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Pre-fire aboveground biomass, estimated from lidar, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems

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Forest Ecosystems 2022年第2期9卷 234-246页

作者： Jose Manuel Fernandez-Guisuraga Susana Suarez-Seoane Paulo MFernandes Victor Fernandez-Garcia Alfonso Fernandez-Manso Carmen Quintano Leonor Calvo Area of Ecology Department of Biodiversity and Environmental ManagementFaculty of Biological and Environmental SciencesUniversity of Leon24071LeonSpain Centro de Investigacao e de Tecnologias Agroambientais e Biologicas Universidade de Tras-os-Montes e Alto Douro5000-801Vila RealPortugal Department of Organisms and Systems Biology(Ecology Unit)and Research Unit of Biodiversity(IMIB UO-CSIC-PA)University of OviedoOviedoMieresSpain Agrarian Science and Engineering Department School of Agricultural and Forestry EngineeringUniversity of Leon24400PonferradaSpain Electronic Technology Department School of Industrial EngineeringUniversity of Valladolid47011ValladolidSpain Sustainable Forest Management Research Institute University of Valladolid-Spanish National Institute for Agriculture and Food Research and Technology(INIA)34004PalenciaSpain

Background:The characterization of surface and canopy fuel loadings in fire-prone pine ecosystems is critical for understanding fire behavior and anticipating the most harmful ecological effects of fire.Nevertheless,the joint consideration of both overstory and understory strata in burn severity assessments is often dismissed.The aim of this work was to assess the role of total,overstory and understory pre-fire aboveground biomass(AGB),estimated by means of airborne Light Detection and Ranging(lidar)and Landsat data,as drivers of burn severity in a megafire occurred in a pine ecosystem dominated by Pinus pinaster Ait.in the western Mediterranean Basin.Results:Total and overstory AGB were more accurately estimated(R^(2) equal to 0.72 and 0.68,respectively)from lidar and spectral data than understory AGB(R^(2)=0.26).Density and height percentile lidar metrics for several strata were found to be important predictors of AGB.Burn severity responded markedly and non-linearly to total(R^(2)=0.60)and overstory(R ^(2)=0.53)AGB,whereas the relationship with understory AGB was weaker(R ^(2)=0.21).Nevertheless,the overstory plus understory AGB contribution led to the highest ability to predict burn severity(RMSE=122.46 in dNBR scale),instead of the joint consideration as total AGB(RMSE=158.41).Conclusions:This study novelty evaluated the potential of pre-fire AGB,as a vegetation biophysical property derived from lidar,spectral and field plot inventory data,for predicting burn severity,separating the contri-bution of the fuel loads in the understory and overstory strata in Pinus pinaster stands.The evidenced relationships between burn severity and pre-fire AGB distribution in Pinus pinaster stands would allow the implementation of threshold criteria to support decision making in fuel treatments designed to minimize crown fire hazard.

关键词： Aboveground biomass Burn severity Landsat lidar Pinus pinaster

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Validation of Doppler Wind lidar during Super Typhoon Lekima (2019)

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Frontiers of Earth Science 2022年第1期16卷 75-89页

作者： Shengming TANG Yun GUO Xu WANG Jie TANG Tiantian LI Bingke ZHAO Shuai ZHANG Yongping LI Shanghai Typhoon Institute of China Meteorological Administration Shanghai 200030China Key Laboratory of Numerical Modeling for Tropical Cyclones China Meteorological AdministrationShanghai 200030China State Key Laboratory of Mountain Bridge and Tunnel Engineering Chongqing Jiaotong UniversityChongqing 400074China

This study undertook verification of the applicability and accuracy of wind data measured using a WindCube V2 Doppler Wind lidar(DWL).The data were collected as part of a field experiment in Zhoushan,Zhejiang Province(China),which was conducted by Shanghai Typhoon Institute of China Meteorological Administration during the passage of Super Typhoon Lekima(2019).The DWL measurements were compared with balloon-borne GPS radiosonde(GPS sonde)data,which were acquired using balloons launched from the DWL location.Results showed that wind speed measured by GPS sonde at heights of100 m)was found for DWL-measured wind speed time-averaged during the ascent of the GPS sonde from the ground surface to the height of 270 m(correlation coefficient:0.82;root mean square(RMS):2.19 m·h^(-1)).Analysis revealed that precipitation intensity(PI)exerts considerable influence on both the carrier-to-noise ratio and the rate of missing DWL data;however,PI has minimal effect on the wind speed bias of DWL measurements.Specifically,the rate of missing DWL data increased with increasing measurement height and PI.For PI classed as heavy rain or less(PI90 m·h^(-1)),only data below 100 m were valid.Up to the height of 300 m,the RMS of the DWL measurements was nearly half that of wind profile radar(WPR)estimates(4.32 m·s^(-1)),indicating that DWL wind data are more accurate than WPR data under typhoon conditions.

关键词： lidar WindCube GPS sonde Super Typhoon Lekima precipitation

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A monocular visual SLAM system augmented by lightweight deep local feature extractor using in-house and low-cost lidar-camera integrated device

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International Journal of Digital Earth 2022年第1期15卷 1929-1946页

作者： Jing Li Chenhui Shi Jun Chen Ruisheng Wang Zhiyuan Yang Fan Zhang Jianhua Gong National Engineering Research Center for Geoinformatics Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China National Geomatics Center of China BeijingPeople’s Republic of China Department of Geomatics Engineering University of CalgaryCalgaryCanada Department of Urban Studies and Planning Massachusetts Institute of TechnologyCambridgeMAUSA

Simultaneous Localization and Mapping(SLAM)has been widely used in emergency response,self-driving and city-scale 3D mapping and navigation.Recent deep-learning based feature point extractors have demonstrated superior performance in dealing with the complex environmental challenges(e.***.extreme lighting)while the traditional extractors are struggling.In this paper,we have successfully improved the robustness and accuracy of a monocular visual SLAM system under various complex scenes by adding a deep learning based visual localization thread as an augmentation to the visual SLAM framework.In this thread,our feature extractor with an efficient lightweight deep neural network is used for absolute pose and scale estimation in real time using the highly accurate georeferenced prior map database at 20cm geometric accuracy created by our in-house and low-cost lidar and camera integrated device.The closed-loop error provided by our SLAM system with and without this enhancement is 1.03m and 18.28m respectively.The scale estimation of the monocular visual SLAM is also significantly improved(0.01 versus 0.98).In addition,a novel camera-lidar calibration workflow is also provided for large-scale 3D mapping.This paper demonstrates the application and research potential of deep-learning based vision SLAM with image and lidar sensors.

关键词： Deep local features lightweight network visual localization SLAM lidar

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行道树靶标点云在线分割方法

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南京林业大学学报(自然科学版) 2024年

作者：严宇李秋洁南京林业大学机械电子工程学院

【目的】针对行道树对靶施药技术中的靶标实时在线分割需求，研究基于移动激光扫描(mobile laser scanning，MLS)的行道树靶标点云在线实时分割方法，建立能够实时在线准确分割行道树靶标点云的点云实例分割算法。【方法】本研究以一段3... 详细信息

【目的】针对行道树对靶施药技术中的靶标实时在线分割需求，研究基于移动激光扫描(mobile laser scanning，MLS)的行道树靶标点云在线实时分割方法，建立能够实时在线准确分割行道树靶标点云的点云实例分割算法。【方法】本研究以一段300 m长街道一侧的行道树为研究对象，通过建立FIFO(first input first output)缓冲区，每隔一段时间读取MLS采集到的三维街道点云数据中的若干帧街道点云数据。将读取过后FIFO缓冲区中的街道点云数据转换为三通道街道图像，使用图像实例分割模型对街道图像进行分割，得到行道树候选实例。然后，对行道树候选实例与已检测到的行道树实例进行实例融合，对已检测到的行道树实例进行完整性检测，对检测完整的行道树实例执行图像-点云映射，得到行道树点云实例。最后，使用阈值滤波与K近邻(k-nearest neighbor，KNN)两种方法在点云层面对行道树点云实例进行优化。【结果】在阈值滤波参数设置为0.65 m，KNN的半径参数设置为0.5 m时，行道树靶标点云实例分割结果最优，准确率为0.986 5，召回率为0.940 7，F1分数为0.957 6，平均每帧分割时间为5.261ms。【结论】本研究提出的行道树靶点云在线分割方法有效，满足行道树靶标实时在线分割的要求。

关键词：对靶喷雾行道树在线分割实例分割 K近邻 lidar

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Health assessment of plantations based on lidar canopy spatial structure parameters

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International Journal of Digital Earth 2022年第1期15卷 712-729页

作者： Pengyu Meng Hong Wang Shuhong Qin Xiuneng Li Zhenglin Song Yicong Wang Yi Yang Jay Gao School of Hydrology and Water resources Hohai UniversityNanjingPeople’s Republic of China School of Earth Science and Engineering Hohai UniversityNanjingPeople’s Republic of China School of Environment Faculty of ScienceUniversity of AucklandAucklandNew Zealand

The Yellow River Delta(YRD)has China's largest artificial Robinia pseudoacacia forest,which was planted in the late 1970s and suffered extensive dieback in the 1990s.The health grade of the R.pseudoacacia forest(named canopy vigor grade,CVG)could be achieved by using high-resolution images and canopy vigor indicators(CVIs).However,a previous study showed that there was no significant correlation between CVG and the field-estimated aboveground biomass(AGB)of R.pseudoacacia forest.Therefore,this study aims to construct forest health indicators(FHIs)based on canopy spatial structure parameters extracted from lidar.The FHIs included Weibull_α(the scale parameter of the Weibull density function that reflects the shape of the tree canopy),VCI(vertical complexity index),sdCC(the standard deviation of canopy cover),H99(the 99th percentile height)and cvLAD(the coefficient of variation of leaf area density),and could significantly distinguish three forest health grades(FHG)(p<0.05).The FHG was positively correlated with forest AGB(rs=0.51,p=0.004),and the similarity value with CVG was 63.33%.The results of this study confirmed that the FHIs can reflect both canopy vigor and tree productivity,and distinguish forest health status without prior classification information.

关键词： Forest health indicators lidar structural parameter Robinia pseudoacacia forest the Yellow River Delta

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Maize(Zea mays L.)seedling detection based on the fusion of a modified deep learning model and a novel lidar points projecting strategy

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International Journal of Agricultural and Biological Engineering 2022年第5期15卷 172-180页

作者： Gang Wang Dongyan Huang Deyi Zhou Huili Liu Minghao Qu Zhongyang Ma College of Biological and Agricultural Engineering Jilin UniversityChangchun 130022China Key Laboratory of Bionic Engineering Ministry of EducationJilin UniversityChangchun 130022China

Accurate crop detection is the prerequisite for the operation of intelligent agricultural machinery.Image recognition usually lacks accurate orientation information,and lidar point clouds are not easy to distinguish different objects.Fortunately,the fusion of images and lidar points can complement each other.This research aimed to detect maize(Zea mays L.)seedlings by fusing lidar data with images.By applying coordinate transformation and time stamps,the images and lidar points were realized homogeneous in spatial as well as temporal dimensions.Deep learning was used to develop a maize seedling recognition model,then the model recognized maize seedlings by labeling them with bounding boxes.Meanwhile,lidar points were mapped to the bounding boxes.Only one-third of points that fell into the right middle of bounding boxes were selected for clustering operation,the calculated center of the cluster provided spatial information for target maize seedlings.This study modified the classical single shot multi-box detector(SSD)by merely linking the last feature map to the final output layer,owing to the higher feature maps having the unique advantages of detecting relatively larger objects.In images,maize seedlings were just the largest objects owing to be shot on purpose.This modification enabled the recognition model to finish recognizing an image by only consuming around 60 ms,which saved about 10 ms/image compared with the classical SSD model.The experiment was conducted in a maize field,and the maize was during the elongation stage.Experimental results demonstrated that the standard deviations for maximum distance error and maximum angle error were 1.4 cm and 1.1°,respectively,which can be tolerated under current technical requirements.Since agricultural fields are subject to staple crop-orientated and changeable ambient environment,the fusion of images and lidar points can derive more precision information,and make agricultural machinery smarter.This study can act as an upstream technology for other researches on inte

关键词： maize seedling detection fusion deep learning lidar

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Extraction of straight field roads between farmlands based on agricultural vehicle-mounted lidar

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International Journal of Agricultural and Biological Engineering 2022年第5期15卷 155-162页

作者： Lili Yang Yuanyuan Xu Yajie Liang Jia Qin Yuanbo Li Xinxin Wang Weixin Zhai Long Wen Zhibo Chen Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural AffairsBeijing 100083China

The application of autonomous agricultural vehicles is gaining popularity as a way to increase production efficiency and lower operational costs.To achieve high performance,perception tasks(such as obstacle detection,road extraction,and drivable area extraction)are of great importance.Compared with structured roads,field roads between farmlands,including unstructured roads and semi-structured roads,are unfavorable for autonomous agricultural vehicle driving due to their bumpiness and unstructured nature.This study proposed an extraction method for the straight field roads between farmlands.The proposed method was based on the point cloud data acquired by lidar(Velodyne VLP-16)mounted on a John Deere 12046B-1204 tractor.The proposed method has three aspects:Euclidean Clustering-based extraction,boundary-based extraction,and road point cloud curve segment modification.Firstly,Euclidean Clustering with K-Dimensional(KD)-Tree data structure was adopted to extract the road curve segments close to the lidar composed of road points.Secondly,the boundary lines constraint was constructed to extract the distant road curve segments.Thirdly,the local distance ratio was used to modify the extracted road curve segments.The average extraction accuracy for both semi-structured and unstructured roads exceeded 98%,and the false positive rate(FPR)was less than 0.5%.These experimental findings demonstrated that the proposed road extraction method was precise and effective.The proposed method of this study can be applied to enhance the perception ability of autonomous agricultural vehicles thereby increasing the efficiency and safety of field road driving.

关键词： road extraction straight field road autonomous agricultural vehicle lidar farmland

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基于最小化各向同性误差的lidar-双目相机标定方法

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华南理工大学学报（自然科学版） 2023年第2期51卷 1-9页

作者：陈忠刘梓琛张宪民华南理工大学机械与汽车工程学院广东广州510640

lidar(Laser Imaging,Detection,and Ranging,激光雷达)与双目相机的多模态数据融合是3D重建领域的一个重要研究方向,两种传感器各有优缺点,通过多模态数据融合可以实现互补,获得更好的重建效果。为了实现数据融合,首先需要将两种数据... 详细信息

lidar(Laser Imaging,Detection,and Ranging,激光雷达)与双目相机的多模态数据融合是3D重建领域的一个重要研究方向,两种传感器各有优缺点,通过多模态数据融合可以实现互补,获得更好的重建效果。为了实现数据融合,首先需要将两种数据统一到同一个坐标系下,lidar与相机之间外参的高精度标定结果对后续的3D重建十分重要。在lidar与相机的外参标定过程中,受lidar点云的稀疏性与其定位误差的影响,提取精确的特征点并构建正确的点对应关系是一个挑战性的问题。此外,lidar在球坐标系下完成测量工作,而大多数标定方法忽略这一特性,直接使用笛卡尔坐标测量结果进行标定,引入了沿坐标轴各向异性分布的误差,导致标定精度下降。针对该问题,有研究者提出了各向异性权重方法,但仍存在一些缺陷。文中提出了一种最小化lidar球坐标误差的lidar与双目相机标定方法。首先,提出了一种使用形心特征点的新的标定板,改善特征点的提取精度;其次,将各向异性的lidar笛卡尔坐标误差转换为各向同性的球坐标误差,直接最小化球坐标误差求解外参。实验研究表明,本文提出的各向同性误差法相比各向异性权重方法保证求解结果为全局最优,且在牺牲部分精度的前提下所需标定样本数大幅减少,在最优标定误差为2.75 mm的情况下牺牲3.6%的精度能够减少约54.5%的数据量。

关键词： lidar 双目相机各向异性标定

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Massively parallel chaos for lidar

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Light(Science & Applications) 2023年第5期12卷 708-708页

作者： Shaohua Yu Peng Cheng Laboratory Shenzhen 518055China

Nature Photonics 17,306–314(2023)https://***/10.1038/s41566-023-01158-4 lidar is an essential sensor for Level 3+autonomous driving due to its unparalleled ability of accurate 3D mapping in real-time.However,the congestion problem,which occurs when their channel occupations overlap in time or frequency domain,has prevented the allowed channel number of individual lidar but also limits the density of working devices.This challenge,combined with the significantly increased cost when building the parallel lidar systems,has been stalling the progression of lidar technologies.

关键词： channel lidar chaos

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Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties

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Light(Science & Applications) 2022年第10期11卷 2345-2357页

作者： Yudi Zhou Yang Chen Hongkai Zhao Cedric Jamet Davide Dionisi Malik Chami Paolo Di Grolamo James H.Churnside Aleksey Malinka Huade Zhao Dajun Qiu Tingwei Cui Qun Liu Yatong Chen Sornsiri Phongphattarawat Nanchao Wang Sijie Chen Peng Chen Ziwei Yao Chengfeng Le Yuting Tao Peituo Xu Xiaobin Wang Binyu Wang Feitong Chen Chuang Ye Kai Zhang Chong Liu Dong Liu Ningbo Research Institute State Key Laboratory of Modern Optical InstrumentationCollege of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China Intelligent Optics&Photonics Research Center Jiaxing Key Laboratory of Photonic Sensing&Intelligent ImagingJiaxing Research InstituteZhejiang UniversityJiaxing 314000China Univ.Littoral C?te d'Opale CNRSUniv.LilleIRDUMR 8187-LOG-Laboratoire d'Océanologie et de GéosciencesF-62930 WimereuxFrance Institute of Marine Sciences(ISMAR) Italian National Research Council(CNR)Rome-Tor Vergata 00133Italy Sorbonne Université CNRSLATMOS96 Boulevard de l'Observatoire06304 Nice CedexFrance Scuola di Ingegneria Universitàdella BasilicataViale Ateneo Lucano 101-85100 PotenzaItaly Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder and NOAA Chemical Sciences Laboratory325 BroadwayBoulderCO 80305USA Institute of Physics National Academy of Sciences of BelarusPr.Nezavisimosti 68-2Minsk 220072Belarus Key Laboratory for Ecological Environment in Coastal Areas(State Oceanic Administration) National Marine Environmental Monitoring CenterDalian 116023China CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen UniversityZhuhai 519000China Faculty of Technology and Environment Prince of Songkla UniversityPhuket 83120Thailand Second Institute of Oceanography Ministry of Natural ResourcesHangzhou 310012China Ocean College Zhejiang UniversityZhoushan 316021China Donghai Laboratory Zhoushan 316021China

lidar techniques present a distinctive ability to resolve vertical structure of optical properties within the upper water column at both day-and night-time.However,accuracy challenges remain for existing lidar instruments due to the ill-posed nature of elastic backscatter lidar retrievals and multiple scattering.Here we demonstrate the high performance of,to the best of our knowledge,the first shipborne oceanic high-spectral-resolution lidar(HSRL)and illustrate a multiple scattering correction algorithm to rigorously address the above challenges in estimating the depth-resolved diffuse attenuation coefficient Kd and the particulate backscattering coefficient bbp at 532 nm.HSRL data were collected during day-and night-time within the coastal areas of East China Sea and South China Sea,which are connected by the Taiwan Strait.Results include vertical profiles from open ocean waters to moderate turbid waters and first lidar continuous observation of diel vertical distribution of thin layers at a fixed station.The root-mean-square relative differences between the HSRL and coincident in situ measurements are 5.6%and 9.1%for Kd and bbp,respectively,corresponding to an improvement of 2.7-13.5 and 4.9-44.1 times,respectively,with respect to elastic backscatter lidar methods.Shipborne oceanic HSRLs with high performance are expected to be of paramount importance for the construction of 3D map of ocean ecosystem.

关键词： lidar oceanic ocean

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