- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) - Lecco, Italy (2019.9.25-2019.9.27)] 2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) - Luminaire Digital Design Flow with Delphi4LED LEDs Multi-Domain Compact Model
摘要： A novel technique for parameterizing surface roughness in coastal inundation models using airborne laser scanning (lidar) data is presented. Two important parameters to coastal overland flow dynamics, Manning’s n (bottom friction) and effective aerodynamic roughness length (wind speed reduction), are computed based on a random forest (RM) regression model trained using field measurements from 24 sites in Florida fused with georegistered lidar point cloud data. The lidar point cloud for each test site is separated into ground and nonground classes and the z-dimensional (height or elevation) variance from the least squares regression plane is computed, along with the height of the nonground regression plane. These statistics serve as the predictor variables in the parameterization model. The model is then tested using a bootstrap subsampling procedure consisting of removal without replacement of one record and using the surviving records to train the model and predict the surface roughness parameter of the removed record. When compared with the industry standard technique of assigning surface roughness parameters based on published land use/land cover type, the RM regression models reduce the parameterization error by 93% (0.086–0.006) and 53% (1.299–0.610 m) for Manning’s n and effective aerodynamic roughness length, respectively. These improvements will improve water level and velocity predictions in coastal models.
关键词： lidar,Manning’s n,random forest (RM),land cover,Aerodynamic roughness
更新于2025-09-23 15:21:01
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SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning
摘要： Precise structural information collected from plots is significant in the management of and decision-making regarding forest resources. Currently, laser scanning is widely used in forestry inventories to acquire three-dimensional (3D) structural information. There are three main data-acquisition modes in ground-based forest measurements: single-scan terrestrial laser scanning (TLS), multi-scan TLS and multi-single-scan TLS. Nevertheless, each of these modes causes specific difficulties for forest measurements. Due to occlusion effects, the single-scan TLS mode provides scans for only one side of the tree. The multi-scan TLS mode overcomes occlusion problems, however, at the cost of longer acquisition times, more human labor and more effort in data preprocessing. The multi-single-scan TLS mode decreases the workload and occlusion effects but lacks the complete 3D reconstruction of forests. These problems in TLS methods are largely avoided with mobile laser scanning (MLS); however, the geometrical peculiarity of forests (e.g., similarity between tree shapes, placements, and occlusion) complicates the motion estimation and reduces mapping accuracy. Therefore, this paper proposes a novel method combining single-scan TLS and MLS for forest 3D data acquisition. We use single-scan TLS data as a reference, onto which we register MLS point clouds, so they fill in the omission of the single-scan TLS data. To register MLS point clouds on the reference, we extract virtual feature points that are sampling the centerlines of tree stems and propose a new optimization-based registration framework. In contrast to previous MLS-based studies, the proposed method sufficiently exploits the natural geometric characteristics of trees. We demonstrate the effectiveness, robustness, and accuracy of the proposed method on three datasets, from which we extract structural information. The experimental results show that the omission of tree stem data caused by one scan can be compensated for by the MLS data, and the time of the field measurement is much less than that of the multi-scan TLS mode. In addition, single-scan TLS data provide strong global constraints for MLS-based forest mapping, which allows low mapping errors to be achieved, e.g., less than 2.0 cm mean errors in both the horizontal and vertical directions.
关键词： MLS,Single-scan TLS,Forest mapping,SLAM,LiDAR
更新于2025-09-23 15:21:01
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Automated and efficient powerline extraction from laser scanning data using a voxel-based subsampling with hierarchical approach
摘要： For periodic monitoring of power utilities, there has been keen interest by utility companies to extract the powerlines from laser scanning data. However, challenges arise when utilizing large point clouds as well as avoiding false positives or other errors in the extraction due to noise from objects in close proximity to the powerlines. In this study, we propose an efficient and robust approach to overcome these challenges through two main steps: candidate powerline point extraction and refinement. In the candidate powerline point extraction step, a voxel-based subsampling structure temporarily substitutes the original scan points with regularly spaced subsampled points that still preserve key details present within the point cloud but significantly reduce the dataset size. After removing the ground surface and adjacent objects, candidate powerline points are efficiently extracted through a hierarchical, feature-based filtering process. In the refinement step, the link between the subsampled candidate powerline points and original scan point cloud enable the original points to be segmented and grouped into clusters. By fitting mathematical models, an individual powerline is re-clustered and used to reconstruct the broken sections in the powerlines. The proposed approach is evaluated on 30 unique datasets with different powerline configurations acquired at five different sites by either a terrestrial or mobile laser scanning system. The parameters are optimized through a sensitivity analysis with pointwise comparison between the extracted powerlines and ground truth using 10 diverse datasets, demonstrating that only one requisite parameter varied as a function of resolution while the remaining parameters were generally consistent across the datasets. With optimized parameters, the proposed approach achieved F1 scores of 88.87–95.47% with high efficiency ranging from 0.81 and 1.46 million points/sec when tested on 30 datasets.
关键词： Lidar,Powerlines,Voxel-based subsampling,Laser scanning,Point cloud
更新于2025-09-23 15:21:01
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[IEEE 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) - Phuket, Thailand (2020.2.28-2020.2.29)] 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) - Performance Evaluation of Faults in a Photovoltaic Array Based on V-I and V-P Characteristic Curve
摘要： A novel technique for parameterizing surface roughness in coastal inundation models using airborne laser scanning (lidar) data is presented. Two important parameters to coastal overland flow dynamics, Manning’s n (bottom friction) and effective aerodynamic roughness length (wind speed reduction), are computed based on a random forest (RM) regression model trained using field measurements from 24 sites in Florida fused with georegistered lidar point cloud data. The lidar point cloud for each test site is separated into ground and nonground classes and the z-dimensional (height or elevation) variance from the least squares regression plane is computed, along with the height of the nonground regression plane. These statistics serve as the predictor variables in the parameterization model. The model is then tested using a bootstrap subsampling procedure consisting of removal without replacement of one record and using the surviving records to train the model and predict the surface roughness parameter of the removed record. When compared with the industry standard technique of assigning surface roughness parameters based on published land use/land cover type, the RM regression models reduce the parameterization error by 93% (0.086–0.006) and 53% (1.299–0.610 m) for Manning’s n and effective aerodynamic roughness length, respectively. These improvements will improve water level and velocity predictions in coastal models.
关键词： lidar,Manning’s n,random forest (RM),land cover,Aerodynamic roughness
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan (2019.7.28-2019.8.2)] IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Reconstruction of Airborne Laser Scanner Trajectory From Data
摘要： Multi-echo airborne laser scanner (ALS) has shown increasing utility for forestry applications in the two past decades. Among the numerous algorithms developed to process ALS data on forest environments some require to know actual sensor trajectory and deduced angles of incidence. However, sensor trajectory is not part of the ALS standard LAS file format and is often not delivered with point clouds. Scan angle is usually specified with a one byte precision or not given at all. This paper presents a method for the reconstruction of the sensor trajectory from a multi-echo ALS point cloud. It is based on the intersection of multi-echo pulses and was tested on three data sets acquired over a deciduous, a tropical and a mountainous forest, respectively. It allows sensor location estimate and scan angle estimate with less than 25 cm and 2·10-2° error.
关键词： Lidar,Trajectory Inversion,Airborne Laser Scanner,Forest
更新于2025-09-23 15:21:01
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1579 NM Fiber Laser Source for Spaceborne Monitoring of Carbon Dioxide
摘要： We report on the development of a 1579 nm pulsed fiber laser source with high peak-power, intended to be used as a lidar source for CO2 monitoring from space. We first discuss water-vapor sensitivity of spaceborne CO2 measurements by lidar and point the interest of the 1579 nm wavelength with that respect. Then we detail the current development status of the pulsed fiber laser source.
关键词： CO2 monitoring,lidar,1579 nm,spaceborne,fiber laser
更新于2025-09-23 15:21:01
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[Institution of Engineering and Technology 20th Italian National Conference on Photonic Technologies (Fotonica 2018) - Lecce, Italy (23-25 May 2018)] 20th Italian National Conference on Photonic Technologies (Fotonica 2018) - Detecting the influence of water vapour on the measurements of minor chemical gases with the Differential Absorption LIDAR technique
摘要： The monitoring of pollution is fundamental to guarantee the health and the safety of the environment and humans. In fact, pollution is strictly correlated to climate changes, acid rains and in disease occurrences. The Differential Absorption LIDAR (DIAL) is a remote sensing technique able to identify the chemical substances in the atmosphere. This method is able to give information along long distances (also larger than 1 km) and it is possible to provide the system with equatorial mounts, in order to extract areal or volumetric concentration field. In this work, the authors focus on the interaction of water vapour with minor chemical gas measurements. In fact, the DIAL equation is achieved assuming some hypothesis that could not be acceptable in any conditions. In the present paper is evaluated how water vapour could change the differential extinction coefficients and when influences the final measurements. Both analytical investigation and experimental measurement are provided. These methodologies have been applied to six pollutants: ammonia (NH3), Benzene (C6H6), Nitrogen Dioxide (NO2), Nitrous Oxide (N2O), Ozone (O3) and Sulphur Dioxide (SO2).
关键词： DIAL,LIDAR,Water Vapour,Minor Chemicals,Measurements
更新于2025-09-23 15:21:01
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Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data
摘要： Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present above-ground biomass (AGB) in Alberta, Canada, by taking advantage of a spatially explicit data set derived from a combination of forest inventory data from 1968 plots and space-borne light detection and ranging (lidar) canopy height data. Ten climatic variables, together with elevation, were used for model development and assessment. Four approaches, including spatial interpolation, non-spatial and spatial regression models, and decision-tree-based modeling with random forests algorithm (a machine-learning technique), were compared to find the “best” estimates. We found that the random forests approach provided the best accuracy for biomass estimates. Non-spatial and spatial regression models gave estimates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total AGB stock in Alberta forests was estimated to be 2.26 × 109 Mg (megagram), with an average AGB density of 56.30 ± 35.94 Mg ha?1. At the species level, three major tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.39 × 109 Mg biomass, accounting for nearly 62 % of total estimated AGB. Spatial distribution of biomass varied with natural regions, land cover types, and species. Furthermore, the relative importance of predictor variables on determining biomass distribution varied with species. This study showed that the combination of ground-based inventory data, spaceborne lidar data, land cover classification, and climatic and environmental variables was an efficient way to estimate the quantity, distribution and variation of forest biomass carbon stocks across large regions.
关键词： random forests,remote sensing,lidar,forest biomass,carbon storage,Alberta
更新于2025-09-23 15:21:01
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Massively parallel coherent laser ranging using a soliton microcomb
摘要： Coherent ranging, also known as frequency-modulated continuous-wave (FMCW) laser-based light detection and ranging (lidar)1 is used for long-range three-dimensional distance and velocimetry in autonomous driving2,3. FMCW lidar maps distance to frequency4,5 using frequency-chirped waveforms and simultaneously measures the Doppler shift of the reflected laser light, similar to sonar or radar6,7 and coherent detection prevents interference from sunlight and other lidar systems. However, coherent ranging has a lower acquisition speed and requires precisely chirped8 and highly coherent5 laser sources, hindering widespread use of the lidar system and impeding parallelization, compared to modern time-of-flight ranging systems that use arrays of individual lasers. Here we demonstrate a massively parallel coherent lidar scheme using an ultra-low-loss photonic chip-based soliton microcomb9. By fast chirping of the pump laser in the soliton existence range10 of a microcomb with amplitudes of up to several gigahertz and a sweep rate of up to ten megahertz, a rapid frequency change occurs in the underlying carrier waveform of the soliton pulse stream, but the pulse-to-pulse repetition rate of the soliton pulse stream is retained. As a result, the chirp from a single narrow-linewidth pump laser is transferred to all spectral comb teeth of the soliton at once, thus enabling parallelism in the FMCW lidar. Using this approach we generate 30 distinct channels, demonstrating both parallel distance and velocity measurements at an equivalent rate of three megapixels per second, with the potential to improve sampling rates beyond 150 megapixels per second and to increase the image refresh rate of the FMCW lidar by up to two orders of magnitude without deterioration of eye safety. This approach, when combined with photonic phase arrays11 based on nanophotonic gratings12, provides a technological basis for compact, massively parallel and ultrahigh-frame-rate coherent lidar systems.
关键词： photonic chip,FMCW lidar,soliton microcomb,parallel distance and velocity measurements,coherent ranging
更新于2025-09-23 15:21:01
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Improved Two-wavelength Lidar algorithm for Retrieving Atmospheric Boundary Layer Height
摘要： The atmospheric boundary layer height (BLH) is a critical parameter for the spread and dispersion of atmospheric pollutants. We propose an improved two-wavelength Lidar algorithm for retrieving the BLH based on particle clustering. The algorithm was improved from two aspects: sample sequence selection and classifier optimisation. The backscatter coefficient and color ratio were chosen for the sample sequence construction, and Gaussian mixture models were used for the sample sequence classification. The improved method was tested on different real cases and compared with radiosonde measurements. The experimental results demonstrated the viability of the algorithm under weak mixing conditions, which can be problematic for prior methods. In addition, Lidar data from June 2015 to June 2016 were collected to investigate the stability of the improved method. The correlation between the BLH retrieved using the improved algorithm and that from the radiosonde measurements was R2 = 0.92, with an RMSE of 170 m. The correlation between the two-wavelength Lidar algorithm and radiosonde measurements was R2 = 0.82, with an RMSE of 180 m. The results show that the improved algorithm can obtain the BLH effectively and stability.
关键词： Color ratio,Lidar,Atmospheric boundary layer,Aerosol detection,Radiosonde
更新于2025-09-23 15:21:01