The results of lidar measurements on laboratory-scaled cloud models are presented. The lidar system was based on a picosecond laser source and a streak camera. The cloud was simulated by a homogeneous aqueous suspension of calibrated microspheres. Measurements were repeated for different concentrations of diffusers and for different values of the receiver angular field of view. The geometric situation was similar to one of an actual lidar sounding a 300-m-thick cloud at a distance of 1200 or 7800 m. The results show how the effect of multiple scattering depends on the extinction coefficient of the sounded medium and on the geometric parameters. The depolarization introduced by multiple scattering was also investigated. Measurements were carried out in well-controlled conditions. The results can thus be useful to validate the accuracy of numerical or analytical procedures that have been developed to study multiple-scattering contribution in lidar returns.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/AO.35.005435 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
This study proposes what we believe to be a novel high-spectral-resolution three-frequency Rayleigh lidar for simultaneously measuring middle atmosphere temperature and wind. The temperature and wind could be retrieved without assuming an external reference temperature, as typical for a traditional Rayleigh Doppler lidar. Adopting a similar idea used in sodium temperature/wind lidar, this system alternatively emits laser pulses at three frequencies.
View Article and Find Full Text PDFNEON-SD: A 30-m Structural Diversity Product Derived from the NEON Discrete-Return LiDAR Point Cloud.
Sci Data
October 2024
Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, USA.
Structural diversity (SD) characterizes the volume and physical arrangement of biotic components in an ecosystem which control critical ecosystem functions and processes. LiDAR data provides detailed 3-D spatial position information of components and has been widely used to calculate SD. However, the intensive computation of SD metrics from extensive LiDAR datasets is time-consuming and challenging for researchers who lack access to high-performance computing resources.
View Article and Find Full Text PDFPath Tracing-Inspired Modeling of Non-Line-of-Sight SPAD Data.
Sensors (Basel)
October 2024
School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK.
Non-Line of Sight (NLOS) imaging has gained attention for its ability to detect and reconstruct objects beyond the direct line of sight, using scattered light, with applications in surveillance and autonomous navigation. This paper presents a versatile framework for modeling the temporal distribution of photon detections in direct Time of Flight (dToF) Lidar NLOS systems. Our approach accurately accounts for key factors such as material reflectivity, object distance, and occlusion by utilizing a proof-of-principle simulation realized with the Unreal Engine.
View Article and Find Full Text PDFControls on fluvial sediment evacuation following an earthquake-triggered landslide: Observations from LiDAR time series.
Sci Adv
September 2024
GNS Science, Te Pū Ao Avalon, Lower Hutt, New Zealand.
Catastrophic sediment overloading of mountain streams in response to coseismic landsliding causes river systems to fundamentally reorganize their morphology and sediment transporting characteristics, influencing sediment yields, bedrock incision, and the coupling between erosion and tectonics. A sequence of 13 airborne LiDAR surveys of an alpine tributary of the Hāpuku River, New Zealand, reveals patterns of sediment mass balance change over 5 years following delivery of 6.6 million cubic meters of landslide debris during the 2016 magnitude 7.
View Article and Find Full Text PDFImpact of Multi-Scattered LiDAR Returns in Fog.
Sensors (Basel)
August 2024
Institute for Laser Technologies in Medicine and Metrology at the University of Ulm (ILM), D-89081 Ulm, Germany.
In the context of autonomous driving, the augmentation of existing data through simulations provides an elegant solution to the challenge of capturing the full range of adverse weather conditions in training datasets. However, existing physics-based augmentation models typically rely on single scattering approximations to predict light propagation under unfavorable conditions, such as fog. This can prevent the reproduction of important signal characteristics encountered in a real-world environment.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!