Ice Nucleation of Cirrus Clouds Related to the Transported Dust Layer Observed by Ground-Based Lidars over Wuhan,China

Cirrus clouds related to transported dust layers were identified on 22 occasions with ground-based polarization lidar from December 2012 to February 2018 over Wuhan(30.5°N,114.4°E),China.All the events occurred in spring and winter.Cirrus clouds were mostly located above 7.6 km on top of the aloft dust layers.In-cloud relative humidity with respect to ice(RH_(i))derived from water vapor Raman lidar as well as from ERA5 reanalysis data were used as criteria to determine the possible ice nucleation regimes.Corresponding to the two typical cases shown,the observed events can be classified into two categories:(1)category A(3 cases),in-cloud peak RH_(i)≥150%,indicating competition between heterogeneous nucleation and homogeneous nucleation;and(2)category B(19 cases),in-cloud peak RH_(i)<150%,revealing that only heterogeneous nucleation was involved.Heterogeneous nucleation generally took place during instances of cirrus cloud formation in the upper troposphere when advected dust particles were present.Although accompanying cloud-top temperatures ranged from–51.9℃to–30.4℃,dust-related heterogeneous nucleation contributed to primary ice nucleation in cirrus clouds by providing ice nucleating particle concentrations on the order of 10^(−3)L^(−1)to 10^(2)L^(−1).Heterogeneous nucleation and subsequent crystal growth reduced the ambient RH_(i)to be less than 150%by consuming water vapor and thus completely inhibited homogeneous nucleation.

Comparison of Two Air Pollution Episodes over Northeast China in Winter 2016/17 Using Ground-Based Lidar

This study analyzes and compares aerosol properties and meteorological conditions during two air pollution episodes in 19–22（E1） and 25–26（E2） December 2016 in Northeast China. The visibility, particulate matter（PM） mass concentration, and surface meteorological observations were examined, together with the planetary boundary layer（PBL） properties and vertical profiles of aerosol extinction coefficient and volume depolarization ratio that were measured by a ground-based lidar in Shenyang of Liaoning Province, China during December 2016–January 2017.Results suggest that the low PBL height led to poor pollution dilution in E1, while the high PBL accompanied by low visibility in E2 might have been due to cross-regional and vertical air transmission. The PM mass concentration decreased as the PBL height increased in E1 while these two variables were positively correlated in E2. The enhanced winds in E2 diffused the pollutants and contributed largely to the aerosol transport. Strong temperature inversion in E1 resulted in increased PM2.5 and PM10 concentrations, and the winds in E2 favoured the southwesterly transport of aerosols from the North China Plain into the region surrounding Shenyang. The large extinction coefficient was partially attributed to the local pollution under the low PBL with high ground-surface PM mass concentrations in E1,whereas the cross-regional transport of aerosols within a high PBL and the low PM mass concentration near the ground in E2 were associated with severe aerosol extinction at high altitudes. These results may facilitate better understanding of the vertical distribution of aerosol properties during winter pollution events in Northeast China.

Ground-based and additional science support for SMILE

The joint European Space Agency and Chinese Academy of Sciences Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission will explore global dynamics of the magnetosphere under varying solar wind and interplanetary magnetic field conditions,and simultaneously monitor the auroral response of the Northern Hemisphere ionosphere.Combining these large-scale responses with medium and fine-scale measurements at a variety of cadences by additional ground-based and space-based instruments will enable a much greater scientific impact beyond the original goals of the SMILE mission.Here,we describe current community efforts to prepare for SMILE,and the benefits and context various experiments that have explicitly expressed support for SMILE can offer.A dedicated group of international scientists representing many different experiment types and geographical locations,the Ground-based and Additional Science Working Group,is facilitating these efforts.Preparations include constructing an online SMILE Data Fusion Facility,the discussion of particular or special modes for experiments such as coherent and incoherent scatter radar,and the consideration of particular observing strategies and spacecraft conjunctions.We anticipate growing interest and community engagement with the SMILE mission,and we welcome novel ideas and insights from the solar-terrestrial community.

Improving Satellite-Retrieved Cloud Base Height with Ground-Based Cloud Radar Measurements

Cloud base height(CBH) is a crucial parameter for cloud radiative effect estimates, climate change simulations, and aviation guidance. However, due to the limited information on cloud vertical structures included in passive satellite radiometer observations, few operational satellite CBH products are currently available. This study presents a new method for retrieving CBH from satellite radiometers. The method first uses the combined measurements of satellite radiometers and ground-based cloud radars to develop a lookup table(LUT) of effective cloud water content(ECWC), representing the vertically varying cloud water content. This LUT allows for the conversion of cloud water path to cloud geometric thickness(CGT), enabling the estimation of CBH as the difference between cloud top height and CGT. Detailed comparative analysis of CBH estimates from the state-of-the-art ECWC LUT are conducted against four ground-based millimeter-wave cloud radar(MMCR) measurements, and results show that the mean bias(correlation coefficient) is0.18±1.79 km(0.73), which is lower(higher) than 0.23±2.11 km(0.67) as derived from the combined measurements of satellite radiometers and satellite radar-lidar(i.e., Cloud Sat and CALIPSO). Furthermore, the percentages of the CBH biases within 250 m increase by 5% to 10%, which varies by location. This indicates that the CBH estimates from our algorithm are more consistent with ground-based MMCR measurements. Therefore, this algorithm shows great potential for further improvement of the CBH retrievals as ground-based MMCR are being increasingly included in global surface meteorological observing networks, and the improved CBH retrievals will contribute to better cloud radiative effect estimates.

Ground-Based LiDAR Improves Phenotypic Repeatability of Above-Ground Biomass and Crop Growth Rate in Wheat

Highly repeatable,nondestructive,and high-throughput measures of above-ground biomass(AGB)and crop growth rate(CGR)are important for wheat improvement programs.This study evaluates the repeatability of destructive AGB and CGR measurements in comparison to two previously described methods for the estimation of AGB from LiDAR:3D voxel index(3DVI)and 3D profile index(3DPI).Across three field experiments,contrasting in available water supply and comprising up to 98 wheat genotypes varying for canopy architecture,several concurrent measurements of LiDAR and AGB were made from jointing to anthesis.Phenotypic correlations at discrete events between AGB and the LiDAR-derived biomass indices were significant,ranging from 0.31(P<0:05)to 0.86(P<0:0001),providing confidence in the LiDAR indices as effective surrogates for AGB.The repeatability of the LiDAR biomass indices at discrete events was at least similar to and often higher than AGB,particularly under water limitation.The correlations between calculated CGR for AGB and the LiDAR indices were moderate to high and varied between experiments.However,across all experiments,the repeatabilities of the CGR derived from the LiDAR indices were appreciably greater than those for AGB,except for the 3DPI in the water-limited environment.In our experiments,the repeatability of either LiDAR index was consistently higher than that of AGB,both at discrete time points and when CGR was calculated.These findings provide promising support for the reliable use of ground-based LiDAR,as a surrogate measure of AGB and CGR,for screening germplasm in research and wheat breeding.

Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels.This research develops a general framework to integrate ground-based and UAV-LiDAR(ULS)data to better estimate tree parameters based on quantitative structure modelling(QSM).This is accomplished in three sequential steps.First,the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy.Next,redundancy and noise were removed for the ground-based/ULS LiDAR data fusion.Finally,tree modeling and biophysical parameter retrieval were based on QSM.Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest,including poplar and dawn redwood species.Generally,ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data.The fusion-derived tree height,tree volume,and crown volume significantly improved by up to 9.01%,5.28%,and 18.61%,respectively,in terms of rRMSE.By contrast,the diameter at breast height(DBH)is the parameter that has the least benefits from fusion,and rRMSE remains approximately the same,because stems are already well sampled from ground data.Additionally,particularly for dense forests,the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR.Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests,whereby the improvement owing to fusion is not significant.

智航无人机载LiDAR系统在轨道交通建设中的应用分析

为了减少轨道交通建设外业勘测的工作量,提高制图的效率和精度,本文首先基于实际项目,选用智航SF1650六旋翼无人机载LiDAR系统对济南新东站片区进行航摄,利用获取的LiDAR点云和影像数据制作DEM、DOM,然后再进行大比例尺地形图和片区断面图的制作。通过外业勘测核实,DEM和DOM精度完全满足轨道交通建设工程制作大比例尺地形图的要求,且相较于传统立体航测,精度和数据利用率得到很大的提升,极大减少了外业勘测工作量,验证了智航SF1650无人机载LiDAR系统的可行性,为今后的工程应用提供了参考方案。

基于机载LiDAR技术植被茂密区小型滑坡识别与评价

山体滑坡会导致生命和财产损失,获取完整的滑坡空间分布图及对易发区域的准确判定有利于指导生产、生活和生态空间优化。在滑坡调查过程中,茂密的植被覆盖使滑坡调查难度加大,机载激光雷达(light detection and ranging,LiDAR)技术的穿透能力使真实地形特征得以呈现,从而实现植被茂密区滑坡识别。该文通过仿地飞行获取研究区LiDAR点云数据,基于点云数据得到数字高程模型(digital elevation model,DEM),在山体阴影分析、彩色增强显示及三维场景模拟基础上,识别出区域内已有滑坡的位置与规模,经野外核实,滑坡解译精度为86.4%。针对滑坡易发区评价问题,以现有滑坡为样本,首次采用遥感分类思维开展滑坡易发区划定,采用小区域内与滑坡发育有关的高程、坡度和地表起伏度组合成影像,以支持向量机为分类方法,判定出滑坡易发区域,经滑坡检验样本分析,滑坡识别精度为81.91%。研究表明:基于高精度的LiDAR数据及其视觉增强后的图像能识别小型滑坡,采用支持向量机分类法可以准确确定滑坡易发区,为下一步三生空间规划与优化提供依据。

基于RS-Conv的多尺度神经网络LiDAR点云断裂带提取方法

断裂带与地震、滑坡等自然灾害的发生有着密切关系,精准提取断裂带不仅可为地震断层的定量化研究提供指导,还可为地震灾害风险评估及防震减灾决策的制定提供科学依据。针对现有方法中LiDAR点云断裂带提取不完整、连续性差及错误率高等问题,文中提出了一种基于RS-Conv的多尺度神经网络LiDAR点云断裂带提取方法,以便更好地解决复杂地形区域的断裂带自动提取问题。该方法首先构建不同空间尺度的邻域点集,从而更全面地考察点云的局部几何结构特征。考虑到RS-Conv算子能够很好地表征中心点与邻域点的空间关系,文中以RS-Conv算子作为卷积模块构建了多尺度神经网络模型,以提取出LiDAR点云不同尺度的深层次特征,对其进行堆叠并输入到全连接层,以完成对断裂带点的提取。最后,在ISPRS点云数据集、川滇点云数据集和鲜水河数据集上对文中所述方法与张量分解方法和Deep Neural Networks(DNN)方法进行了对比实验,结果表明,文中方法的分类精度最高,分类总误差最低仅为0.3%,较其他方法降低了0.91%~2.79%,证实了该方法在点云断裂带提取方面的优越性。

基于IMU-LiDAR紧耦合的煤矿防冲钻孔机器人定位导航方法

防冲钻孔机器人是冲击地压矿井卸压的关键设备,其在复杂卸压巷道的精确地图构建和的稳定导航是实现钻孔作业智能化的基础和前提。在分析激光雷达点云畸变成因和同步定位与地图构建(SLAM)算法缺陷的基础上,设计了基于惯性测量单元(IMU)连续时间轨迹的点云畸变矫正方法,建立了激光雷达和IMU的数据融合模型,提出了基于IMU-LiDAR紧耦合的防冲钻孔机器人定位建图方法。根据煤矿卸压巷道特点建立了密闭坡道模型,开展了建图效果仿真分析,结果表明,所提算法在定位精度、轨迹误差方面均优于现有常用算法。在此基础上,设计了基于改进人工势场法和快速扩展随机树的动态路径规划方法,建立了适用于防冲钻孔机器人的路径规划与导航融合方案,并设计了2种仿真运动场景,结果表明,所提路径规划方法在全局路径规划和动态路径规划的平均路径长度、平均运行时间、平均生成节点数等方面均具有较好的综合性能。为了进一步验证防冲钻孔机器人定位导航方法的实用性,在校内模拟巷道、地面实验基地和井下卸压巷道等场景下开展了多组对比实验,结果表明:将IMU数据与LiDAR数据紧耦合后,所提方法的定位建图精度明显提高,在特征退化场景中具有优越的定位建图性能,且规划路径的运算效率和路径代价方面均具有良好的表现,验证了所提定位导航方法在多种场景中的可行性和优越性。

一种DEM辅助下的LiDAR点云PTD滤波改进算法

针对传统渐进加密不规则三角网(PTD)滤波算法在复杂地形环境下需要反复调试地面点判断参数才能获得较好结果的局限性,以往期DEM数据提取的地形高程和地形梯度为辅助,改进PTD中初始地面种子点的选取方法,优化地面点判断参数,并对往期DEM数据和现势LiDAR点云数据之间的地形变化进行检测和处理,适用于不同坡度地形条件的复杂地形,滤波效果较好。对比分析实验数据精度可知,该算法能有效降低I类与II类误差,且样本分类精度均在90%以上,说明DEM辅助可切实提高PTD滤波算法的精度。

改进的密度聚类精确自适应提取LiDAR电力线点云方法

原有邻域半径r_(Eps)与密度阈值p_(MinPts)两个参数的初始赋值导致电力线点云的提取结果存在不确定性,在密度聚类的基础上增添了点云簇类自适应判别方法,该方法避免人员重复测试初始参数的繁琐过程,采用C++语言完成了对该算法电力线精确提取及电力线拟合程序的开发与测试。结果表明:改进后的密度聚类法在电力线点云提取的损失率仅0.02%,三维重建残差为0.213 m;该方法大幅提高了电力线点云提取的准确性与便捷性,适用于高压电力走廊的电力巡检与三维重建等工作。

机载LiDAR和模糊推理系统在黄土高原土壤侵蚀监测中的应用

黄土高原是我国乃至全球土壤侵蚀最严重的地区之一,受限于监测技术,黄土高原土壤侵蚀的研究多集中于径流小区尺度,大规模的侵蚀研究和野外观测仍相对缺乏。机载激光雷达(LiDAR)技术的革新为高精度、大规模土壤侵蚀研究提供了新的可能。然而,该技术受复杂地形的影响会产生高程不确定性误差,导致其监测土壤侵蚀的能力缺乏深入研究。基于此,本文以我国黄土高原丘陵沟壑区典型小流域桥沟流域为研究对象,联合机载Li-DAR测量和模糊推理系统(FIS),定量分析了两期DEM求差(DoD)不确定性的空间分布,研究小流域土壤侵蚀与沉积的空间分布特征。结果表明:(1)地形形态、点云密度等对插值生成的DEM误差影响较大,地形平坦区域的DoD不确定性明显小于地形陡峭区域的DoD不确定性;(2)FIS算法通过将已知的误差源整合到一个稳定的误差模型中,减少了主观干预和人为误差,避免了在复杂的DEM表面对误差进行错误估计,提高了计算结果的准确性;(3)梁峁坡地区侵蚀产沙体积为9.21m^(3),占坡沟系统产沙体积的15.89%;沟谷坡地区侵蚀严重,侵蚀产沙体积为48.76m^(3),占坡沟系统产沙体积的84.11%,在坡沟系统的产沙中起主导作用。沟床地区主要以沉积为主,梁峁坡和沟谷坡对侵蚀产沙贡献较大,而沟床则主要为产沙沉积区。研究成果不仅为黄土高原小流域的土壤侵蚀监测技术的拓展提供了新的视角,也为实施有效的水土保持措施提供了理论依据和参考。

基于车载LiDAR的交通标线自动提取方法研究

针对自动驾驶技术对高精度道路信息实时存储分析的需求日渐增大、道路点云数据冗余离散的问题,本文提出了一种从车载LiDAR点云数据中自动提取道路面、分类并矢量化交通标线的有效方法。首先,将点云数据中的非地面点滤除;其次,基于载体车辆的行车轨迹线生成伪扫描线实现道路面的提取;然后,构建一系列二维点云参考影像,利用点云强度等特征信息检测交通标线边界像素点及坐标,并去除离群值对交通标线进行分类细化;最后,对本文方法提取与传统方法提取的交通要素进行对比,实验结果表明,本文提取方法的准确度及效率都有了一定的提升。

无人机LiDAR点云与无人机影像匹配点云分析比较

随着无人机技术的不断发展,无人机数码航测技术和无人机LiDAR技术在测量领域的应用越来越广泛。为分析无人机LiDAR点云和无人机影像匹配点云2种点云的差异,该文通过对西南某铁路一个测区在同一飞行高度的情况下同时进行无人机数码航摄及无人机LiDAR航摄2种方式航摄。对2种不同的摄影方式获取的点云进行比较,分析出2种方法获取点云在形态表现、滤波分类,以及利用2种点云制作DEM高程精度方面的差异,为实际工程航飞方式的选择提供一个参考。

CNN-Transformer结合对比学习的高光谱与LiDAR数据协同分类

针对高光谱图像(hyperspectral images,HSI)与LiDAR数据多模态分类任务中的跨模态信息表达和特征对齐等问题,提出一种基于对比学习CNN-Transformer高光谱和LiDAR数据协同分类网络(Contrastive Learning based CNNTransformer Network,CLCT-Net)。CLCT-Net通过由ConvNeXt V2 Block构成的共有特征提取模块,获得不同模态间的共性特征,解决异构传感器数据之间语义对齐的问题。构建了包含空间-通道分支和光谱上下文分支的双分支HSI编码器,以及结合频域自注意力机制的LiDAR编码器,以获取更丰富的特征表示。利用集成对比学习进行分类,进一步提升多模态数据协同分类的精度。在Houston 2013和Trento数据集上的实验结果表明,相较于其他高光谱图像和Li‐DAR数据分类模型,本文所提模型获得了更高的地物分类精度,分别达到了92.01%和98.90%,实现了跨模态数据特征的深度挖掘和协同提取。

基于大数据流水线系统的算法模型整合方法研究——以基于机器学习方法的LiDAR数据树木生物量反演为例

【背景】激光雷达(LiDAR)数据在森林资源分析利用方面有着广泛应用,科研人员研制了很多涉及大数据管理和人工智能的专业算法模型,这些算法模型目前多数散落在研究人员手里,尚缺乏新型信息化平台对其进行整合。【方法】大数据流水线系统πFlow软件具有大数据管理能力和大数据算法集成能力,并可以所见即所得方式构建流水线并调度运行流水线,适合于LiDAR数据复杂算法模型的整合,且流水线可定制、可复用。【内容】本文介绍了πFlow的特点和功能,并以基于LiDAR冠层高度模型(CHM)数据的树冠解析及利用机器学习方法估测树木生物量为例,介绍了将算法整合到πFlow并构建LiDAR数据分析处理流水线的方法和技术,且对流水线进行了测试运行。【结果】利用πFlow构建的可重复信息化平台可支撑野外站观测网络的LiDAR数据生物量快速反演,为数据密集型的专业数据处理算法模型的整合提供了创新方法技术。

机载LiDAR点云数据的建筑屋顶面提取算法

针对机载LiDAR点云数据的屋顶面提取过程中因受植被影响导致提取精度低的问题,提出了一种基于区域生长的屋顶面点云提取算法。进行滤波处理得到非地面点云,利用屋顶面点云邻域特征信息提取屋顶面种子点,引入植被指数和RGB差值信息作为生长约束条件对屋顶面点云进行生长分割,利用屋顶面的高程与面积值对提取结果进行过滤优化,得到屋顶面点云。选取了农村、城市、工厂三组不同场景的测试数据进行实验,结果表明:Kappa系数分别达到了97.29%、97.82%、97.13%,算法可实现较好的建筑屋顶面提取效果,且针对不同建筑场景具有良好的适应性。

LiDar点云指导下特征分布趋同与语义关联的3D目标检测

针对现有基于伪点云的3D目标检测算法精度远低于基于真实激光雷达(Light Detection and ranging,LiDar)点云的3D目标检测,本文研究伪点云重构,并提出适合伪点云的3D目标检测网络.考虑到由图像深度转换得到的伪点云稠密且随深度增大逐渐稀疏,本文提出深度相关伪点云稀疏化方法,在减少后续计算量的同时保留中远距离更多的有效伪点云,实现伪点云重构.本文提出LiDar点云指导下特征分布趋同与语义关联的3D目标检测网络,在网络训练时引入LiDar点云分支来指导伪点云目标特征的生成,使生成的伪点云特征分布趋同于LiDar点云特征分布,从而降低数据源不一致造成的检测性能损失;针对RPN(Region Proposal Network)网络获取的3D候选框内的伪点云间语义关联不足的问题,设计注意力感知模块,在伪点云特征表示中通过注意力机制嵌入点间的语义关联关系,提升3D目标检测精度.在KITTI 3D目标检测数据集上的实验结果表明:现有的3D目标检测网络采用重构后的伪点云,检测精度提升了2.61%;提出的特征分布趋同与语义关联的3D目标检测网络,将基于伪点云的3D目标检测精度再提升0.57%,相比其他优秀的3D目标检测方法在检测精度上也有提升.

机载LiDAR系统安置角自适应迭代修正算法探讨

安置角参数在机载激光雷达作业中对后期点云数据精度质量有着极大的影响,使其真实地物点位置发生改变,相邻航带点云出现分层,而传统布设地面控制点的标定模式效率低、成本高、要求高、可操作性差,为规避上述诸多弊端,给出一种安置角自适应的标定模型,通过相邻重叠航带内部大量同名特征点的空间位置匹配、安置角参数的迭代运算,利用最小二乘配置方法求解机载激光雷达扫描系统的三个安置角参数,并借助C语言实现算法程序,在测试案例中得到了验证,提升了安置角标定效率,提高了机载激光雷达技术在复杂地形中点云的处理质量、适用性与可操作性。