Offshore wind profiling using light detection and ranging measurements

\nThe atmospheric monitoring is very important for the observation of air shower by air fluorescence technique.\nIn the Telescope Array (TA) experiment, LIDAR (LIght Detection And Ranging) system and CLF (Central Laser Facility) system have been used for the measurement of atmospheric transparency. The LIDAR system is located in the southeast of TA site. The CLF is located in the center of the TA site. The usefulness of the CLF and LIDAR systems is demonstrated by analyzing the time variation of atmospheric transparency with the systems. The two atmospheric monitor systems are complementary. Therefore, monitoring efficiency is advanced by a new LIDAR system that was installed at CLF system.\nClouds are observed with CCD camera, IR camera and eye scan visual check. In addition, we have also measured atmospheric parameters at the ground level using several weather systems.\n

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.

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.

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

: 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.

A second-generation Gamma Remote Sensing Portable RAdio Detection and Ranging (RADAR) Interferometer (GPRI-II) and a Lieca C-10 LIght Detection and Ranging (LIDAR) were employed to monitor the volume change behavior of two thin compacted clay test sections at the University of Arkansas Cato Springs Research Center (UACRSC). The viability of using remote sensing methods to accurately identify and quantify deformation associated with expansive clay behavior was evaluated. Two 464.52 m2 (5,000 ft2) sections consisting of 20.3 cm (8 inches) thick compacted clay layer underlain by a 5.1 cm (2 inches) thick sand drainage blanket were constructed. One test section was amended with three (Phase 1) and six (Phase 2) percent sodium bentonite. Four LIDAR and 355 RADAR observations were captured from the roof of the UACSRC building between January and July, 2012. Multiple RADAR images were captured on a weekly basis and after significant precipitation events. RADAR measurements, obtained by calculating the interferometric phase difference between coregistered temporally separated RADAR images, were used to determine topography and deformation. LIDAR measurements, as obtained from LIDAR point clouds, were also used to determine topography and deformation. To obtain accurate topography and deformation measurements the vegetation effects within the LIDAR data was removed. Deformation maps generated using both the RADAR and LIDAR methods are presented and compared. The results indicate both technologies are capable of detecting surface movements associated with expansive clay behavior. Moreover, the surface movements associated with expansive clay behavior can be accurately quantified using the LIDAR device.

Abstract. Quantification of tropical forest above-ground biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne light detection and ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed, and regression models could be improved through the use of LiDAR point densities as input (R2 = 0.88; n = 52). Surveying with a LiDAR point density per square metre of about 4 resulted in the best cost / benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site-specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 43%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong greenhouse gas (GHG) emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments.

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

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.

Review on Lidar Technology

<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>

Accurate topographic information is critical for a number of applications in science and engineering. In recent years, airborne light detection and ranging (LiDAR) has become a standard tool for acquiring high quality topographic information. The assessment of airborne LiDAR derived DEMs is typically based on (i) independent ground control points and (ii) forward error propagation utilizing the LiDAR geo-referencing equation. The latter approach is dependent on the stochastic model information of the LiDAR observation components. In this paper, the well-known statistical tool of variance component estimation (VCE) is implemented for a dataset in Houston, Texas, in order to refine the initial stochastic information. Simulations demonstrate the impact of stochastic-model refinement for two practical applications, namely coastal inundation mapping and surface displacement estimation. Results highlight scenarios where erroneous stochastic information is detrimental. Furthermore, the refined stochastic information provides insights on the effect of each LiDAR measurement in the airborne LiDAR error budget. The latter is important for targeting future advancements in order to improve point cloud accuracy.

The ability to measure vegetation structure at spatial scales that are biologically meaningful for wildlife is often limited because information about the spatial scale of habitat selection is lacking and there are logistical constraints to measuring vegetation structure at ever larger spatial scales. To address this challenge, we used LiDAR-derived measurements of vegetation canopy height to quantify habitat associations of riparian birds at the Cosumnes River Preserve in central California, USA. Our objectives were (1) to evaluate the utility of LiDAR (light detection and ranging) measurements for describing habitat associations of riparian passerine birds, and (2) to capitalize on the ease with which LiDAR measurements can be summarized at multiple spatial scales to evaluate the predictive performance of vegetation measurements across spatial scales from 0.2 to 50 ha. At each location where we conducted point-count surveys of the avian community, we summarized the mean and coefficient of variation of canopy height measured at five spatial scales (0.2, 0.8, 3.1, 12.6, and 50.2 ha). For each of these spatial scales, we used stepwise model selection to identify the best logistic-regression model describing patterns of occurrence for 16 species of passerine birds that were sufficiently abundant for analysis. We then used area-under-the-curve (AUC) values to identify models that performed well (AUC > 0.75) on a temporally independent data set. Of the 16 species, 10 species had logistic-regression models with AUC values > 0.75. For six of these species, AUC values were highest for the models with vegetation measurements at the 0.2-3 ha scale. For the other four species, AUC values were highest for the model with vegetation variables measured at the 50-ha scale. These results illustrate the utility of using LiDAR-derived measurements of vegetation to understand habitat associations of riparian birds and underscore the importance of using multiscale approaches to modeling wildlife habitat use.

Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data

The expansion of wind energy development has resulted in larger wind farms and closer placement of turbines to utilize the space available. Each turbine produces a wake that affects downstream turbines, which causes issues such as production loss, blade loading, and fluctuating electrical output. To minimize this impact, the wake produced by turbines must first be understood. Experimental methods were used in the exploration of the wake effects of wind turbines. Smoke visualization inside the University of Waterloo Wind Generation Facility was used to determine the helical vortex wake distribution behind a 3.3 m diameter turbine as well as the tip vortex shedding from the blade. Wind Doppler Light Detection and Ranging (LiDAR) measurement devices were modified and used to measure wind turbine wake velocities. The University of Waterloo Wind Energy Group has a ZephIR z150 LiDAR to use in this study. The LiDAR was verified under normal operation for accuracy against a cup and vane anemometer. The LiDAR was then verified for accuracy and to determine the position of measurements after modifications necessary for future wake measurement experiments.