Ground-based Scanning Lidar Research Articles

High-Order Taylor Expansion for Wind Field Retrieval Based on Ground-Based Scanning Lidar

The uniform and linear wind models have been commonly used for wind field retrieval in meteorological community. However, the accuracy and robustness of the retrieval results can be quite unsatisfactory due to the mismatch between these models and the real wind distribution, especially under complex wind conditions. In this article, a nonlinear model based on high-order Taylor expansion is proposed to deal with this limitation, and the combination of ridge regression and decomposition-iteration process (denoted as Ridge-DI method) is further introduced to solve the model with high accuracy and robustness. A case study on simulation and field experiment shows that the proposed method with the third-order Taylor expansion can reduce the mean root-mean-square errors (RMSEs) of the retrieved velocities by more than 16.84% in comparison with traditional methods.

LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models

This study is a follow up on a previous one carried out within the frame of the French project SMARTEOLE, during which, a ground-based scanning LiDAR measurement campaign was conducted in the onshore wind farm of Sole du Moulin Vieux. That previous study focused on the wakes of two wind turbines that experienced different degrees of interaction depending on the incoming wind direction, through the processing of LiDAR measurements. The measurement duration (7 months) ensured the statistical convergence of the ensemble-averaged flow fields obtained after holding a categorisation process based on the wind speed at hub height, wind direction, and atmospheric stability, where only near-neutral stability conditions were considered. The present study focuses on integrating the operational data of the wind turbines through SCADA processing to complement the LiDAR wake field observations and to be used as an input for analytical wake models. First, the correlation between the atmospheric stability, deduced from MERRA-2 dataset, and the free-stream turbulence intensity, measured by the wind turbines’ anemometers, is studied for different wind speed ranges. It is observed that the turbulence intensity tends towards a consistent value as the atmospheric stability approaches near-neutral stability conditions, giving confidence into the applied strategy of data categorisation based on MERRA-2 outputs. The influence of the degree of wake interaction on the wake added turbulence, the velocity and power deficits between both turbines is assessed. Clear trends between the wake added turbulence and both the velocity and power deficits are detected. Consequently, two fitting laws are proposed. Then, different analytical wake models and wake superposition methods are fed with the operational data deduced from the processed SCADA data, and are used for predicting the evolution of the velocity deficit within the wake. Some statistical metrics are used for error quantification of the different engineering wake models compared to the scanning LiDAR measurements, used as reference, and Blondel and Cathelain produces the closest results to the field measurements.

Maximum-Likelihood Retrieval of Volcanic Ash Concentration and Particle Size From Ground-Based Scanning Lidar

An inversion methodology, named maximum-likelihood (ML) volcanic ash light detection and ranging (Lidar) retrieval (VALR-ML), has been developed and applied to estimate volcanic ash particle size and ash mass concentration within volcanic plumes. Both estimations are based on the ML approach, trained by a polarimetric backscattering forward model coupled with a Monte Carlo ash microphysical model. The VALR-ML approach is applied to Lidar backscattering and depolarization profiles, measured at visible wavelength during two eruptions of Mt. Etna, Catania, Italy, in 2010 and 2011. The results are compared with those of ash products derived from other parametric retrieval algorithms. A detailed comparison among these different retrieval techniques highlights the potential of VALR-ML to determine, on the basis of a physically consistent approach, the ash cloud area that must be interdicted to flight operations. Moreover, the results confirm the usefulness of operating scanning Lidars near active volcanic vents.

Determination of the smoke-plume heights and their dynamics with ground-based scanning lidar.

Lidar-data processing techniques are analyzed, which allow determining smoke-plume heights and their dynamics and can be helpful for the improvement of smoke dispersion and air quality models. The data processing algorithms considered in the paper are based on the analysis of two alternative characteristics related to the smoke dispersion process: the regularized intercept function, extracted directly from the recorded lidar signal, and the square-range corrected backscatter signal, obtained after determining and subtracting the constant offset in the recorded signal. The analysis is performed using experimental data of the scanning lidar obtained in the area of prescribed fires.

The Canopy Semi-analytic Pgap And Radiative Transfer (CanSPART) model: Validation using ground based lidar

A canopy structure model based on simple geometric forms and parameterised with plot-scale statistical biometric data is used to predict gap probability (Pgap) profiles. The Semi-analytic Pgap And Radiative Transfer (CanSPART) model is flexibly formulated using distributions of tree height, crown and stem dimensions and can be used for ‘bottom-up’ or ‘top-down’ modelling of gap profiles. Gap probabilities derived from a ground-based scanning lidar are used to validate the output of the model. Direct comparisons of Pgap are made at a range of zenith angles as well as mean foliage profiles calculated from a combination of profiles at multiple angles. Test sites include one pine plantation and several eucalypt woodland sites with varying density, structural complexity, layering and clumping. It is shown that this model performs better in clumped canopies than a simple exponential model and there is agreement with the lidar data except in the near-vertical profiles where spatial variance is not fully accounted for in the measured data. Model-derived canopy gap probabilities compared with observations using a one-sample t-test are significant for 11 out of 12 test sites. Simplification of some of the model inputs was successful at some test sites, but at the most complex site the full model was needed to satisfactorily replicate the shape of the measured gap probability profiles.

Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint

This paper presents a method for using the intensity of returns from a scanning light detection and ranging (lidar) system from a single viewing point to identify the location and measure the diameter of tree stems within a forest. Such instruments are being used for rapid forest inventory and to provide consistent supporting information for airborne lidars. The intensity transect across a tree stem is found to be consistent with a simple model parameterised by the range and diameter of the trunk. The stem diameter is calculated by fitting the model to transect data. The angular span of the stem can also be estimated by using a simple threshold where intensity values are tested against the expected intensity for a stem of given diameter. This is useful when data are insufficient to fit the model or the stem is partially obscured. The process of identifying tree positions and trunk diameters is fully automated and is shown to be successful in identifying a high proportion of trees, including some that are partially obscured from view. The range and bearing to trees are in excellent agreement with field data. Trunk angular span and diameter estimations are well correlated with field measurements at the plot scale. The accuracy of diameter estimation is found to decrease with range from the scanning position and is also reduced for stems subtending small angles (less than twice the scanning resolution) to the instrument. A method for adjusting survey results to compensate for trees missed due to obscuration from the scanning point and the use of angle count methods is found to improve basal area estimates and achieve agreement within 4% of field measurements.

Detailed Stem Measurements of Standing Trees from Ground-Based Scanning Lidar

Abstract A commercially available pan-and-tilt mount laser scanner was used to acquire data for subsequent three-dimensional modeling and measurement of standing forest trees. Methods were developed for identifying trees in range images and co-registering range images acquired from different vantage points. Upper-stem diameters and branch heights derived from the range images were compared to measurements made after the felling of a small number of loblolly pine (Pinus taeda L.) trees. Tree identification assumed bole cross-sections were circular, estimating their geometric centers at successive heights up the stem. Tree center estimates at multiple heights were then used to co-register images made from different vantage points. Co-registration (x, y) errors did not exceed 2.1 cm in any of the 18 pairwise registrations carried out. Results showed excellent agreement (average error < 1 cm) between the lidar-derived diameter estimates and caliper measurements for bole sections below the base of live crown. Less accurate estimates (<2 cm) were obtained for stem heights up to 13 m. Results indicated the potential for accurate assessment of branch or whorl heights using ground-based scanning lidar, with the greatest accuracy likely to be realized for branches near the base of the live crown and below it.

Optical coherency of sunphotometry, sky radiometry and lidar measurements during the early phase of Pacific 2001

Passive sunphotometry and sky radiometry data at sites in the Lower Fraser Valley (Langley-Lochiel) and Saturna Island were synchronously acquired with ground-based scanning lidar (Rapid Acquisition Scanning Aerosol Lidar, RASCAL) and airborne lidar (AERosol Imaging Airborne Lidar, AERIAL) during the Pacific 2001 Air Quality Study. The temporal and spatial behavior of these optical measurements is investigated during a pollution event which occurred from 13 to 16 August 2001. A mid-day minimum in lidar-derived extinction to backscatter ratios (Sa) values, was attributed, at least in part, to the relative humidity (RH) induced optical influence of the column integrated fine mode and/or coarse mode particles in the PBL. Systematically larger Sa values predicted by the sunphotometer and sky radiance inversions were hypothesized to be due to differences between the retrieved refractive index and the actual refractive index of the coarse mode. Aerosol optical depth differences were within maximum error bounds (0.02) when comparing the sunphotometry with spatial maps derived from AERIAL transects. Daily temporal trends of optical and microphysical parameters derived from sunphotometry and sky radiance data at this site were consistent with information deduced from the lidar and meteorological data; while the daily aerosol optical depth decrease was clearly associated with particle removal induced by daytime sea-breeze advection, a significant-to-dominant part of this decrease was associated with decreasing RH growth effects on at least one day.

Assessing forest metrics with a ground-based scanning lidar

A ground-based scanning lidar (light detection and ranging) system was evaluated to assess its potential utility for tree-level forest mensuration data extraction. Ground-based-lidar and field-mensuration data were collected for two forest plots: one located within a red pine (Pinus resinosa Ait.) plantation and another in a mixed deciduous stand dominated by sugar maple (Acer saccharum Marsh.). Five lidar point cloud scans were collected from different vantage points for each plot over a 6-h period on 5 July 2002 using an Optech Inc. ILRIS-3D laser imager. Field- validation data were collected manually over several days during the same time period. Parameters that were measured in the field or derived from manual field measures included (i) stem location, (ii) tree height, (iii) stem diameter at breast height (DBH), (iv) stem density, and (v) timber volume. These measures were then compared with those derived from the ILRIS-3D data (i.e., the lidar point cloud data). It was found that all parameters could be measured or derived from the data collected by the ground-based lidar system. There was a slight systematic underestimation of mean tree height resulting from canopy shadow effects and suboptimal scan sampling distribution. Timber volume estimates for both plots were within 7% of manually derived estimates. Tree height and DBH parameters have the potential for objective measurement or derivation with little manual intervention. However, locating and counting trees within the lidar point cloud, particularly in the multitiered deciduous plot, required the assistance of field-validation data and some subjective interpretation. Overall, ground-based lidar demonstrates promise for objective and consistent forest metric assessment, but work is needed to refine and develop automatic feature identification and data extraction techniques.