基于激光诱导叶绿素荧光寿命成像技术的植物荧光特性研究

针对植物荧光遥感探测中信号易受干扰的问题, 提出了一种用于评估植物生长状况及环境监测的荧光寿命成像技术. 采用凹透镜对355 nm波长的激光扩束, 再照射植物激发叶绿素荧光, 由增强型电荷耦合器件接收荧光信号. 采用时间分辨测量法, 连续用相同激光脉冲照射植物以激发相同的荧光信号, 同时不断改变激光脉冲触发探测器启动的延时时间, 从而能够得到完整的离散荧光信号分布图像. 对植物特定位置点产生的离散荧光信号进行拟合, 再运用一种改进型的迭代解卷积法可反演高精度的荧光寿命; 进而反演图像各点的荧光寿命以生成植物的荧光寿命分布图. 该方法所绘制的荧光寿命图比荧光强度图能更准确地反映植物内部的叶绿素含量, 并对活体植物叶绿素荧光寿命的物理特性进行了初步研究, 证明叶绿素荧光寿命与植物生理状态存在一定关联; 并且叶绿素荧光寿命与活体植物所处环境存在着复杂的关系. 未来将与生物物理学家们合作, 继续探寻叶绿素荧光寿命与植物生存环境的关系.     

利用LiDAR系统对飞机残骸进行分析的新方法

飞机失事的事故率较低,但损失一般较为严重.对飞机残骸现场的有效记录,能够帮助我们了解失事场景,并进行后续分析,但国内外相关研究较少.本文利用一套LiDAR系统对事故现场进行测绘和三维尺度图像信息保存.并结合改进的ICP算法实现了更高精度的点云拼接,从而对失事前后的同一部件进行对比分析,以确定其受力和变形情况,进而帮助确定失事原因.与其他飞机失事场景记录方法相比,该系统三维重现效果更好.     

激光扫描匹配方法研究综述

激光扫描匹配是利用激光雷达进行导航、定位与地图构建的基础,本文对各类激光扫描匹配方法进行了综述。将现有方法归纳为基于点的扫描匹配方法、基于特征的扫描匹配方法和基于数学特性的扫描匹配方法 3类,系统总结了相应类型的常见方法;对典型的算法及其改进算法进行了梳理,并指出了存在的主要问题和发展趋势;介绍了激光扫描匹配方法性能评价和对比的最新研究进展,最后,展望了激光扫描匹配技术未来的研究方向。     

基于移动激光扫描的橡胶林风害相关参数精准反演

林木参数反演是森林资源管理与培育经营的关键环节。迅速发展的激光探测与测量技术突破了传统测量方法,可以快速的获取林木的空间三维信息,在林业普查中具备高效率、高精度的优势。结合计算机图形学与图像学方法,以中国最大的橡胶生产基地海南省儋州市长期受台风侵害下的两个不同品种橡胶林段(林段1 PR107,林段2 CATAS7-20-59)为研究对象,设计了面向离散激光点云的单株林木参数提取方法,自动获取橡胶林木风害后的生物量指标。首先,通过人背负移动激光雷达获取林段点云数据并使用瑞利商求取台风造成的主枝干倾角,以找寻每株橡胶树的树冠中心点。其次,对点云进行垂直投影,并采用分水岭与Meanshift算法实现株株分离。最后,基于以上操作自动获取与实测值相近的林木参数,例如冠幅、冠径、冠积、叶面积密度、叶面积分布以及主枝与分枝之间夹角等。计算表明,林段1与林段2东西冠幅分别为3.95和3.73 m,与实测相差1.74%～6.27%;林段1与林段2南北向冠幅分别为6.47和6.51 m,与实测相差2.54%～4.02%;林段1与林段2平均胸径分别为5.20和4.73 cm,与实测相差0.64%～2.44%;林段1与林段2平均冠积分别为168.01和141.80 m3,与实测相差0.67%～0.85%;林段1与林段2主枝干倾斜角分别为18.80°和13.11°,与实测相差5.53%～7.09%;林段1与林段2二级分支与主枝干的夹角分别为40.21°~69.23°和10.63°~32.14°,它们相差62.63%;林段1在不同天顶角下的叶面积指数均大于林段2。通过对一定样本（150棵/每类林段）的分析结果与真实比对表明,该方法对林木参数反演结果精度较高,有效地评估了不同品种橡胶树在台风下的损伤度（如主枝干歪斜率、叶面积密度及衰减分布）。参数反演结果与实测值仅有8%的偏差,此偏差主要由橡胶林分叶片稠密,导致林分中叶面与枝干扫描数据获取缺失,以及外界环境干扰如风力扰动、点云拼接误差、激光束发散率、激光扫描范围等原因造成。同时,由于林段1(PR107)的橡胶树的主枝与分枝夹角、冠积以及叶面积指数均大于林段2(CATAS7-20-59)的橡胶树,导致林段1的橡胶树比林段的2橡胶树在台风侵害下更脆弱。因此,该研究可用于研究风力侵害对于不同森林地块的影响,以及量化风害造成的生态系统紊乱的影响。同时,该方法解决了人背负激光扫描数据进行单株提取与林木参数反演的问题,为激光测绘在林业中的应用提供了新思路。     

An entropy-based filtering approach for airborne laser scanning data

Parameter-tuning is a challenging task when generating digital terrain models from airborne laser scanning (light detection and ranging, LiDAR) data. To address this issue, this paper presents a filtering method for near-infrared laser scanning data that exploits the principle of entropy maximization as the optimization objective. The proposed approach generates ground elevation of point cloud by constructing a triangulated irregular network, calculates the entropy of the elevation from different parts, and automatically separates ground and non-ground points by the principle of entropy maximization. Experimental results from different ground surfaces show that the proposed entropy-based filtering method can effectively extract bare-earth points from the point cloud without adjusting thresholds.     

海南岛沿岸海域水体漫衰减系数光谱分析及LiDAR测深能力估算

机载激光雷达测深是近年来蓬勃发展的主动式水深测量方法,能够快速精准地获取近岸水深和水下地形,特别是对于浅海、岛礁等船只无法达到的区域具有显著优势。而机载激光雷达系统的测深能力主要受到水体浑浊度的影响。激光测深实验中对实验区域的水体浑浊度研究将有助于实验方案的设计。以中国海南岛沿岸海域为例,研究了该海域水体浑浊度和机载激光雷达测深系统CZMIL(coastal zone mapping and imaging LiDAR)测深能力之间的关系,建立了运用水体漫衰减系数估算机载激光雷达测深系统测深能力的算法。首先分析并确定了实验区域的漫衰减系数K_d(490)反演算法;其次,运用该区域实测光学数据建立了漫衰减系数K_d(490)和K_d(532)之间的数值关系;接着总结了K_d(532)和CZMIL系统最大测深值之间的关系;最后运用MODIS数据合成了海南岛沿岸海域在CZMIL系统海道测量模式下的测深能力空间分布图,重点分析了海口和陵水附近海域的最大可测水深分布情况。为海南岛沿岸海域开展激光测深实验提供了参考和依据。     

激光诱导植物荧光寿命校正技术及其特性分析

激光诱导植物荧光寿命测量法是在植物荧光光谱分析法基础上开发的一种评估植物生长状况及环境监测的新技术。根据植物叶绿素荧光信号的物理特性,利用信息仿真技术开发了一种叶绿素荧光寿命校正方法,可提高植物叶绿素荧光寿命的测量精度。利用激光诱导叶绿素荧光寿命测量系统分别测得叶绿素荧光及其背景信号,先用解卷积法叶绿素荧光信号中分离出荧光衰减函数,可获取荧光寿命估计值。再结合叶绿素荧光寿命校正技术就能反演得到高精度的植物荧光寿命。仿真与实验结果表明：该方法可实现高精度的植物荧光寿命实时监测;并对不同含量的叶绿素提取液进行了测试,构建了植物荧光寿命与叶绿素含量的对应关系模型。未来该技术可用于遥感监测海洋、湖泊、河流中藻类植物的生物含量。     

Detection of gullies in Fort Riley military installation using LiDAR derived high resolution DEM

Intensive use of military vehicles in military installations create conditions favorable for gully formation. Gullies impede the access of vehicle, restrict the continuation of training, and lead to significant damage to vehicle and risk the life of soldiers. Therefore, it is critical to correctly identify the locations of gullies for continuous training mission. In this study, Fort Riley (FR) military installation was chosen as the study area. LiDAR derived 1 m resolution digital elevation model (DEM) acquired on 2010 was used to map the gullies. A procedure that measures local topographic position, i.e., difference from mean elevation (DFME) along with its integration to the land surface having high surface curvature values was employed. Two high spatial resolution WorldView-2 images of 2010 and field gully data collected in 2010 were utilized for accuracy assessment. Results showed that: (1) A total of 237 small and 166 large gullies were detected and most of them dominated the central west and northwest parts of the installation; (2) Based on the visual interpretation in the WorldView-2 images, there was no statistically significant difference between the detected and observed numbers of gullies; (3) Gullies measured in the field were well detected with an overall accuracy of 78%.     

Numerical simulation and inversion validation of the real correlation random wind field generation for coherent Doppler LiDAR

Simulation of the coherent Doppler LiDAR signal requires accurate computation of homogeneous random wind fields. Based on complex random processes with specified spatial statistics given by the covariance function, an improved real correlation random wind field algorithm is proposed for real random processes, the simulation results are compared with the given covariance function and the real correlation algorithm conforms to the given covariance function quite well.     

Research on classification of LiDAR images derived from waveform decomposition over a suburban area

Light detection and ranging (LiDAR), as an active remote sensing technology, is characterized by providing high-precision geographical location information. In this study, we further explored its capability in image classification over a suburban area. Firstly, full waveforms of small footprint airborne LiDAR were decomposed into discrete point clouds. During the decomposition, six parameters describing the physical interaction between laser pulse and the targets were calculated. They are amplitude, pulse width, central position, range, backscatter cross-section and backscatter coefficient. Secondly, the point clouds were interpolated into raster. Correspondingly, six high spatial resolution images (0.5 m) were produced. Three classification models namely decision tree (DT), maximum likelihood (ML) and support vector machine (SVM) were established based on these images. The objects of interest were classified into buildings, trees, bare soil and crop land. Results showed that all these three models yielded high overall accuracy and kappa coefficient. SVM performed the best with the highest overall accuracy (87.85%) and kappa coefficient (83.29%). Therefore, we came to conclude that classification models can also achieve satisfactory classification accuracy on LiDAR images as they did on common remote-sensed images. In addition, our study proved that physical information derived from waveform LiDAR showed good potential in classification.