The Laser and its Application to Meteorology

Goyer, Guy G.; Watson, R. D.

doi:10.1175/1520-0477-44.9.564

Cited by 75 publications

(23 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LIDAR (Light Detection and Ranging) technology involves illuminating a target with pulsed laser light and measuring reflected pulses to provide information about target range. LIDAR was first applied in meteorology (e.g., Goyer and Watson, 1963), but has found a very wide range of applications since, including recent use in obstacle avoidance and navigation in autonomous vehicles (e.g., Schwarz, 2010). Now that lightweight LIDAR units are available, they provide a simpler, practical solution for instantaneous measurement of altitude in UAVs.…”

Section: Introductionmentioning

confidence: 99%

Inexpensive Aerial Photogrammetry for Studies of Whales and Large Marine Animals

et al. 2017

View full text Add to dashboard Cite

We describe a simple system enabling accurate measurement of swimming marine mammals and other large vertebrates from low-altitude single-frame photogrammetry via inexpensive modifications to a "prosumer" unmanned aerial vehicle (UAV) equipped with gimballed micro4/3 camera and 25 mm lens. Image scale is established via an independently powered LIDAR/GPS data-logging system recording altitude and GPS location at 1 Hz. Photogrammetric calibration of the camera and lens allowed distortion parameters to be rigorously accounted for during image analysis, via a custom-programmed Graphical User Interface (GUI) running in MATLAB. The datalogger, camera calibration methods and measurement software are adaptable to a wide range of UAV platforms. Mean LIDAR accuracy, measured from 10 bridges 9-39 m above water, was 99.9%. We conducted 136 flights in New Zealand's subantarctic Auckland Islands to measure southern right whales. Mean lengths of 10 individual whales, each photographed between 7 and 15 times, had CVs (SD/mean) ranging from 0.5 to 1.8% (mean = 1.2%). Repeated measurements of a floating reference target showed a mean error of c.1%. Our system is relatively inexpensive, easily put together, produces accurate, repeatable measurements from single vertical images, and hence is applicable to a wide range of ecological questions in marine and terrestrial habitats.

show abstract

Section: Introductionmentioning

confidence: 99%

Inexpensive Aerial Photogrammetry for Studies of Whales and Large Marine Animals

et al. 2017

View full text Add to dashboard Cite

show abstract

“…LiDAR systems are subject to limitations inherent to their principle of operation. Single systems can only measure the so-called line-of-sight (LOS) velocity along the direction of the laser beam, which is calculated from the light backscattered from aerosols [22].…”

Section: Planar Lidar Datamentioning

confidence: 99%

Temporal Up-Sampling of Planar Long-Range Doppler LiDAR Wind Speed Measurements Using Space-Time Conversion

Beck

Kühn

2019

Remote Sensing

View full text Add to dashboard Cite

Measurement campaigns in wind energy research are becoming increasingly complex, which has exacerbated the difficulty of taking optimal measurements using light detection and ranging (LiDAR) systems. Compromises between spatial and temporal resolutions are always necessary in the study of heterogeneous flows, like wind turbine wakes. Below, we develop a method for space-time conversion that acts as a temporal fluid-dynamic interpolation without incurring the immense computing costs of a 4D flow solver. We tested this space-time conversion with synthetic LiDAR data extracted from a large-eddy-simulation (LES) of a neutrally stable single-turbine wake field. The data was synthesised with a numerical LiDAR simulator. Then, we performed a parametric study of 11 different scanning velocities. We found that temporal error dominates the mapping error at low scanning speeds and that spatial error becomes dominant at fast scanning speeds. Our space-time conversion method increases the temporal resolution of the LiDAR data by a factor 2.4 to 40 to correct the scan-containing temporal shift and to synchronise the scan with the time code of the LES data. The mean-value error of the test case is reduced to a minimum relative error of 0.13% and the standard-deviation error is reduced to a minimum of 0.6% when the optimal scanning velocity is used. When working with the original unprocessed LiDAR measurements, the space-time-conversion yielded a maximal error reduction of 69% in the mean value and 58% in the standard deviation with the parameters identified with our analysis.

show abstract

“…Amongst of these sophisticated technologies, an aerial light detection and ranging (aerial LiDAR) or airborne laser scanning (ALS) system can be counted as the most popularly use for urban analyses. Nevertheless, the first LiDAR applications originated from meteorology fields, when the National Centre for Atmospheric Research applied them to measure distances and thicknesses of the clouds (Goyer & Watson, 1963). Eventually, accuracies and usefulness of a LiDAR system for topographic observations were acknowledged, when they were implemented to map the Moon's surfaces in the Apollo 15 mission in 1971 (Rodionov et al, 1971).…”

Section: Evolution In Topographic Data Acquisitionmentioning

confidence: 99%

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

Meesuk¹

2017

View full text Add to dashboard Cite

Although all care is taken to ensure the integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers, the author nor UNESCO-IHE for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein. A pdf version of this work will be made available as Open Access via http://repository.tudelft.nl/ihe This version is licensed under the Creative Commons Attribution-Non Commercial 4.0 International License, http://creativecommons.org/licenses/by-nc/4.0/Published by: CRC Press/Balkema PO Box 11320, 2301 EH Leiden, the Netherlands e-mail: Pub.NL@taylorandfrancis.com www.crcpress.com -www.taylorandfrancis.com to my dear family SummaryModelling urban flood dynamics requires detailed knowledge of a number of complex processes. Numerical simulation of flood propagation requires proper understanding of all processes involved. Also, special care has to be taken to adequately represent the urban topography, taking into account typical urban features like underpasses and hidden alleyways. Incorrect input data will affect the numerical model results, which may lead to inadequate flood-protection measures or even catastrophic flooding situations. This thesis explores how to include particular urban topographic features in urban flood modelling.Aerial Light Detection and Ranging (LiDAR) systems offer opportunities for achieving good quality topographic data, with less fieldwork on the ground. Even though aerial LiDAR data have long been used in many applications, conventional top-view LiDAR data can not quite capture some hidden urban features. However, recent improvements in Structure from Motion (SfM) techniques provide opportunities to achieve improved quality topographic data by using multiple viewpoints, including side-view data.This dissertation explores insights into the capabilities of assimilating SfM point cloud data by using multi-source views as input for enhancing 2D urban flood modelling. Side-view SfM point cloud data are collected and merged with conventional top-view LiDAR point cloud data to create novel Multi-Source Views (MSV) topographic data. The main objectives of this research are (i) to provide insight into the capabilities of using computer-based environments; (ii) to explore the benefits of using the new MSV data; (iii) to enhance 2D model schematizations; (iv) to compare simulated results using new MSV data and conventional top-view LiDAR data as input for urban flood models; and (v) to help developing flood-protection measures. Findings showed that simulation results using conventional top-view LiDAR-DSM as input show the least flood inundation areas and results contained many dry areas. This is because conventional LiDAR-DSM does not capture hidden urban features like underpasses, high trees, overarching structures, which behave as obstacles in 2D model schematics. When applying extended top-view LiDAR-DBM+ and the newly developed MSV-DEM as input, simulation results showed m...

show abstract

The Laser and its Application to Meteorology

Cited by 75 publications

References 7 publications

Inexpensive Aerial Photogrammetry for Studies of Whales and Large Marine Animals

Inexpensive Aerial Photogrammetry for Studies of Whales and Large Marine Animals

Temporal Up-Sampling of Planar Long-Range Doppler LiDAR Wind Speed Measurements Using Space-Time Conversion

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

Contact Info

Product

Resources

About