Atmospheric monitor for Telescope Array experiment

The beam scanner is a predominant part in the light detection and ranging (LiDAR) system to achieve three-dimensional (3D) imaging. The solid-state beam-steering device has emerged as a promising candidate technology for a beam scanner with the advantages of robustness, stability, and high scanning speed. Here we propose a frequency modulated continuous wave (FMCW) LiDAR system with an in-fiber solid-state beam scanner. A 45° tilted fiber grating (TFG) is first employed to achieve in-fiber solid-state spectral scanning in the LiDAR system. A maximum output efficiency of 93.7% is achieved with proper polarization control. A single-mode fiber is then used to fabricate a 2-cm 45° TFG, which significantly reduces the size and the cost of the beam scanner in the LiDAR system. We experimentally realize 3D imaging of targets placed at a distance of 1.2 m based on our proposed LiDAR system. In addition, the system can achieve a detection distance of 6 m with a ranging precision of 24 mm.

Agricultural production, the main pillar of food security, is highly dependent on soil quality, and threatened by erosion processes that degrade soil quality. This article is part of a research to verify the usefulness of differential interferometric analysis on TopSAR (Terrain Observation with Progressive Scans SAR, Synthetic Aperture Radar) images to measure water and tillage erosion in small agricultural basins. For this, images from the Sentinel 1 mission are used, analyzing the deformations on the earth’s surface. The purpose of this research is to verify the accuracy of the proposed method by comparing its measures with the ones taken with the gold standard laser terrestrial LIDAR (Light Detection and Ranging) system, as well as to establish a basic step period framework that guarantees an admissible loss of coherence. The results on a pilot plot in El Molar (north of Madrid, Spain) showed that the differences lay within the range of the error associated with the very LIDAR system and showed that coherence losses correspond with the deformations measured. Given the economic and labor advantages of the differential interferometric analysis, this method could be regarded as an excellent alternative to the use of LIDAR in large-scale studies for measuring ground deformation caused by water and tillage erosion.

Airborne light detection and ranging (LIDAR) system uses in many field such as military, agriculture, geology and transportation. LIDAR data acquisition consumes several gigabytes from the storage capacity of the system. So, it's necessary to reduce the on board storage capacity as well as its transmission rate. Compression of LIDAR data means reducing the amount of data required to represent the digital data acquired by LIDAR system. This is done by removing data redundancies to achieve compression. In this paper we explain LIDAR system theory and component, compare between lossy and lossless compression techniques then we discuss LIDAR data compression techniques in order to choose a suitable one for LIDAR system that gives high compression ratio with suitable processing time. In most LIDAR data compression techniques classification of LIDAR data is an important issue as a preprocessing step to simplify the data. Using more than one compression algorithms increase the compression ratio but increase the processing time as well.

Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction

The advantages and limitations of the ZephIR®, a continuous‐wave, focused light detection and ranging (LiDAR) wind profiler, to observe offshore winds and turbulence characteristics were tested during a 6 month campaign at the transformer/platform of Horns Rev, the world's largest wind farm. The LiDAR system is a ground‐based sensing technique which avoids the use of high and costly meteorological masts. Three different inflow conditions were selected to perform LiDAR wind profiling. Comparisons of LiDAR mean wind speeds against cup anemometers from different masts showed high correlations for the open sea sectors and good agreement with their longitudinal turbulence characteristics. Cup anemometer mean wind speed profiles were extended with LiDAR profiles up to 161 m on each inflow sector. The extension resulted in a good profile match for the three surrounding masts. These extended profiles, averaged over all observed stabilities and surface roughness lengths, were compared to the logarithmic profile. The observed deviations were relatively small. Offshore wind farm wakes were also observed from LiDAR measurements where the wind speed deficits were detected at all LiDAR heights. Profile‐derived friction velocities and roughness lengths were compared to Charnock's sea roughness model. These average values were found to be close to the model, although the scatter of the individual estimations of sea roughness length was large. Copyright © 2008 John Wiley & Sons, Ltd.

Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning

Power-line inspection is an important means to maintain the safety of power networks. Light detection and ranging (LiDAR) technology can provide high-precision 3D information about power corridors for automated power-line inspection, so there are more and more utility companies relying on LiDAR systems instead of traditional manual operation. However, it is still a challenge to automatically detect power lines with high precision. To achieve efficient and accurate power-line extraction, this paper proposes an algorithm using entropy-weighting feature evaluation (EWFE), which is different from the existing hierarchical-multiple-rule evaluation of many geometric features. Six significant features are selected (Height above Ground Surface (HGS), Vertical Range Ratio (VRR), Horizontal Angle (HA), Surface Variation (SV), Linearity (LI) and Curvature Change (CC)), and then the features are combined to construct a vector for quantitative evaluation. The feature weights are determined by an entropy-weighting method (EWM) to achieve optimal distribution. The point clouds are filtered out by the HGS feature, which possesses the highest entropy value, and a portion of non-power-line points can be removed without loss of power-line points. The power lines are extracted by evaluation of the other five features. To decrease the interference from pylon points, this paper analyzes performance in different pylon situations and performs an adaptive weight transformation. We evaluate the EWFE method using four datasets with different transmission voltage scales captured by a light unmanned aerial vehicle (UAV) LiDAR system and a mobile LiDAR system. Experimental results show that our method demonstrates efficient performance, while algorithm parameters remain consistent for the four datasets. The precision F value ranges from 98.4% to 99.7%, and the efficiency ranges from 0.9 million points/s to 5.2 million points/s.

<p>LiDAR (Light Detection and Ranging) is a technology that provides three-dimensional point cloud data with high spatial accuracy. It is increasingly used in various applications and disciplines, including engineering. Along with other capabilities, LiDAR systems are able to record reflected backscattered energy as intensity data and measured distances to targets as range data. As the laser signal wavelength that operates in LiDAR sensors is typically in the near-infrared (NIR) spectrum, high spectral reflectance separability can be observed and a number of different materials distinguished. These capabilities have motivated researchers to study the applicability of using LiDAR range and intensity data for pavement crack extraction.</p> <p>The main goal of the present research is to examine the potential use of LiDAR range and intensity data for automated pavement crack detection. The study explores the following: a) crack detection using the two image classification logics of pixel-based and object-based image classification; b) crack detection using the Maximum Likelihood Classifier (MLC), the Random Trees Classifier (RTC), and the Support Vector Machine (SVM) classifier; c) crack detection by conducting image classification of four scenarios of multi-layer images generated from LiDAR data; and d) crack detection using LiDAR data with different point spacing.</p> <p>The experimental results indicate that the use of LiDAR data independently is effective for pavement crack detection. In addition, the results show that object-based image classification logic optimizes classification accuracy and crack detection results, and that employing SVM enhances the classification process and improves crack detection accuracy. It is also observed that using range data separately produces high accuracy for the crack detection results while the accuracy is slightly reduced when intensity data are combined with range data compared with using range data only. Furthermore, in the classification process, adding slope and aspect layers to range and intensity data decreases the accuracy of crack detection results, while increasing LiDAR data point spacing reduces crack extraction accuracy.</p>

Characterization of environmental loads related concrete pavement deflection behavior using Light Detection and Ranging technology

: Recent improvements in mechanical and optical instrumentation precision have occurred. Therefore, the removal of meteorologically caused systematic errors is now a major consideration in stellar position accuracy and in the repeatability of arrangement in positions taken during nightly observations. The two problems singled out in this report are absolute water vapor content and temperature structure measurements. The study revealed that (1) estimates of water vapor distribution with an accuracy of approximately 20% would routinely correct position errors of 0.01 of angle and (2) that knowledge of the temperature structure with an accuracy of 0.5K would remove the systematic errors caused by isopycnic tilts, which range from 0.01 up to 0.1 of angle at extreme zenith viewing angles. The study showed that a remote-sensing solution to the measurement problems of the U.S. Naval Observatory in Washington, DC, is provided by LIDAR (Light Detection and Ranging). Two major LIDAR systems that fulfill the Observatory's needs are based on Raman and DIAL (Differential Absorption Lidar) techniques. A study and review of experimental system using the DIAL and Raman techniques indicate tht a DIAL system is the recommended approach for simultaneous measurement of water vapor density and temperature. A DIAL system should be constructed to obtain ground-based vertical water vapor and temperature measurements. Further, alexandrite should be used as the lasing medium for the LIDAR system.

The aim of this paper is to propose a new method of scanning the field of view (FOV) in light detection and ranging (LiDAR) systems by utilizing computer-generated holography (CGH) method with a spatial light modulator (SLM). Based on replacing the objective in the telescope with the SLM, we were able to successfully scan large FOVs in LiDAR systems, confirming the effectiveness of this approach. The proposed conceptual breakthrough of this approach will be presented as part of the preliminary studying project for the LiDAR system in the satellite payloads.

A small cost-low civilian UAV (Unmanned Aerial Vehicle UAV) platform usually requests that all carried components should be light in weight, small in volume, and efficient in energy. A laser diode adopted in LiDAR (Light Detection And Ranging) system is often driven by pulsed voltage, which is several 100 V in voltage, 10-100 A in current, several hundred millisecond in pulse width. A DC-DC converter and a fast switch were often applied by the traditional power supply for driving the LiDAR system. This traditional power supply had problems in efficiency and bulk, has been demonstrated that it is not proper for application on a small low-cost civilian UAV platform. In this paper, a novel power supply topology, which is based on two coupled coils, pulse generator circuit, and a fast switch, is proposed. The power supply topology has been designed and assembled. After tested, it is confirmed that the proposed power supply is able to generate pulse voltage of 100-300 V, up to 120 A pulse current, 50-200 μs pulse width, and 50 Hz maximum pulse frequency. The specification is sufficient to drive a laser diode used in LiDAR scanning system. This power supply topology gives rid of the DC-DC converter, and is light in weight, small in volume and suitable to be used on a UAV platform.

In order to increase the detected range and reduce the effect of signal-induced noise in light detection and ranging (LIDAR) measurements of the high atmospheric layers, we have developed a normally off-gated photomultiplier tube module for use in the photon-counting mode. The module allows the detection of the weak backscattering signal, while at the same time rejecting the intense backscattering signal from the near range with an adjustable rejection range. The gating of our module is based on the application of positive voltage pulses on all the even dynodes of a photomultiplier head. We describe the electronic circuit design and discuss the implementation of the module. The capability of the module in rejecting intense backscattering signal from the near range was tested on our LIDAR system. The agreement between the range-corrected LIDAR signals and the standard atmospheric molecular number density up to a height of 60 km indicates a good performance by the module. Comparison between Raman and elastic backscattering signals under cloudy sky conditions indicates that the intense backscattering from near range is sufficiently suppressed and has a negligible effect on the performance of our module.

Light detection and ranging (LiDAR) systems based on direct time-of-flight (DTOF) are used in spacecraft navigation, assembly-line robotics, augmented and virtual reality (AR/VR), (drone-based) surveillance, advanced driver assistance systems (ADAS), and autonomous cars. Common requirements are accuracy and speed, while ensuring long operating distance, high tolerance to background illumination and robustness to interference from other LiDAR systems. To meet these demands, the DTOF sensor community has provided numerous architectures, typically making use of resource sharing that often introduces tradeoffs between pixel count and speed. If resource sharing is not used, reduced fill factor, high non-uniformity, and pile-up distortion generally arise, thus limiting overall performance [1].

Efficient tree modeling from airborne LiDAR point clouds