Quality Assessment of Terrestrial Laser Scanner Ecosystem Observations Using Pulse Trajectories

Abstract

Considering the trajectories of pulses from terrestrial laser scanners (TLS) can provide refined models of occlusion and improve the assessment of observation quality in forests and other ecosystems. By considering the space traversed by light detection and ranging (lidar) pulses, we can separate empty regions of an ecosystem sample from unobserved regions of an ecosystem sample. We apply this method of TLS observation quality assessment, and analyze Compact Biomass Lidar 2 (CBL2) TLS observations of a single tree and of a deciduous forest stand. We show the contribution of information from each TLS scan to be inconsistent and the combination of multiple scans to have diminishing returns for new information, without guaranteeing complete coverage of a sample. We quantitatively investigate the effects of imposing information quality requirements on TLS sampling, for example, requiring minimum numbers of observations in each region or requiring regions to be observed from a minimum number of independent scans. We show empirically that rigid, predefined TLS sampling schemes, even with hypothetically dense coverage, cannot guarantee successful samples in geometrically complex systems such as forests. Through these methods, we lay the groundwork for on-the-fly assessment of observation quality according to several modeling-relevant metrics which enhance TLS ecosystem assessment. We also establish the value of flexible deployment options for TLS instruments, including the ability to deploy at a variety of heights.

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

“…To overcome these challenges, Vicari, Disney, et al () suggested using high TLS resolution levels and optimal field scanning protocols, and co‐registration methodologies such as used by Wilkes et al (). Another strategy is to apply on‐the‐fly adjustment (Paynter et al, ) for TLS data quality control.…”

“…Another strategy is to apply on-the-fly adjustment (Paynter et al, 2018) for TLS data quality control.…”

Characterizing a New England Saltmarsh with NASA G-LiHT Airborne Lidar

“…Additionally, since water strongly absorbs the 1550 nm wavelength of the G-LiHT lidar, it is difficult to ascertain whether areas without observations were occluded from view, or were reached by pulses, but reflected insufficient energy to declare a return. With this potentially confounding factor in mind, it seems prudent to implement an observation density assessment that infers the trajectories of pulses [123] before increasing the resolution of the data products. Such an approach mitigates the uncertainty in ecosystems with a high Analyzing the density of the airborne lidar observations implies that higher resolution data products could be produced from future G-LiHT acquisitions, and possibly even derived from the existing datasets ( Figure 19).…”

Vegetation Horizontal Occlusion Index (VHOI) from TLS and UAV Image to Better Measure Mangrove LAI

“…However, it cannot be completely eliminated. No matter how TLS station is set up in the forest, some regions of occlusion still exists, especially in the dense and complex forest [26]. The occlusion problem is even more severe in mangrove forests.…”

“…Several previous studies have shown that regions of occlusion can be evaluated by computing the complete trajectories of laser pulses [25,26,34,35]. The occlusion effect is represented by the extent of the occluded regions, which is the accumulated volume of voxel with no record of any interactions with pulse and voxel.…”