Assessing the Ability of Image Based Point Clouds Captured from a UAV to Measure the Terrain in the Presence of Canopy Cover

Abstract

Point clouds captured from Unmanned Aerial Systems are increasingly relied upon to provide information describing the structure of forests. The quality of the information derived from these point clouds is dependent on a range of variables, including the type and structure of the forest, weather conditions and flying parameters. A key requirement to achieve accurate estimates of height based metrics describing forest structure is a source of ground information. This study explores the availability and reliability of ground surface points available within point clouds captured in six forests of different structure (canopy cover and height), using three image capture and processing strategies, consisting of nadir, oblique and composite nadir/oblique image networks. The ground information was extracted through manual segmentation of the point clouds as well as through the use of two commonly used ground filters, LAStools lasground and the Cloth Simulation Filter. The outcomes of these strategies were assessed against ground control captured with a Total Station. Results indicate that a small increase in the number of ground points captured (between 0 and 5% of a 10 m radius plot) can be achieved through the use of a composite image network. In the case of manually identified ground points, this reduced the root mean square error (RMSE) error of the terrain model by between 1 and 11 cm, with greater reductions seen in plots with high canopy cover. The ground filters trialled were not able to exploit the extra information in the point clouds and inconsistent results in terrain RMSE were obtained across the various plots and imaging network configurations. The use of a composite network also provided greater penetration into the canopy, which is likely to improve the representation of mid-canopy elements.

Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

“…The PTIN was also analyzed by Wallace et al [68] using DAP-based data, where the calibration was performed for different ecosystems. However, in contrast to the study of Wallace et al [68], our work did not distinguish the areas regarding the terrain conditions or forest covers due to a lack of data for this discretization, especially in the forest modeling assessment. Instead, the filters were analyzed considering all ALS data available so the results of the calibration can be applied to a wider range of forest conditions.…”

Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland

“…The resulting DTM was then used to calculate the height relative to the ground for all points by subtracting the respective TIN values from the elevation of each point. The use of two subsequent filters determined spurious points below the average ground elevation, which are commonly associated with point clouds derived using SfM [60], being removed from further analysis and variable computation.…”

Evaluating the Capability of Unmanned Aerial System (UAS) Imagery to Detect and Measure the Effects of Edge Influence on Forest Canopy Cover in New England

“…They attributed their result to unreconstructed smaller gaps during SfM-MVS processing. Other studies investigating the ability of photogrammetric point clouds to detect openings have reported similar results [47][48][49]91]. The SfM-MVS method relies heavily on not only the ability to detect features in an image, but ability to match those features across a large number of images.…”