激光雷达森林参数反演研究进展

激光雷达通过发射激光能量和接收返回信号的方式,来获取高精度的森林空间结构和林下地形信息。全波形激光雷达通过记录返回信号的全部能量,得到亚米级植被垂直剖面;离散回波激光雷达记录的单个或多个回波,表示来自不同冠层的回波信号。星载激光雷达一般采用全波形或光子计数激光剖面系统,仅能获取卫星轨道下方的单波束或多波束数据,用于区域/全球范围的森林垂直结构及变化观测。机载激光雷达多采用离散回波或全波形激光扫描系统,能够获取飞行轨迹下方特定视场范围内的扫描数据,用于林分/区域范围的森林结构观测。地基激光雷达多采用离散回波激光扫描系统,获取以测站为中心的球形空间内扫描数据,用于单木/样地范围的森林结构观测。激光雷达单木因子估测方法可分为CHM单木法、NPC单木法和体元单木法3类。CHM单木法通过局部最大值识别树冠顶点,采用区域生长或图像分割算法识别树冠边界或树冠主方向,NPC单木法一般通过空间聚类或形态学算法识别单木,体元单木法在3维体元空间采用区域生长或空间聚类算法识别树冠。根据激光雷达冠层高度分布可以估测林分因子,冠层高度分布特征来自于离散点云或全波形。多时相激光雷达可用于森林生长量、生物量变化等监测,以及森林采伐、灾害等引起的结构变化监测。随着激光雷达技术的发展,它将在森林调查、生态环境建模等生产与科学研究领域中得到更为广泛的应用。     

基于可展开式太阳漫反射板的星上相对辐射定标

相对辐射定标的目的是降低或消除由于探测元件之间响应差异引起的图像高频竖向条纹或条带噪声,是遥感数据定量化应用的前提与基础。针对可展开式太阳漫反射星上定标装置,本文提出利用漫反射板收拢过程一次成像的星上相对辐射定标方法。通过卫星机械结构驱动漫反射板收拢调整传感器入瞳处太阳入射能量,获取覆盖传感器全部灰度动态范围的星上定标图像,采用直方图匹配算法解算相对辐射定标系数。为了验证本文提出的方法,利用在轨运行的吉林一号光谱星多光谱仪进行星上相对辐射定标实验,测试定标系数对原始图像的校正效果并定量分析相对辐射定标精度,实验结果表明定标系数能够有效消除全色与多光谱谱段地物原始图像的竖向高频条纹与条带噪声,定量分析的结果表明本文提出的方法在传感器灰度响应的全动态范围均能达到较好的校正效果,谱段相对辐射定标精度优于2%,为卫星在轨运行期间进行定量化遥感应用提供可靠保障。     

基于LiDAR波形分解的点云SVM分类方法研究

LiDAR全波形数据可以记录发射激光脉冲与地物作用形成的后向散射信号的全回波信息,是发射激光脉冲沿途遇到的所有目标回波信号的总和,揭示了地物的几何和物理属性,是地物分类的重要依据。然而目前基于全波形分解的地物分类研究较少。本文将LIDAR全波形数据分解成波宽、振幅、回波次数三个独立的属性,并分别将这三个属性与高程进行格网化,生成一幅含有四个图层的图像。然后使用SVM分类器对这幅图像进行分类,成功分出了房屋、地面、高大植被,分类精度96.2482%,kappa 0.9281。     

多光谱相机基于灰阶靶标的在轨绝对辐射定标

多光谱相机在轨绝对辐射定标是其遥感数据定量化应用的关键环节。高空间分辨率多光谱相机基于大面积灰阶靶标的在轨辐射定标, 以灰阶靶标BRDF、漫射/总辐射比和大气光学厚度等参数的地面测量为主, 通过目标反射辐射与大气程辐射、周围环境辐射的分离, 消除了对气溶胶散射的假设, 简化了定标流程, 突破了基于大面积均匀场定标受到的地理位置和天气状态等条件限制, 该方法有望实现高空间分辨率多光谱相机全动态范围内的高频次、高精度业务化在轨辐射定标。不确定度分析表明, 目前该辐射定标方法可实现4.7%的不确定度, 将来有望提高到3%—4%的水平。对天绘一号多光谱相机进行了基于大面积灰阶靶标的定标试验, 通过两次过顶时刻地面总辐照度变化的比较及靶标观测值的回归分析, 初步判断两次定标时间内多光谱相机波段3的性能发生了变化。     

单频机载激光测深海陆回波自动分类方法EI北大核心CSCD

海陆回波分类是机载激光测深中的一项波形预处理步骤,关系着后续信号检测和点云生成的精度。针对现有海陆回波分类方法不适用于单频机载激光测深系统且自动化程度不高的问题,本文提出一种单频机载激光测深海陆回波自动分类方法:首先,通过首末回波信号检测及点位计算获得回波的点云高程特征;然后,采用高程直方图拟合的方式确定平均水面位置,依据点云高程特征判定大部分回波的海陆属性,对余下的未定回波,仅保留其中的最强信号并统一处理为单信号回波,同时提取波形的信号特征和能量分布特征,依据点云高程特征的相似性自动建立训练样本集;最后,利用支持向量机分类器实现未定回波的分类。采用国产系统Mapper5000采集的实测数据进行试验,结果表明基于首末回波点云的初分类可快速、准确地对远离海陆交界处的回波进行分类,基于波形特征的未定回波分类可在自动建立的训练样本集支持下实现海陆交界处未定回波的高精度分类。与传统方法相比,本文方法无须近红外通道波形和人工样本的辅助就可以达到较高的分类精度,其中总体分类精度可达99.82%,海陆交界处分类精度可达91.59%。     

利用激光强度信息分类激光扫描测高数据

三维机载激光扫描测高数据中不仅含有每个激光脚点的位置和高程信息，而且越来越多的系统同时能提供激光脚点回波信号的强度信息。不同反射面介质对激光信号的反射特性不一样．用实测的数据对激光回波信号的强度信息进行统计标定，并基于标定结果．实现了联合激光强度信息和高程信息进行分类的算法．获得了较为满意的结果。     

全波形激光雷达的波形优化分解算法

随着数据存储能力和处理速度的提高,三维激光扫描系统逐渐具备全波形采集和分析技术。为了从全波形数据中获得脉冲时间、幅度、脉宽以及多回波分布等综合信息,波形分解成为了全波形激光雷达数据处理的关键技术之一。针对LM算法在一定程度上依赖初值,而传统激光雷达数据处理容易遗漏部分重叠的返回波,本文提出了一种改进回波分量初值设定的算法来获取回波脉冲的位置、宽度和强度。针对一套自主研发的全波形记录激光雷达演示系统进行了波形分解试验,定性和定量分析结果验证了该方法的有效性、可靠性和准确性。     

高光谱激光雷达提取植被生化组分垂直分布

对地高光谱激光雷达可以获得观测对象含有高光谱属性的全波形激光雷达回波,为探测植被生化特征的立体分布提供了新的遥感探测手段。基于此仪器开展室内试验,提出了植被生化组分垂直分布提取方法。首先,针对仪器的特点,提出了高光谱激光雷达全波形数据处理的方法;其次,以火炬花为例开展了室内扫描,并对获取的高光谱激光雷达数据进行了处理,获得带有高光谱属性的激光雷达点云数据;最后,根据植被指数与生化组分的关系,提取了叶绿素和胡萝卜素的生化组分垂直分布结果。研究结果表明,在植被顶部生化组分含量较低,叶绿素a普遍低于0.5 mg/g,胡萝卜素低于0.2 mg/g,而在中部叶片处,生化组分含量明显较高,与红色(顶部)和绿色叶片(中部)在植被垂直方向的分布一致,这表明基于仪器开展植被生理生化参数垂直分布遥感反演具有极大的应用潜力。     

机载LiDAR全波形数据分解的研究与应用

近年来,新兴机载全波形Li DAR技术在摄影测量与遥感领域受到广泛关注。其相比传统机载Li DAR的优势在于它对后向散射回波信息进行了完整的数字化记录。波形分解是波形数据处理的重要环节,通过波形分解不仅仅可以得到高质量的三维点云产品,还能够提高DEM的生成精度并辅助点云的精确分类,随着研究的不断深入,全波形数据将在更多领域发挥更大的实用效益。     

星载紫外遥感辐射计积分球定标新方法的研究

在紫外波段，使用常规手段实现高精度的辐射定标是比较困难的，这主要是由于标准灯的定标不确定度约4％和漫反射板双向反射分布函数（BRDF）的测量不确定度4％-6％所致。为了提高定标精度，本文应用积分球辐亮度定标方法，获得了接近理想的大面积辐亮度光源（约2％），标定了在研的星载紫外遥感光谱辐射计的亮度响应度。文中将标准灯考虑为均匀亮度的点光源，对辐射计的投影视场进行了照明因子修正。同时进行了常规的灯-板定标方法与积分球定标新方法的比较，得到了采用两种方法标定的辐射计亮度响应度有较好的一致性（3％）的结论。初步的定标数据分析比对显示，漫反射板BRDF的测量不确定度和仪器投影视场内辐亮度的计算不确定度是比对中不一致主要的来源。     

机载激光雷达全波形数据与系统点云差异的定量分析

目前常用的小光斑机载LiDAR波形数据与系统点云数据的来源相关性较大,波形数据的优势难以严格定量地评价和比较。LeicaALS60机载LiDAR系统记录的全波形数据与点云数据相对独立,点云数据来自硬件系统脉冲探测方法,而波形数据是未加处理的原始回波序列。本文对原始波形数据进行分解获取发射脉冲的全部回波,与系统探测点云进行了定量对比,并选取典型林区和城区数据,得到波形在两种地物类型中垂直信息获取能力的定量表征参数。结果表明,波形数据对不同地物类型均能丰富垂直结构信息和提高点云垂直分辨率,且这种提高在林区表现优于城区人工建筑和裸地;激光对树木冠层的穿透能力更明显地表现在回波波形信息中,相较于传统点云激光雷达,全波形LiDAR在森林垂直参数获取方面潜力更大。     

Waveform-based point cloud classification in land-cover identification

Full-waveform topographic LiDAR data provide more detailed information about objects along the path of a laser pulse than discrete-return (echo) topographic LiDAR data. Full-waveform topographic LiDAR data consist of a succession of cross-section profiles of landscapes and each waveform can be decomposed into a sum of echoes. The echo number reveals critical information in classifying land cover types. Most land covers contain one echo, whereas topographic LiDAR data in trees and roof edges contained multi-echo waveform features. To identify land-cover types, waveform-based classifier was integrated single-echo and multi-echo classifiers for point cloud classification.The experimental area was the Namasha district of Southern Taiwan, and the land-cover objects were categorized as roads, trees (canopy), grass (grass and crop), bare (bare ground), and buildings (buildings and roof edges). Waveform features were analyzed with respect to the single- and multi-echo laser-path samples, and the critical waveform features were selected according to the Bhattacharyya distance. Next, waveform-based classifiers were performed using support vector machine (SVM) with the local, spatial features of waveform topographic LiDAR information, and optical image information. Results showed that by using fused waveform and optical information, the waveform-based classifiers achieved the highest overall accuracy in identifying land-cover point clouds among the models, especially when compared to an echo-based classifier.     

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.     

Analysis of full-waveform LiDAR data for classification of an orange orchard scene

Full-waveform laser scanning data acquired with a Riegl LMS-Q560 instrument were used to classify an orange orchard into orange trees, grass and ground using waveform parameters alone. Gaussian decomposition was performed on this data capture from the National Airborne Field Experiment in November 2006 using a custom peak-detection procedure and a trust-region-reflective algorithm for fitting Gauss functions. Calibration was carried out using waveforms returned from a road surface, and the backscattering coefficient γ was derived for every waveform peak. The processed data were then analysed according to the number of returns detected within each waveform and classified into three classes based on pulse width and γ. For single-peak waveforms the scatterplot of γ versus pulse width was used to distinguish between ground, grass and orange trees. In the case of multiple returns, the relationship between first (or first plus middle) and last return γ values was used to separate ground from other targets. Refinement of this classification, and further sub-classification into grass and orange trees was performed using the γ versus pulse width scatterplots of last returns. In all cases the separation was carried out using a decision tree with empirical relationships between the waveform parameters. Ground points were successfully separated from orange tree points. The most difficult class to separate and verify was grass, but those points in general corresponded well with the grass areas identified in the aerial photography. The overall accuracy reached 91%, using photography and relative elevation as ground truth. The overall accuracy for two classes, orange tree and combined class of grass and ground, yielded 95%. Finally, the backscattering coefficient γ of single-peak waveforms was also used to derive reflectance values of the three classes. The reflectance of the orange tree class (0.31) and ground class (0.60) are consistent with published values at the wavelength of the Riegl scanner (1550 nm). The grass class reflectance (0.46) falls in between the other two classes as might be expected, as this class has a mixture of the contributions of both vegetation and ground reflectance properties.     

全波形LiDAR数据分解的可变分量高斯混合模型及RJMCMC算法

传统激光雷达(light detection and ranging,LiDAR)数据处理均采用固定数的波形分解方法,容易遗漏部分重叠的返回波,降低波形拟合精度。为了实现可变数波形分解,本文提出了一种自动确定波形分解数的方法。假定波形数据服从混合高斯分布,并以此建立理想的波形模型;定义用于控制理想模型与实际波形拟合程度的能量函数,用吉布斯分布构建或然率;根据贝叶斯定理构建刻画波形分解的后验概率模型;设计可逆跳转马尔科夫链蒙特卡洛(reversible jump Markov chain Monte Carlo,RJMCMC)算法模拟该后验概率模型,以确定波形分解数并同时完成波形分解。为了验证提出算法的正确性,分别对不同区域的ICESat-GLAS波形数据进行了波形分解试验,定性和定量分析结果验证了本文方法的有效性、可靠性和准确性。     

Mapping the understorey of deciduous woodland from leaf-on and leaf-off airborne LiDAR data: A case study in lowland Britain

This study examines the understorey information present in discrete-return LiDAR (Light Detection And Ranging) data acquired for temperate deciduous woodland in mid summer (leaf-on) and in early spring when the understorey had mostly leafed out, but the overstorey had only just begun budburst (referred to here as leaf-off). The woodland is ancient, semi-natural broadleaf and has a heterogeneous structure with a mostly closed canopy overstorey and a patchy understorey layer. In this study, the understorey was defined as suppressed trees and shrubs growing beneath an overstorey canopy. Forest mensuration data for the study site were examined to identify thresholds (taking the 95th percentile) for crown depth as a percentage of crown top height for the six overstorey tree species present. These data were used in association with a digital tree species map and leaf-on first return LiDAR data, to identify the possible depth of space available below the overstorey canopy in which an understorey layer could exist. The leaf-off last return LiDAR data were then examined to identify whether they contained information on where this space was occupied by suppressed trees or shrubs forming an understorey. Thus, understorey was mapped from the leaf-off last return data where the height was below the predicted crown depth. A height threshold of 1 m was applied to separate the ground vegetation layer from the understorey. The derived understorey model formed a discontinuous layer covering 46.4 ha (or 31% of the study site), with an average height of 2.64 m and a 77% correspondence with field data on the presence/absence of suppressed trees and shrubs (kappa 0.53). Because the first return data in leaf-on and leaf-off conditions were very similar (differing by an average of just 0.87 m), it was also possible to map the understorey layer using leaf-off data alone. The resultant understorey model covered 39.4 ha (or 26% of the study site), and had a 72% correspondence with field data on the presence/absence of suppressed trees and shrubs (kappa 0.45). This moderate reduction in the area of understorey mapped and associated accuracy came with a saving of half of all data acquisition and pre-processing costs. Whilst the understorey modelling presented here undoubtedly benefited from the specific timing of LiDAR data acquisition and from ancillary data available for the study site, the conclusions have resonance beyond this case study. Given that the understorey and overstorey canopies in lowland broadleaf woodland can merge into one another, the modelling of understorey information from discrete-return LiDAR data must consider overstorey canopy characteristics and laser penetration through the overstorey. It is not adequate in such circumstances to apply simple height thresholds to LiDAR height frequency distributions, as this is unlikely to distinguish whether a return has backscattered from the lower parts of the overstorey canopy or from near the surface of the understorey canopy.     

机载激光雷达平均树高提取研究

为了研究机载激光雷达(LiDAR)树高提取技术,以山东省泰安市徂徕山林场为实验区,于2005年5月进行了机载LiDAR数据获取和外业测量.通过对LiDAR点云数据的分类处理,分别得到了试验区的地面点云子集、植被点云子集和高程归一化的植被点云子集.基于高程归一化的植被点云子集计算了上四分位数处的高度,与实地测量的数据进行了比较,并结合中国森林调查规程进行了实用性分析.结果表明:对于较低密度的点云数据,使用分位数法可以较好地进行林分平均高的估计;机载激光雷达技术对树高估计是可行的,精度都高于87%,总体平均精度为90.59%,其中阔叶树的精度高于针叶树.该试验精度可以满足中国二类森林调查规程中平均树高因子的一般商品林和生态公益林的精度要求,对国有商品林小班的调查精度要求(5%)存在一点差距,需要在国有商品林区进一步开展验证工作.对本试验区而言,已经可以满足其作为森林公园生态公益林的调查要求.     

资源三号02星对地激光测高系统几何检校及验证

星载激光测高仪安装误差、激光指向和激光测距误差等导致最终激光测高精度不高,对激光器进行在轨几何检校可以有效提升激光测高精度。针对资源三号02星（ZY3-02）激光测高仪的工作模式,以裸露地表的航天飞机雷达地形测绘任务（shuttle radar topography mission,SRTM）数字高程模型（digital elevation model,DEM）数据约束同轨激光测距值,通过逼近地形起伏趋势线实现了卫星激光器出射方向的初始检校,实验证明不同轨激光指向的相对检校精度在20 m以内。利用地面铺设激光靶标的方法对星载激光测高系统进行几何精检校,并通过外业测量验证了ZY3-02激光器在平坦区域的测高精度优于0.5 m。     

激光雷达回波模型辅助的坡地森林冠层高度反演

大光斑激光雷达数据已广泛应用于森林冠层高度提取,但通常仅限于地形坡度小于20°的平缓地区。在地形坡度大于20°的陡峭山区,地形引起的波形展宽使得地面回波和植被回波信息混合在一起,给森林冠层高度提取带来巨大挑战。本文利用激光雷达回波模型和地形信息,提出了一种模型辅助的坡地森林冠层高度反演算法。该方法以激光雷达回波信号截止点为参考,定义了波形高度指数H50和H75,使用激光雷达回波模型与已知地形信息模拟裸地的激光雷达回波,将裸地回波信号截止点与森林激光雷达回波信号截止点对齐,利用裸地回波计算常用的波形相对高度指数RH50和RH75,对森林冠层高度进行反演。并与高斯波形分解法和波形参数法的反演结果进行了比较。研究结果表明:(1)利用所提取的波形指数RH50和RH75对胸高断面积加权平均高(Lorey’s height)进行了估算,在坡度小于20°时,高斯波形分解法、波形参数法和模型辅助法的估算结果与实测值线性拟合的相关系数(R2)分别为0.70,0.78和0.98,对应的均方根误差(RMSE)分别为2.90 m,2.48 m和0.60 m,模型辅助法略优于其他两种方法;(2)在坡度大于20°时,高斯波形分解法、波形参数法和模型辅助法的R2分别为0.14,0.28和0.97,相应的RMSE分别为4.93 m,4.53 m和0.81 m,模型辅助法明显优于其他两种方法;(3)在0°—40°时,模型辅助法对Lorey’s height估算结果与实测值的R2为0.97,RMSE为0.80 m。本研究提出的模型辅助法具有更好的地形适应性,在0°—40°的坡度范围内具备对坡地森林冠层高度反演的潜力。