrammetry are not the same, at least on paper (Wilkinson et al. This sensor differs from those that belong to the first category of this conceptual project, since it is comparable to a large scale aerial LiDAR sensor and is mounted on an aerial vehicle with a pilot, but it is compact and suitable for use with unmanned aerial vehicles. It is quite difficult to define a summary table on the classification of UAVs because every modern UAV is full of technology and it is difficult to compare different systems with each other. The test is based on the estimation of the UAV compass as an angle between the two measured prisms and its comparison with the data recorded from the UAV.
It explains the best use cases, how data accuracy is guaranteed when capturing a drone-based LiDAR, why LiDAR and high-resolution images are complementary technologies and new market opportunities for bathymetric LiDAR. The hydrological prediction serves to make the decision to send the unmanned aerial vehicle team with the mobile unmanned aerial vehicle laboratory to the field to monitor the dynamics of floods. A solution to this problem is provided by using UAVs as a platform for acquiring optical photogrammetric images or LiDAR data. However, the level of detail that LiDAR already has is increasingly available for UAV photogrammetry and motion-based structure techniques.
In the late 1990s (Miller and Amidi 199), many different LiDAR sensors were developed (first experimental and then for commercial purposes) designed especially for unmanned aerial vehicle applications. While it admits that LiDAR competes with traditional stereophotogrammetry, since it was used to generate cutting lines (creating 3D polylines to represent elements such as curbs, retaining walls and bridges), NV5 has just started to dedicate itself to everything related to 3D LiDAR since being able to see under vegetation and generate so many points on the ground has been an enormous advantage for them, says Fraser. To ensure that data collected by a LiDAR UAV is properly archived and stored for future use or reference purposes, it is important to use a joint platform of unmanned aerial vehicles with hybrid georeferencing. This will guarantee accuracy when capturing drone-based LiDAR data.
Additionally, it is essential to follow naming conventions specified in order to make sure that all data collected can be easily accessed in the future. By doing this, users can take advantage of all new market opportunities for bathymetric LiDAR. It is also important to consider using cloud storage solutions as an additional way of archiving data collected by a LiDAR UAV.