Abstract
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.
Highlights
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
In the digital single-lens reflex (DSLR) dense matching point cloud manual tree detection resulted in 14 missed trees (0.63%), or false negatives, and the wide-angle point cloud yielded missed trees (1.36%) (Table 2)
We evaluated the method of [31] as available in the rLiDAR package with canopy height model (CHM) derived from a low-altitude lidar point cloud, as well as dense matching point clouds derived from both DSLR and wideangle digital cameras
Summary
The “standard” for forest mensuration, the timber cruise, requires selective sampling by a forester in the field to measure and estimate forest parameters such as tree count, diameter at breast height (DBH), tree height, stem form, and other physical traits [12] It follows that the latest trend in forest inventory and mensuration is the use of automated systems, both for the collection and the processing of data [6,13,14,15,16,17]. The present work uses both photogrammetric and lidar data, the former of metry, with the objective of identifying which datasets are of suitable quality for use in which is collected using two camera types commonly used for UAS photogrammetry, with automated ITD. Each dataset the best chance of success for each subplot conditions, providing a fair assessment of data quality among the datasets
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