机载大光斑激光雷达数据估测森林结构参数研究进展

针对LiDAR对森林结构具有较强探测能力,从而能够准确地获取森林的三维结构的优势,该文试图将该技术运用于估测森林结构参数,并介绍了机载大光斑LiDAR系统的工作原理和主要技术规格,系统总结了机载大光斑LiDAR数据估测森林结构参数的研究现状,分析了机载大光斑LiDAR估测森林结构参数的局限性并对其进行了展望。     

基于激光雷达的大兴安岭典型森林生物量制图技术研究

森林生物量作为森林生态系统基本的数量表征,表明了森林的经营水平和开发利用价值,并能反映其与环境在物质循环和能量流动方面的复杂关系。同时,森林生物量也是林业问题和生态问题研究的基础。以内蒙古大兴安岭国家野外生态站为研究区域,通过对机载激光雷达(LiDAR)点云数据的预处理,利用计算机编程提取LiDAR点云数据的结构参数,以植被分位数高度变量与密度变量为自变量,结合地面调查数据,建立生物量与LiDAR结构参数的回归模型(决定系数为0.69,均方根误差为0.34)。运用IDL编程对LiDAR点云块数据进行运算并生成分辨率为20m×20m的栅格图像,拼接后得到整个区域的地上生物量分布图,对生成的地上生物量分布图进行验证的R2为0.78,RMSE为23.09t/hm2,平均估测精度达83%。     

基于机载激光雷达数据的森林结构参数反演

机载激光雷达(Light Detection And Ranging,LiDAR)技术对植被空间结构和地形的探测能力较强,在植被参数定量测量和反演方面具有显著优势。首先利用野外调查并结合高分辨率Geoeye-1影像数据,对黑河上游天涝池流域植被类型进行分类,提取研究区森林分布,然后结合0.5m×0.5m机载激光雷达(LiDAR)数据对森林结构参数(树高、冠幅、胸径和叶面积指数)进行反演,最后利用实际观测数据对反演结果进行验证。结果表明:机载激光雷达数据能够精确地反演森林结构参数,树高、冠幅、胸径和叶面积指数的实测值与估测值决定系数分别为0.98、0.84、0.57和0.73。本研究获得流域森林覆盖区域高精度树冠高度和叶面积指数空间分布图,同时分析了冠层高度和叶面积指数随高度的变化。本研究的结果为该流域分布式生态水文模型提供了重要的输入参数。     

面向应用的《激光遥感》课程实践设计

《激光遥感》课程是武汉大学遥感信息工程学院为适应遥感技术发展的最新需求,针对本科生设置的一门专业必修课程,为学生传授LiDAR技术的相关知识。本文针对教学大纲和LiDAR技术的特点,根据科研和生产的需要,设计了实践课程,并提出了课程的框架和建设方案。     

机载LiDAR数据航带平差研究进展

面向机载LiDAR数据航带平差处理的理论前沿,全面总结了机载LiDAR航带平差技术的国内外现状并进行了相应的归纳、分析,指出了当前研究中的一些不足,为后续的研究提供了参考。     

用于铁路勘察的机载LiDAR数据生产DEM方法研究

机载LiDAR数据是一种比较好的DEM生产的数据源,尽管生产流程基本成熟,但其中的一些关键技术还不能满足生产需求,需要在行业应用时加以深入研究。针对铁路勘察的具体特点和要求,提出了一种综合使用专业软件和多种航空摄影测量技术手段,满足铁路勘察设计需求的机载LiDAR数据生产DEM方法,并将其运用在生产实践中,大规模生产的结果表明,本流程和方法是可行的。     

阵列推扫式机载激光雷达三维点云解算方法研究

激光雷达（LiDAR）系统能够快速获取高精度数字地表模型,广泛应用于国土资源调查、地形测量、林业、灾害评估等方面。LiDAR系统采集的原始数据必须经过点云解算才能生成满足应用需求的三维点云,为此,在分析阵列推扫式机载激光雷达系统成像原理的基础上,详细推导了基于同平台POS数据的三维坐标解算模型,提出了面向该系统的激光雷达三维点云解算方法以实现三维点云的生成,利用飞行试验数据解算结果验证了此方法的有效性。该方法适用于同类型阵列推扫式机载激光雷达的点云解算。     

基于无人机激光雷达的森林冠层高度分析——以祁连山东、中部青海云杉为例

快速准确获取森林结构参数对森林资源调查管理及全球碳汇研究具有重要意义.以祁连山东、中部青海云杉林为研究对象,利用16个无人机激光雷达(LiDAR)点云数据、正射影像数据结合实地样方观测数据,提取样方内青海云杉的单木树高并准确验证树木分割精度;结合实测数据和地形数据,依据统计指标验证提取树高精度并分析原因;基于点云数据提...     

机载X-波段双天线InSAR数据森林树高估测方法

短波长的干涉合成孔径雷达(InSAR)适用于数字表面模型(DSM)提取,但难以提取准确的林下地相位,在缺乏高精度数字高程模型(DEM)的森林区域,短波长InSAR数据估测树高的能力受到限制。针对这一问题,采用机载X-波段单极化(HH)双天线InSAR数据开展了森林树高估测方法研究。双天线InSAR可以忽略时间去相干的影响,并且X-波段波长较短,入射角较大(中心入射角45.77°),地表对干涉去相干的贡献可以忽略,因此可将干涉复相干作为体去相干,对体去相干模型中的结构函数进行勒让德展开,截取第0阶展开式得到了基于相干幅度的森林树高估测模型,利用均匀选取的LiDAR冠层高度模型(CHM)检验样本对估测结果进行严格的精度评价,并与差分法的树高估测结果进行对比。精度评价结果显示:相干幅度法与差分法都得到了较高的估测精度,两者的R~2、RMSE、总精度分别为0.81、0.86;1.20m、0.97m;86.4%、88.7%。研究结果表明:相干幅度与森林树高具有负相关关系,适用于估测树高,基于单极化相干幅度的估测模型也可以得到较高的估测精度,与差分法的估测结果相比,虽然估测精度略有降低,但此方法具有两方面的优势:一方面,估测结果不需要实测样地数据标定,对于没有实测样地数据的森林区域亦能进行高精度的树高估测;另一方面,相干幅度法不需要高精度的DEM,具有更强的实用性。     

基于机载LVIS和星载GLAS波形LiDAR数据反演森林LAI

波形激光雷达（Light Detection And Ranging, LiDAR）已经大量用于森林叶面积指数（Leaf Area Index, LAI）估算,但是波形LiDAR数据估算森林LAI易受地形影响。地形坡度引起的波形展宽使得地面回波和植被冠层回波信息混合在一起,难以得到准确的地面回波和冠层回波,进而影响到LAI估算精度。为了估算不同地形坡度条件下的LAI,本文采用一种坡度自适应的方法处理机载LVIS和星载GLAS波形数据。通过坡度自适应的方法得到地面波峰位置,基于高度阈值来区分地面回波和冠层回波,进而得到能量比值用于LAI估算。基于LVIS和GLAS数据,估算了不同森林站点的LAI,并利用实测LAI数据进行检验。结果表明：利用波形LiDAR数据可以估算森林LAI,坡度自适应方法可以改善地形的影响,提高LAI估算精度。对于机载LVIS,估算新英格兰森林LAI精度为R²=0.77和RMSE=0.21;对于星载GLAS,估算塞罕坝森林LAI精度为R²=0.81和RMSE=0.28。无论机载还是星载数据,该方法都有着较高的精度,对于复杂地形估算LAI具有一定潜力。     

机载脉冲激光雷达剖面测量技术的进展及应用

机载脉冲激光雷达(LiDAR)剖面测量技术是一种先进的主动遥感测量技术,可以快速、大面积地直接获取地表地物、森林和水下地貌等的三维信息,具有机动性强、高效和实时等优点。该技术可用于海岸线海水深度测量,海岸生态状况监测,森林资源调查及地震等突发事件响应,具有巨大的应用潜力和广阔的发展前景。首先全面介绍了国外机载脉冲激光雷达剖面测量技术的发展历史,评述了各个发展阶段,并介绍了国内该技术的发展状况。然后分析了机载脉冲激光雷达剖面测量技术的主要应用领域。最后对机载脉冲激光雷达剖面测量技术的未来发展趋势作了展望。     

Computer-based synthetic data to assess the tree delineation algorithm from airborne LiDAR survey

Small Footprint LiDAR (Light Detection And Ranging) has been proposed as an effective tool for measuring detailed biophysical characteristics of forests over broad spatial scales. However, by itself LiDAR yields only a sample of the true 3D structure of a forest. In order to extract useful forestry relevant information, this data must be interpreted using mathematical models and computer algorithms that infer or estimate specific forest metrics. For these outputs to be useful, algorithms must be validated and/or calibrated using a sub-sample of ‘known’ metrics measured using more detailed, reliable methods such as field sampling. In this paper we describe a novel method for delineating and deriving metrics of individual trees from LiDAR data based on watershed segmentation. Because of the costs involved with collecting both LiDAR data and field samples for validation, we use synthetic LiDAR data to validate and assess the accuracy of our algorithm. This synthetic LiDAR data is generated using a simple geometric model of Loblolly pine (Pinus taeda) trees and a simulation of LiDAR sampling. Our results suggest that point densities greater than 2 and preferably greater than 4 points per m2 are necessary to obtain accurate forest inventory data from Loblolly pine stands. However the results also demonstrate that the detection errors (i.e. the accuracy and biases of the algorithm) are intrinsically related to the structural characteristics of the forest being measured. We argue that experiments with synthetic data are directly useful to forest managers to guide the design of operational forest inventory studies. In addition, we argue that the development of LiDAR simulation models and experiments with the data they generate represents a fundamental and useful approach to designing, improving and exploring the accuracy and efficiency of LiDAR algorithms.     

Object-based land cover classification using airborne LiDAR

Light Detection and Ranging (LiDAR) provides high resolution horizontal and vertical spatial point cloud data, and is increasingly being used in a number of applications and disciplines, which have concentrated on the exploit and manipulation of the data using mainly its three dimensional nature. LiDAR information potential is made even greater though, with its consideration of intensity.Elevation and intensity airborne LiDAR data are used in this study in order to classify forest and ground types quickly and efficiently without the need for manipulating multispectral image files, using a supervised object-orientated approach. LiDAR has the advantage of being able to create elevation surfaces that are in 3D, while also having information on LiDAR intensity values, thus it is a spatial and spectral segmentation tool. This classification method also uses point distribution frequency criteria to differentiate between land cover types. Classifications were performed using two methods, one that included the influence of the ground in heavily vegetated areas, and the other which eliminated the ground points before classification. The classification of three meanders of the Garonne and Allier rivers in France has demonstrated overall classification accuracies of 95% and 94% for the methods including and excluding the ground influence respectively. Five types of riparian forest were classified with accuracies between 66 and 98%. These forest types included planted and natural forest stands of different ages. Classifications of short vegetation and bare earth also produced high accuracies averaging above 90%.     

Delineation of individual deciduous trees in plantations with low-density LiDAR data

Delineation of individual deciduous trees with Light Detection and Ranging (LiDAR) data has long been sought for accurate forest inventory in temperate forests. Previous attempts mainly focused on high-density LiDAR data to obtain reliable delineation results, which may have limited applications due to the high cost and low availability of such data. Here, the feasibility of individual deciduous tree delineation with low-density LiDAR data was examined using a point-density-based algorithm. First a high-resolution point density model (PDM) was developed from low-density LiDAR point cloud to locate individual trees through the horizontal spatial distribution of LiDAR points. Then, individual tree crowns and associated attributes were delineated with a 2D marker-controlled watershed segmentation. Additionally, the PDM-based approach was compared with a conventional canopy height model (CHM) based delineation. The results demonstrated that the PDM-based approach produced an 89% detection accuracy to identify deciduous trees in our study area. The tree attributes derived from the PDM-based algorithm explained 81% and 83% of tree height and crown width variations of forest stands, respectively. The conventional CHM-based tree attributes, on the other hand, could explain only 71% and 66% of tree height and crown width, respectively. Our results suggest that the application of the PDM-based individual tree identification in deciduous forests with low-density LiDAR data is feasible and has relatively high accuracy to predict tree height and crown width, which are highly desired in large-scale forest inventory and analysis.     

基于RFID和Android的林木调查系统

以林木调查为例,介绍了将RFID技术应用于林业调查比传统工作模式的优势,列举了其在林业调查中的部分业务应用。同时基于RFID技术和Android技术,介绍了林木调查系统的总体设计,并以资源清查中的一类调查为例,重点介绍了Android终端数据采集软件的设计及功能实现方法,文章最后总结了林木调查系统在林业上的应用范围以及RFID技术与Android技术在林业上的应用发展前景。     

Modeling forest songbird species richness using LiDAR-derived measures of forest structure

Conservation of biodiversity requires information at many spatial scales in order to detect and preserve habitat for many species, often simultaneously. Vegetation structure information is particularly important for avian habitat models and has largely been unavailable for large areas at the desired resolution. Airborne LiDAR, with its combination of relatively broad coverage and fine resolution provides existing new opportunities to map vegetation structure and hence avian habitat. Our goal was to model the richness of forest songbirds using forest structure information obtained from LiDAR data. In deciduous forests of southern Wisconsin, USA, we used discrete-return airborne LiDAR to derive forest structure metrics related to the height and density of vegetation returns, as well as composite variables that captured major forest structural elements. We conducted point counts to determine total forest songbird richness and the richness of foraging, nesting, and forest edge-related habitat guilds. A suite of 35 LiDAR variables were used to model bird species richness using best-subsets regression and we used hierarchical partitioning analysis to quantify the explanatory power of each variable in the multivariate models. Songbird species richness was correlated most strongly with LiDAR variables related to canopy and midstory height and midstory density (R² = 0.204, p < 0.001). Richness of species that nest in the midstory was best explained by canopy height variables (R² = 0.197, p < 0.001). Species that forage on the ground responded to mean canopy height and the height of the lower canopy (R² = 0.149, p < 0.005) while aerial foragers had higher richness where the canopy was tall and dense and the midstory more sparse (R² = 0.216, p < 0.001). Richness of edge-preferring species was greater where there were fewer vegetation returns but higher density in the understory (R² = 0.153, p < 0.005). Forest interior specialists responded positively to a tall canopy, developed midstory, and a higher proportion of vegetation returns (R² = 0.195, p < 0.001). LiDAR forest structure metrics explained between 15 and 20% of the variability in richness within deciduous forest songbird communities. This variability was associated with vertical structure alone and shows how LiDAR can provide a source of complementary predictive data that can be incorporated in models of wildlife habitat associations across broad geographical extents.     

茂密植被区域LiDAR点云数据滤波方法研究

点云数据的滤波和分类是激光雷达数据应用处理重要环节,是当前研究的热点问题。本文针对茂密植被区域点云数据的特点,提出了以移动窗口和坡度算法为基础的改进的点云数据滤波算法。试验结果表明,改进的滤波算法对地形变化复杂、植被郁闭度较高覆盖、地面激光脚点比少的点云数据有良好的效果。     

Strengths and limitations of assessing forest density and spatial configuration with aerial LiDAR

Changes in the structural state of forests of the semi-arid U.S.A., such as an increase in tree density, are widely believed to be leading to an ecological crisis, but accurate methods of quantifying forest density and configuration are lacking at landscape scales. An individual tree canopy (ITC) method based on aerial LiDAR has been developed to assess forest structure by estimating the density and spatial configuration of trees in four different height classes. The method has been tested against field measured forest inventory data from two geographically distinct forests with independent LiDAR acquisitions. The results show two distinct patterns: accurate, unbiased density estimates for trees taller than 20 m, and underestimation of density in trees less than 20 m tall. The underestimation of smaller trees is suggested to be a limitation of LiDAR remote sensing. Ecological applications of the method are demonstrated through landscape metrics analysis of density and configuration rasters.     

基于机载LiDAR数据的三维树木建模方法

提出一种基于机载激光雷达系统LiDAR数据树木可视化建模的方法。利用机载LiDAR数据的特点,结合L-系统分形的思想,对传统的L-系统方法进行随机化、参数化的扩展和改进,由此对数据进行建模。针对LiDAR数据分布空间由内到外的层次特点,采取分步的建模策略,在外层建立从LiDAR数据中提取L-系统参数的方法。实验结果表明,该方法具有较好的建模效果,适用于三维数字城市、虚拟现实以及林业领域。     

Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass

Regression has been widely applied in Light Detection And Ranging (LiDAR) remote sensing to spatially extend predictions of total aboveground biomass (TAGB) and other biophysical properties over large forested areas. Sample (field) plot size has long been considered a key sampling design parameter and focal point for optimization in forest surveys, because of its impact on sampling effort and the estimation accuracy of forest inventory attributes. In this study, we demonstrate how plot size and co-registration error interact to influence the estimation of LiDAR canopy height and density metrics, regression model coefficients, and the prediction accuracy of least-squares estimators of TAGB. We made use of simulated forest canopies and synthetic LiDAR point clouds, so that we could maintain strict control over the spatial scale and complexity of forest scenes, as well as the magnitude and type of planimetric error inherent in ground-reference and LiDAR datasets. Our results showed that predictions of TAGB improved markedly as plot size increased from 314 (10 m radius) to 1964 m² (25 m radius). The co-registration error (spatial overlap) between ground-reference and LiDAR samples negatively impacted the estimation of LiDAR metrics, regression model fit, and the prediction accuracy of TAGB. We found that larger plots maintained a higher degree of spatial overlap between ground-reference and LiDAR datasets for any given GPS error, and were therefore more resilient to the ill effects of co-registration error compared to small plots. The impact of co-registration error was more pronounced in tall, spatially heterogeneous stands than short, homogeneous stands. We identify and briefly discuss three possible ways that LiDAR data could be used to optimize plot size, sample selection, and the deployment of GPS resources in forest biomass surveys.