A Study on Factors Affecting Airborne LiDAR Penetration

Abstract

This study uses data from different periods, areas and parameters of airborne LiDAR (light detection and ranging) surveys to understand the factors that influence airborne LiDAR penetration rate. A discussion is presented on the relationships between these factors and LiDAR penetration rate. The results show that the flight height above ground level (AGL) does not have any relationship with the penetration rate. There are some factors that should have larger influence. For example, the laser is affected by a wet ground surface by reducing the number of return echoes. The field of view (FOV) has a slightly negative correlation with the penetration rate, which indicates that the laser incidence angle close to zero should achieve the best penetration. The vegetation cover rate also shows a negative correlation with the penetration rate, thus bare ground and reduced vegetation in the aftermath of a typhoon also cause high penetration rate. More return echoes could be extracted from the full-waveform system, thereby effectively improving the penetration rate. This study shows that full-waveform LiDAR is an effective method for increasing the number of surface reflected echoes. This study suggests avoiding LiDAR survey employment directly following precipitation to prevent laser echo reduction.

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

“…Even though a direct comparison of regression analysis results derived from other studies requires particular caution, it is worth noting that more variation in the load of different surface fuel layers was explained in this work compared to most of the other reported prediction accuracies described in Section 1. Possible reasons may encompass the use of higher-density data (e.g., Reference [47,50]), as well as the different forest composition and topography characterizing other examined areas, which affect the canopy penetration capabilities of the laser [57]. The use of pulse intensity-related variables also had a substantial influence on the results' accuracy since the point cloud structural information (i.e., height metrics) alone is not adequate for reliable load estimation of the different SFTs [47,100].…”

“…They present limitations, such as attenuation by dense foliage and possible point positioning errors, which can hinder near-surface load assessment [53][54][55][56]. Moreover, different sensor characteristics, forest composition, and topography conditions can lead to variations in the laser penetration depth and, hence, the results in terms of the most suitable SFL prediction method and derived accuracy [24,34,57]. The use of full-waveform or discrete return LiDAR data can also define the most appropriate analysis approach to be adopted and its predictive performance.…”

A new point cloud processing method unveiled hidden coastal boulders from deep vegetation

“…Effect of vegetation can be represented by the shielding ratio. For example, it has been reported that the higher the shielding ratio by vegetation, the smaller the transmission rate of radar emitted from LiDAR: the transmission rate becomes around 20% when the shielding rate is above 70% 39 . Specifically for the LiDAR equipments that we used in this study, the effect of shielding by vegetation has also been preliminary evaluated: the ground surface of nearby mangrove forests cannot be adequately captured when shielding ratio exceeds about 85% (K. Kasai, personal communication).…”

Range Information Characterization of the Hokuyo UST-20LX LIDAR Sensor

“…There are many commercial-off-the-shelf (COTS) LiDAR products for many different applications ranging from hand-held 3D mapping sensors to large 3D sensors for autonomous driving cars to even more sophisticated space-based LiDAR sensors used to monitor the health of crops across nations [2]. The LiDAR sensor investigated in this paper was the Hokuyo UST-20LX Scanning Laser Range Finder (Hokuyo, Osaka, Japan) [3] as seen in Figure 1.…”