机载LiDAR生成亚热带森林林下DTM的地面误差分析

目前LiDAR技术已经成为DTM的主要生产方法。地面误差对LiDAR生成DTM的精度影响比较明显,特别是由于亚热带森林植被覆盖区LiDAR激光点云少,生成的DTM更复杂,需要分析地面误差对LiDAR生成林下DTM的精度影响。本文以华南农业大学增城教学科研基地为研究对象,从森林郁闭度和坡度两个方面探讨了地面误差对机载LiDAR数据生成林下DTM精度的影响。研究结果发现高程误差会随郁闭度的增大而增大;而随坡度变化趋势不明显,但是坡度为15°时成为误差的分水岭,其前后误差差异比较明显。总体而言,郁闭度的影响更为明显。     

多源遥感数据支持下的区域性森林冠层高度估测

结合机载LiDAR数据,提出了一种改进的GLAS光斑点冠层高度地形校正模型,以校正后的GLAS光斑点作为输入样本,结合MODIS遥感影像,利用支持向量回归（SVR）的方法对研究区森林冠层高度进行分生态区估测,并利用野外调查数据和机载LiDAR冠层高度结果对估测结果进行验证。结果显示:研究区的坡度等级直接影响GLAS光斑点森林冠层高度估测精度,改进的地形校正模型可以较好的减小坡度对GLAS光斑点森林冠层高度估测的影响,模型精度RMSE稳定在3.25~3.48 m;不同生态分区的SVR模型估测精度较为稳定,其RMSE=6.41~7.56 m;与算数平均高相比,样地的Lorey's高与制图结果拟合最好,不同生态分区平均估测精度为80.3%。机载LiDAR冠层高度结果的验证平均精度为79.5%,和Lorey's高验证结果呈现较好的一致性。     

Airborne LiDAR point cloud in mapping of fluvial forms: a case study of a Hungarian floodplain

The aim of this paper was to analyze the ground and low vegetation points of a Light Detection and Ranging (LiDAR) point cloud from the aspect of the generated digital terrain model (DTM). We determined the height difference between the surveyed surface and the DTM and the level of interspersion of ground and low vegetation points in a floodplain. Finally, we performed a supervised classification with topographic (elevation, slope and aspect) variables and an Normalized Difference Vegetation Index (NDVI) layer to identify swales and point bars as floodplain forms. Cross sections of field surveys provided reference data to express the magnitude of the bias on the DTM caused by the vegetation, and we proved that the bias can reach the 60% of the relative height and depth of the floodplain forms (mean error was 0.15 ± 0.12 m). A landscape metric, the Aggregation Index, provided an appropriate tool to analyze and quantify the interspersion of the ground and vegetation points: indicating a high level of interspersion of the classified points, i.e. proved that vegetation points where the last echoes reflected from the vegetation became ground points. Floodplain classification performed best with the common use of DTM, slope, aspect and NDVI coverages, with 71% overall accuracy.     

A novel approach to estimate canopy height using ICESat/GLAS data: A case study in the New Forest National Park,UK

The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) is a spaceborne LiDAR sensor. It is the first LiDAR instrument which can digitize the backscattered waveform and offer near global coverage. Among others, scientific objectives of the mission include precise measurement of vegetation canopy heights. Existing approaches of waveform processing for canopy height estimation suggest Gaussian decomposition of the waveform which has the limitation to properly characterize significant peaks and results in discrepant information. Moreover, in most cases, Digital Terrain Models (DTMs) are required for canopy height estimation. This paper presents a new automated method of GLAS waveform processing for extracting vegetation canopy height in the absence of a DTM. Canopy heights retrieved from GLAS waveforms were validated with field measured heights. The newly proposed method was able to explain 79% of variation in canopy heights with an RMSE of 3.18 m, in the study area. The unexplained variation in canopy heights retrieved from GLAS data can be due to errors introduced by footprint eccentricity, decay of energy between emitted and received signals, uncertainty in the field measurements and limited number of sampled footprints.Results achieved with the newly proposed method were encouraging and demonstrated its potential of processing full-waveform LiDAR data for estimating forest canopy height. The study also had implications on future full-waveform spaceborne missions and their utility in vegetation studies.     

机载LiDAR点云密度对DEM精度的影响

机载激光雷达技术(LiDAR)作为一项先进的遥感技术,是植被覆盖区DEM获取的重要手段之一,而不同地形坡度条件及点云密度对DEM产品质量有重要影响。本文以辽宁省某市的机载LiDAR数据为基础,选取5种不同地形坡度的点云数据,通过随机、等间距及基于曲率3种不同的点云抽稀方法,按照点云保留率为80%、60%、40%、20%和10%共5个不同梯度的抽稀倍数对原始点云进行抽稀简化处理,生成与之对应的DEM并对其进行精度评价,以此研究地形坡度、点云抽稀方法、抽稀倍数对DEM精度的影响。结果表明,DEM精度与地形坡度呈负相关关系,即RMSE随地形坡度升高不断增加;基于曲率的抽稀方法在地形坡度＞30°时,相较于其他两种方法RMSE较小,具有明显优势;40%的点云保留率是平衡DEM精度与数据存储效率的一个节点,当点云保留率<40%时,DEM的高程RMSE会迅速增大。该研究对于利用机载LiDAR进行大范围DEM生产具有一定的指导和借鉴意义。     

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.     

基于坡度自适应的机载LiDAR分割算法

本文提出了一种适用于离散LiDAR数据的区域生长算法:将离散点云数据重采样为规则格网,通过坡度自适应区域生长法分割规则格网,获得不同的面片;建立各个分割面片之间的拓扑关系,将分割面片划分为粗差、植被、建筑物和地面;检测原始激光脚点到DTM的距离,判断是否为地面点。文中采用ISPRS提供的测试数据验证了算法分割的有效性。     

基于遥感的区域尺度森林地上生物量估算研究

森林是陆地生态系统最大的碳库,精确估算森林生物量是陆地碳循环研究的关键。首先从机载LiDAR数据中提取高度和密度统计量,采用逐步回归模型进行典型样区生物量估算;然后利用机载LiDAR数据估算的生物量作为样本数据,与多光谱遥感数据Landsat8 OLI的波段反射率及植被指数建立回归模型,实现区域尺度森林地上生物量估算。实验结果显示,机载LiDAR数据估算的鼎湖山样区生物量与地面实测生物量的相关性R2达0.81,生物量RMSE为40.85 t/ha,说明机载LiDAR点云数据的高度和密度统计量与生物量存在较高的相关性。以机载LiDAR数据估算的生物量为样本数据,结合多光谱遥感数据Landsat8 OLI估算粤西北地区的森林地上生物量,精度验证结果为:R2为0.58,RMSE为36.9 t/ha;针叶林、阔叶林和针阔叶混交林等3种不同森林类型生物量的估算结果为:R2分别为0.51(n=251)、0.58(n=235)和0.56(n=241),生物量RMSE分别为24.1 t/ha、31.3 t/ha和29.9 t/ha,估算精度相差不大。总体上看,利用遥感数据可以开展区域尺度的森林地上生物量估算,为森林固碳监测提供有力的参考数据。     

面向对象的植被与建筑物重叠区域的点云分类方法

在分析LiDAR点云数据分类现状的基础上,针对植被与建筑物重叠区域分类困难的问题,提出了一种基于面向对象的点云分类方法.首先采用三角网渐进内插的滤波方法将点云分为地面点和非地面点,并得到DTM;然后对高出DTM一定高度的非地面点建立三角网,删除较长的三角网的边(地物间的边),从而将非地面点云分割成多个对象;再利用各个对象内的三角网坡度信息熵大小判断该对象属于植被或建筑物;最后对于难以区分的对象(植被与建筑物重叠区)根据建筑物几何规则形状延伸扩充,从而提高植被和建筑物重叠区的点云分类准确率.实验结果表明,该方法能够很好地区分建筑物和植被点,分类准确率达到87％.     

ICESat-2机载试验点云滤波及植被高度反演

新一代星载激光雷达卫星ICESat-2将采用多波束微脉冲光子计数技术,并进行高程剖面式的对地观测。由于该点云数据具有背景噪声大、密度低并呈线状分布等特点,传统的点云滤波算法并不适用,研究新的点云滤波算法十分必要。本文以ICESat-2的机载模拟器MABEL数据为例,首先介绍了微脉冲光子计数激光雷达的基本原理和数据特点,并针对高程剖面点云提出基于局部距离统计和最小二乘局部曲线拟合的点云滤波算法;然后,对美国加利福尼亚州Sierras-Forest地区MABEL试验中532 nm通道的光子点云进行滤波处理,并利用识别的地面点插值得到3 m分辨率的线状DEM,进而估算了该区域美国云杉的平均树高;最后,对该滤波算法进行精度评价,并分析了误差来源及其对DEM精度和树高反演精度的影响。结果表明:(1)该算法整体精度达97.6%,能有效剔除绝大部分噪声点且对地形起伏具有较强的自适应能力;(2)误分噪声点影响了滤波过程中局部地形的拟合,而滤波过程中的分类误差将降低DEM和树高反演的精度。     

Characterization of the horizontal structure of the tropical forest canopy using object-based LiDAR and multispectral image analysis

This article's goal is to explore the benefits of using Digital Surface Model (DSM) and Digital Terrain Model (DTM) derived from LiDAR acquisitions for characterizing the horizontal structure of different facies in forested areas (primary forests vs. secondary forests) within the framework of an object-oriented classification. The area under study is the island of Mayotte in the western Indian Ocean. The LiDAR data were the data originally acquired by an airborne small-footprint discrete-return LiDAR for the “Litto3D” coastline mapping project. They were used to create a Digital Elevation Model (DEM) at a spatial resolution of 1 m and a Digital Canopy Model (DCM) using median filtering. The use of two successive segmentations at different scales allowed us to adjust the segmentation parameters to the local structure of the landscape and of the cover. Working in object-oriented mode with LiDAR allowed us to discriminate six vegetation classes based on canopy height and horizontal heterogeneity. This heterogeneity was assessed using a texture index calculated from the height-transition co-occurrence matrix. Overall accuracy exceeds 90%. The resulting product is the first vegetation map of Mayotte which emphasizes the structure over the composition.     

Combining QuickBird,LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach

There are now a wide range of techniques that can be combined for image analysis. These include the use of object-based classifications rather than pixel-based classifiers, the use of LiDAR to determine vegetation height and vertical structure, as well terrain variables such as topographic wetness index and slope that can be calculated using GIS. This research investigates the benefits of combining these techniques to identify individual tree species. A QuickBird image and low point density LiDAR data for a coastal region in New Zealand was used to examine the possibility of mapping Pohutukawa trees which are regarded as an iconic tree in New Zealand. The study area included a mix of buildings and vegetation types. After image and LiDAR preparation, single tree objects were identified using a range of techniques including: a threshold of above ground height to eliminate ground based objects; Normalised Difference Vegetation Index and elevation difference between the first and last return of LiDAR data to distinguish vegetation from buildings; geometric information to separate clusters of trees from single trees, and treetop identification and region growing techniques to separate tree clusters into single tree crowns. Important feature variables were identified using Random Forest, and the Support Vector Machine provided the classification. The combined techniques using LiDAR and spectral data produced an overall accuracy of 85.4% (Kappa 80.6%). Classification using just the spectral data produced an overall accuracy of 75.8% (Kappa 67.8%). The research findings demonstrate how the combining of LiDAR and spectral data improves classification for Pohutukawa trees.     

基于改进的数学形态学算法的LiDAR点云数据滤波

为解决复杂场景下高陡边坡点云数据的植被过滤问题,首先研究了高陡边坡上植被和岩石激光点云的多尺度维度特征;然后利用支持向量机(support vector machine,SVM)构建分类器,针对高陡边坡点云数据提出滤波算法,并编制了三维激光点云滤波软件LIDARVIEW。实验数据表明,复杂场景内不同尺度的植被均得到很好识别,滤波算法分类精度较高;算法不受激光点云的密度、遮挡和复杂地形的影响,且适用于机载雷达点云数据的滤波;植被覆盖率高的岩石分类精度高于93%,植被覆盖率低的岩石分类精度高于97%。算法对山丘区有复杂地貌的高陡边坡地形测量具有重大研究意义。     

CO-Kriging Approach for Cartosat-1 Height Product with ICESat/GLAS Data for Digital Elevation Surface Generation

Nowadays, Geostatistics and its various interpolation techniques have become a major threshold area in the field of research in GIS. In this research work poorly sampled (less accurate height data relative to ICESat/GLAS height data) Cartosat-1 height data has been used with well sampled (more accurate height data relative to Cartosat-1 height data) ICESat/GLAS LiDAR (Light Detection and Ranging) height point data using Cokriging Interpolation technique, to study the effect of ICESat/GLAS on Cartosat-1 height data. Space borne LiDAR data has led researchers to explore its utilities in many applications. Space borne LiDAR data can be acquired through space borne LiDAR sensors also, like; GLAS (Geoscience Laser Altimeter System) system onboard ICESat (Ice, Cloud and land Elevation Satellite) satellite. In this study, it has been tried to apply Cokriging interpolation on two different sources of data sets, with a common variable (elevation) to generate DES and assessment of this surface has been conducted by DGPS data. After optimizing Cokriging parameters, results of digital elevation surface (DES) generated using Cokriging showed that RMSE has been second least than global polynomial in comparison to Kriging interpolation RMSE after being evaluated by GPS values. So, global polynomial as well as cokriging interpolation technique out performs while comparing with kriging technique for DES generation.     

九寨沟核心景区多源遥感数据地质灾害解译初探

点云密度是激光雷达（light detection and ranging,LiDAR）技术的重要参数,对森林遥感反演指数的提取有重要影响。以1 600 m×1 450 m大小的无人机（unmanned aerial vehicle,UAV）LiDAR数据为实验数据,采用分级随机抽稀法对实验数据进行抽稀,获取不同密度的点云数据集,利用不同密度数据集提取郁闭度、间隙率、叶面积指数、点云高度和密度分位数等森林遥感反演指数,并与原始数据提取的森林遥感反演指数进行差值比较。（1）当点云密度较小时,提取的郁闭度略微偏低,而间隙率略微增加,点云密度对郁闭度、间隙率的影响极小。（2）当点云密度较大时,对叶面积指数的影响不大,但当点云密度较小时,对叶面积指数的影响较大,个别区域可能出现叶面积指数突变。（3）当点云密度较大时,点云密度对高度、密度分位数的影响不明显,但当点云密度降至3.6点/m²时,可能会出现个别区域密度、高度密度分位数突变的情况。点云密度对森林遥感反演指数有重要影响,合适的点云密度有利于更准确地描述森林结构形态,过小的点云密度影响森林遥感反演指数的提取。

     

Mt. Etna volcano high-resolution topography: airborne LiDAR modelling validated by GPS data

High-resolution digital topography is essential for land management and planning in any type of territory as well as the reproduction of the Earth surface in a geocoded digital format that allows several Digital Earth applications. In a volcanic environment, Digital Elevation Models are a valid reference for multi-temporal analyses aimed to observe frequent changes of a volcano edifice and for the relative detailed morphological and structural analyses. For the first time, a DTM (Digital Terrain Model) and a DSM (Digital Surface Model) covering the entire Mt. Etna volcano (Italy) derived from the same airborne Light Detection and Ranging (LiDAR) are here presented. More than 250 million 3D LiDAR points have been processed to distinguish ground elements from natural and anthropic features. The end product is the highly accurate representation of Mt. Etna landscape (DSM) and ground topography (DTM) dated 2005. Both models have a high spatial resolution of 2?m and cover an area of 620?km². The DTM has been validated by GPS ground control points. The vertical accuracy has been evaluated, resulting in a root-mean-square-error of ±?0.24?m. The DTM is available as electronic supplement and represents a valid support for various scientific studies.     

Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping

Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, the inventory and characterization of wetland habitats are most often limited to small areas. This explains why the understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. While LiDAR data and multispectral Earth Observation (EO) images are often used separately to map wetland habitats, their combined use is currently being assessed for different habitat types. The aim of this study is to evaluate the combination of multispectral and multiseasonal imagery and LiDAR data to precisely map the distribution of wetland habitats. The image classification was performed combining an object-based approach and decision-tree modeling. Four multispectral images with high (SPOT-5) and very high spatial resolution (Quickbird, KOMPSAT-2, aerial photographs) were classified separately. Another classification was then applied integrating summer and winter multispectral image data and three layers derived from LiDAR data: vegetation height, microtopography and intensity return. The comparison of classification results shows that some habitats are better identified on the winter image and others on the summer image (overall accuracies = 58.5 and 57.6%). They also point out that classification accuracy is highly improved (overall accuracy = 86.5%) when combining LiDAR data and multispectral images. Moreover, this study highlights the advantage of integrating vegetation height, microtopography and intensity parameters in the classification process. This article demonstrates that information provided by the synergetic use of multispectral images and LiDAR data can help in wetland functional assessment     

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。     

一种基于形态学的激光雷达数据滤波算法的改进

机载激光雷达是DEM生产的重要技术手段之一.为了获取高分辨率的DEM,首先要将建筑物、车辆、植被等非地面点去除,这一过程称为滤波.本文在现有算法的基础上提出一种改进的形态学滤波算法.该算法逐步增大滤波窗口尺寸,并采用自适应的高差阈值,较好地保留了地形拓扑信息.试验采用ISPRS提供的参考数据进行测试分析,该数据代表典型...     

机载激光雷达及高光谱的森林乔木物种多样性遥感监测

利用机载LiDAR和高光谱数据并结合37个地面调查样本数据,基于结构差异与光谱变异理论,通过相关分析法分别筛选了3个最优林冠结构参数和6个最优光谱指数,在单木尺度上利用自适应C均值模糊聚类算法,在神农架国家自然保护区开展森林乔木物种多样性监测,实现了森林乔木物种多样性的区域成图。研究结果表明,(1)基于结合形态学冠层控制的分水岭算法可以获得较高精度的单木分割结果(R~2=0.88,RMSE=13.17,P0.001);(2)基于LiDAR数据提取的9个结构参数中,95%百分位高度、冠层盖度和植被穿透率为最优结构参数,与Shannon-Wiener指数的相关性达到R~2=0.39—0.42(P0.01);(3)基于机载高光谱数据筛选的16个常用的植被指数中,CRI、OSAVI、Narrow band NDVI、SR、Vogelmann index1、PRI与Shannon-Wiener指数的相关性最高(R~2=0.37—0.45,P0.01);(4)在研究区,利用以30 m×30 m为窗口的自适应模糊C均值聚类算法可预测的最大森林乔木物种数为20,物种丰富度的预测精度为R~2=0.69,RMSE=3.11,Shannon-Wiener指数的预测精度为R~2=0.70,RMSE=0.32。该研究在亚热带森林开展乔木物种多样性监测,是在区域尺度上进行物种多样性成图的重要实践,可有效补充森林生物多样性本底数据的调查手段,有助于实现生物多样性的长期动态监测及科学分析森林物种多样性的现状和变化趋势。