Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise.

Identifying and mitigating sources of measurement error is a critical task in geomatics research and the geospatial industry as a whole. In pursuit of such error, accuracy assessments of lidar data have revealed a range bias in low-cost scanners. This phenomenon is a temporally correlated instability in the lidar scanner where the measured distance between target and sensor changes over time while both are held stationary. This research presents an assessment of two low-cost lidar scanners, the Velodyne^® HDL–32E and Livox^® Mid–40, in which their temporal stability is analyzed and methods to mitigate systematic error are implemented. By immobilizing each scanner as it observes a stationary target surface over the course of multiple hours, trends in scanner precision are identified. Scanner accuracy is then determined using a terrestrial lidar scanner, the Riegl^® VZ-400, to observe both subject scanner and target, and extracting the distances between scanner origin and observed surface. Patterns identified in each scanner’s distance measurements indicate temporal autocorrelation, and, by exploiting the high linear correlation between scanner internal temperature and measured distance in the HDL–32E, it is possible to mitigate the resulting error. Application of the proposed solution lowers the Velodyne^® scanner’s measurement RMSE by over 60%, providing levels of measurement accuracy comparable to more expensive lidar systems.

Many lightweight lidar sensors employed for UAS lidar mapping feature a fan-style laser emitter-detector configuration which results in a non-uniform pattern of laser pulse returns. As the role of UAS lidar mapping grows in both research and industry, it is imperative to understand the behavior of the fan-style lidar sensor to ensure proper mission planning. This study introduces sensor modeling software for scanning simulation and analytical equations developed in-house to characterize the non-uniform return density (i.e., scan pattern) of the fan-style sensor, with special focus given to a popular fan-style sensor, the Velodyne VLP-16 laser scanner. The results indicate that, despite the high pulse frequency of modern scanners, areas of poor laser pulse coverage are often present along the scanning path under typical mission parameters. These areas of poor coverage appear in a variety of shapes and sizes which do not necessarily correspond to the forward speed of the scanner or the height of the scanner above the ground, highlighting the importance of scan simulation for proper mission planning when using a fan-style sensor.

Lidar from small unoccupied aerial systems (UAS) is a viable method for collecting geospatial data associated with a wide variety of applications. Point clouds from UAS lidar require a means for accuracy assessment, calibration, and adjustment. In order to carry out these procedures, specific locations within the point cloud must be precisely found. To do this, artificial targets may be used for rural settings, or anywhere there is a lack of identifiable and measurable features in the scene. This paper presents the design of lidar targets for precise location based on geometric structure. The targets and associated mensuration algorithm were tested in two scenarios to investigate their performance under different point densities, and different levels of algorithmic rigor. The results show that the targets can be accurately located within point clouds from typical scanning parameters to <2 cm $\sigma ,$ and that including observation weights in the algorithm based on propagated point position uncertainty leads to more accurate results.

Equipping unmanned aerial systems (UASs) with light detection and ranging (lidar) has been made possible with recent advancements, which has made these sensors compact and gradually more cost-effective. Despite the increased proliferation of UAS-lidar in several fields, the geometric accuracy of lidar-generated point clouds, together with their visual qualities, needs to be explored for building surveying applications. Considering that red−blue−green (RGB) cameras are the most prevalent UAS sensors in building surveying, a lidar- and an RGB camera-equipped UAS was deployed on a mixed infrastructure to simultaneously collect data and generate corresponding point clouds. Different geometric features from both RGB and lidar point clouds were measured and compared quantitatively against benchmark field observations. A qualitative analysis on the point clouds’ visual qualities was also performed and a sensor recommendation matrix was proposed based on desired application accuracy. Lidar has proven to be a viable alternative, providing better geometric accuracy, data quality, and clarity in all three dimensions.