机载LiDAR数据的LZD航带平差

系统误差是影响机载LiDAR系统精度的主要因素,因而消除或削弱系统误差影响、提高数据精度具有重要理论意义和工程实用价值。提出基于最小高程差(LZD)的机载LiDAR航带平差方法,并通过引入高斯-马尔柯夫模型提高了平差精度。实验部分考察了引入高斯-马尔柯夫模型的必要性及算法精度。实验结果表明:引入高斯-马尔柯夫模型可有效地提高算法精度;使用LZD进行机载LiDAR航带平差获取的结果均可以满足工程生产的精度需求;与商业软件TerraMatch相比,LZD的精度和TerraMatch的精度相当。     

Automated residential building detection from airborne LiDAR data with deep neural networks

Detection of building objects in airborne LiDAR data is an essential task in many types of geospatial data applications such as urban reconstruction and damage assessment. Traditional approaches used in building detection often rely on shape primitives that can be detected by 2D/3D computer vision techniques. These approaches require carefully engineered features which tend to be specific to building types. Furthermore, these approaches are often computationally expensive with the increase of data size. In this paper, we propose a novel approach that employs a deep neural network to recognize and extract residential building objects in airborne LiDAR data. This proposed approach does not require any pre-defined geometric or texture features, and it is applicable to airborne LiDAR data sets with varied point densities and with damaged building objects. The latter makes our approach particularly useful in damage assessment applications. The research results show that the proposed approach is capable of achieving the state-of-the-art accuracy in building detection in these different types of point cloud data sets.     

Mapping of understory lichens with airborne discrete-return LiDAR data

High backscatter reflectance at NIR wavelengths has been observed for reindeer lichens (Cladina sp.) in the laboratory. The results suggested that lichens could be separated from soil and other parts of forest understory using this property. An experiment was carried out to test this hypothesis in situ. The lichen vegetation of a 960-m² plot in a barren pine stand in Juupajoki, Finland was mapped in 3D, using methods of close-range photogrammetry. The data of two airborne discrete-return sensors were compared for their ability to classify understory lichen vegetation. Normalization of the LiDAR intensities was carried out, using natural targets. The results showed that lichen surfaces had a higher intensity than on average. Normalization of the intensities improved separability of lichens from other surfaces, and the best-case classification accuracy was 75%. Detailed analysis of geometric errors revealed small, decimeter-level planimetric offsets in the LiDAR datasets that affected the results notably.     

无需阈值支持的机载LiDAR点云数据滤波方法

点云数据滤波仍旧是现阶段机载LiDAR数据后处理的首要步骤,但其发展尚未完全成熟。在回顾和总结已有滤波算法的基础上,将统计学中偏度与峰度的概念引入到算法中,提出了一种新的基于偏度平衡的地面点与非地面点非监督分类方法,利用统计矩原理从LiDAR点云数据生成的DSM中有效地提取DTM。该方法区别传统算法的最大的优势在于无需参数或者阈值支持,并且相对于LiDAR点云数据的格式和分辨率是独立的。实验结果证明,该方法切实可行,具有较强的适应性,并且能够较好地满足精度要求。     

Characterization of individual tree crowns using three-dimensional shape signatures derived from LiDAR data

Three-dimensional (3D) shape signatures based on the distance distribution of random point pairs are introduced and the effectiveness evaluated using computer simulations and samples of oak and Douglas fir crowns selected from Light Detection and Ranging (LiDAR) point clouds and Digital Surface Models (DSMs). The results suggest that comparison of 3D crown shapes can be effectively reduced to the comparison of frequency distributions of distances between random points, and that it is more computationally efficient when shape signatures are derived from raster surfaces. The results also suggest that the statistically based 3D shape signatures are relatively insensitive to noise and other small local variations, which is important for crown shape analysis in real-world environments.     

Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain

Practical and financial constraints associated with traditional field-based lithological mapping are often responsible for the generation of maps with insufficient detail and inaccurately located contacts. In arid areas with well exposed rocks and soils, high-resolution multi- and hyperspectral imagery is a valuable mapping aid as lithological units can be readily discriminated and mapped by automatically matching image pixel spectra to a set of reference spectra. However, the use of spectral imagery in all but the most barren terrain is problematic because just small amounts of vegetation cover can obscure or mask the spectra of underlying geological substrates. The use of ancillary information may help to improve lithological discrimination, especially where geobotanical relationships are absent or where distinct lithologies exhibit inherent spectral similarity. This study assesses the efficacy of airborne multispectral imagery for detailed lithological mapping in a vegetated section of the Troodos ophiolite (Cyprus), and investigates whether the mapping performance can be enhanced through the integration of LiDAR-derived topographic data. In each case, a number of algorithms involving different combinations of input variables and classification routine were employed to maximise the mapping performance. Despite the potential problems posed by vegetation cover, geobotanical associations aided the generation of a lithological map - with a satisfactory overall accuracy of 65.5% and Kappa of 0.54 - using only spectral information. Moreover, owing to the correlation between topography and lithology in the study area, the integration of LiDAR-derived topographic variables led to significant improvements of up to 22.5% in the overall mapping accuracy compared to spectral-only approaches. The improvements were found to be considerably greater for algorithms involving classification with an artificial neural network (the Kohonen Self-Organizing Map) than the parametric Maximum Likelihood Classifier. The results of this study demonstrate the enhanced capability of data integration for detailed lithological mapping in areas where spectral discrimination is complicated by the presence of vegetation or inherent spectral similarities.     

Estimation of tree crown volume from airborne lidar data using computational geometry

Although simple geometrical shapes are commonly used to describe tree crowns, computational geometry enables calculation of the individual crown properties directly from airborne lidar point clouds. Our objective was to calculate crown volumes (CVs) using this technique and validate the results by comparing them with field-measured values and modelled ellipsoidal crowns. The CVs of standing trees were obtained by measuring the crown radii at different heights, integrating the obtained crown profiles as solids of revolution, and finally averaging the volumes obtained from the four separate profiles. With the lidar data, the CVs were extracted using 3D alpha shape and 3D convex hull techniques. Crown base heights (CBHs) were also estimated from the lidar data and used to exclude echoes from the understory, which was also done using field-based CBHs to exclude this error source. The results show that the field-measured CVs had a high correlation with lidar-based estimates (best R ² = 0.83), but the lidar-based estimates were generally smaller than the field values. The best correspondence (root mean square difference (RMSD) = 45.0%, average difference = –24.7%) was obtained using the convex hull of the point data and field-measured CBH. The CBHs were consistently overestimated (RMSD = 37.3%; average difference = –20.0%), especially in spruces with long crowns. Thus using lidar-based CBH also increased the inaccuracy of the CV estimates. While the underestimation of CV is mainly explained by the inadequate number of echoes from the lower regions of the crowns, the CVs obtained from the lidar were better than those obtained with ellipsoids fitted by using general models for crown dimensions. The utility of the estimated CVs in the prediction of stem diameter is also demonstrated.     

Modeling of urban wind ventilation using high resolution airborne LiDAR data

Accurate mapping of wind ventilation in an urban environment is challenging when large spatial coverage is required. This study has developed a GIS-based model for estimating the frontal area index (FAI) of buildings, infrastructure, and trees using very high resolution airborne light detection and ranging (LiDAR) data, which can also be used to investigate the “wall effect” caused by high-rise buildings at a finer spatial scale along the coasts in the Kowloon Peninsula of Hong Kong. New algorithms were created by improving previous algorithms utilizing airborne LiDAR data in raster unit, as well as considering the backward flow coefficient between windward and leeward buildings.The ventilation corridors estimated by FAI and least cost path (LCP) analysis were analyzed. The optimal ventilation corridors passing through the Kowloon peninsula were observed in the east-west and west-east directions. In addition, these ventilation paths were validated with a computer fluid dynamics (CFD) model i.e. Airflow Analysis in ESRI. The newly developed model calculates finer FAI with greater accuracy when compared with vector-based building polygons. This model further depicts buildings, infrastructure, and trees which are considered as obstacles to wind ventilation. The results can be used by environmental and planning authorities to identify ventilation corridors, and for scenario analysis in urban redevelopment.     

应用于大数据的Trie树排序算法

针对在数据量动态增加的场景下现有的排序算法管理数据导致算法性能大大降低的问题,提出一种16-bit Trie树排序算法.借助邻居节点上存储的链节点指针完成排序,它不仅可以边构建边排序,且引入动态数组可以提高该算法的空间效率.仿真结果表明,传统Trie树支持数据动态更新,但通过遍历Trie树的方式完成排序耗时较多,快速排...     

Delineating tree crowns from airborne laser scanning point cloud data using Delaunay triangulation

Most algorithms for delineating trees from laser scanning data are pixel‐based region growing methods. The algorithm presented in this letter makes use of the original laser points to avoid errors introduced by interpolation. Three different scales are used to identify the seed points or the local maxima at that scale. The seed points are considered to be the estimated tree tops, and are used for growing regions or tree crowns around the seed points. For a test dataset from a Finnish mixed forest and point density of approximately 2 points m^?2, up to 75% of the reference trees could be identified. At the coarser scales, fewer trees were identified, but the crowns were less fragmented. Further work is required to determine how far the method is applicable in other forest conditions and data with other point densities.     

Mapping woodland species composition and structure using airborne spectral and LiDAR data

Tree and shrub species composition and vegetation structure are key components influencing the quality of woodland or forest habitat for a wide range of organisms. This paper investigates the unique thematic classes that can be derived using integrated airborne LiDAR and spectral data. The study area consists of a heterogeneous, semi‐natural broadleaf woodland on an ancient site and homogeneous broadleaf and conifer woodland on an adjoining plantation. A parcel‐based unsupervised classification approach was employed, using the first two Principal Components from 12 selected wavebands of HyMap data and a Digital Canopy Height Model extracted from LiDAR data. The resultant 52 data clusters were amalgamated into 10 distinct thematic classes that contain information on species composition and vegetation structure. The thematic classes are relevant to the National Vegetation Classification (NVC) scheme for woodlands and scrub of Great Britain. Furthermore, in distinguishing structural subdivisions within the species‐based NVC classes, the thematic classification provides greater information for quantifying woodland habitat. The classes show degeneration from and regeneration to mature woodland communities and thus reflect the underlying processes of vegetation succession and woodland management. This thematic classification is ecologically relevant and is a forward development in woodland maps created from remote sensing data.     

Lithological mapping of the Troodos ophiolite, Cyprus, using airborne LiDAR topographic data

Traditional field-based lithological mapping can be a time-consuming, costly and challenging endeavour when large areas need to be investigated, where terrain is remote and difficult to access and where the geology is highly variable over short distances. Consequently, rock units are often mapped at coarse-scales, resulting in lithological maps that have generalised contacts which in many cases are inaccurately located. Remote sensing data, such as aerial photographs and satellite imagery are commonly incorporated into geological mapping programmes to obtain geological information that is best revealed by overhead perspectives. However, spatial and spectral limitations of the imagery and dense vegetation cover can limit the utility of traditional remote sensing products. The advent of Airborne Light Detection And Ranging (LiDAR) as a remote sensing tool offers the potential to provide a novel solution to these problems because accurate and high-resolution topographic data can be acquired in either forested or non-forested terrain, allowing discrimination of individual rock types that typically have distinct topographic characteristics. This study assesses the efficacy of airborne LiDAR as a tool for detailed lithological mapping in the upper section of the Troodos ophiolite, Cyprus. Morphometric variables (including slope, curvature and surface roughness) were derived from a 4 m digital terrain model in order to quantify the topographic characteristics of four principal lithologies found in the area. An artificial neural network (the Kohonen Self-Organizing Map) was then employed to classify the lithological units based upon these variables. The algorithm presented here was used to generate a detailed lithological map which defines lithological contacts much more accurately than the best existing geological map. In addition, a separate map of classification uncertainty highlights potential follow-up targets for ground-based verification. The results of this study demonstrate the significant potential of airborne LiDAR for lithological discrimination and rapid generation of detailed lithological maps, as a contribution to conventional geological mapping programmes.     

基于改进四叉树的LiDAR点云数据组织研究

分析了点云数据处理中常用数据组织方法,并指出方法的性能判定指标。对常用的构建四叉树方法进行了改进以提高建立四叉树索引的速度,分析及改进索引算法改进以增进数据筛选的速度,最后通过实验证明了该方法的有效性和可靠性。     

不确定数据的决策树分类算法

经典决策树算法不能处理树构建和分类过程中的不确定数据。针对这一局限,将可用于不确定数据表达的证据理论与决策树分类算法相结合,把决策树分类技术扩展到含有不确定数据的环境中。为避免在决策树构建过程中出现组合爆炸问题,引入新的测量算子和聚集算子,提出了D-S证据理论决策树分类算法。实验结果表明,D-S证据理论决策树分类算法能有效地对不确定数据进行分类,有较好的分类准确度,并能有效避免组合爆炸。     

Unmasking forest borderlines by an automatic delineation based on airborne laser scanner data

This study presents an automatic methodology based on airborne laser scanner (ALS) data, which allows the mapping of forests, using quantitative criteria typical of forest definitions, i.e. minimum threshold for the height of trees, canopy cover, forest area size, and width. Interactions between forest and other land uses are explored by the methodology for the definition of forest borderlines using an additional criterion; this criterion is the distance-discontinuity (DD), which establishes a minimum width (MW) for portions of territory categorized by land uses different from the forest. The proposed forest mapping approach introduces also a fuzzy algorithm to assess the canopy cover, thereby enhancing the positional accuracy in the delineation of the forest borderline. This methodology has a very flexible mapping approach with the potential to address the many forest definitions existing worldwide. The evaluation and the improvement of the methodology are particularly encouraged by its release as an open source tool.     

Improving within-genus tree species discrimination using the discrete wavelet transform applied to airborne hyperspectral data

Discrete wavelet analysis was assessed for its utility in aiding discrimination of three pine species (Pinus spp.) using airborne hyperspectral data (AVIRIS). Two different sets of Haar wavelet features were compared to each other and to calibrated radiance, as follows: (1) all combinations of detail and final level approximation coefficients and (2) wavelet energy features rather than individual coefficients. We applied stepwise discriminant techniques to reduce data dimensionality, followed by discriminant techniques to determine separability. Leave-one-out cross validation was used to measure the classification accuracy. The most accurate (74.2%) classification used all combinations of detail and approximation coefficients, followed by the original radiance (66.7%) and wavelet energy features (55.1%). These results indicate that application of the discrete wavelet transform can improve species discrimination within the Pinus genus.     

A mixed pixel- and region-based approach for using airborne laser scanning data for individual tree crown delineation in Pinus radiata D. Don plantations

The aim of this study was to evaluate the use of high-resolution airborne laser scanner (ALS) data to detect and measure individual trees. We developed and tested a new mixed pixel- and region-based algorithm (using Definiens Developer 7.0) for locating individual tree positions and estimating their total heights. We computed a canopy height model (CHM) of pixel size 0.25 m from dense first-pulse point data (8 pulses m^?2) acquired with a small-footprint discrete-return lidar sensor. We validated the results of individual tree segmentation with accurate field measurements made in 37 plots of Monterey pine (Pinus radiata D. Don) distributed over an area of 36 km². Fieldwork consisted of labelling all of the trees in each plot and measuring their height and position, for posterior integration of the data from both sources (field and lidar). The proposed algorithm correctly detected and linked 59.8% of the trees in the 37 sample plots. We also manually located the trees by using FUSION software to visualize the raw lidar data cloud. However, because the latter method is extremely time-consuming, we only considered 10 randomly selected plots. Manual location correctly detected and linked 71.9% of the trees (in this subsample the algorithm correctly detected and measured 63.5% of the trees). The R² values for the linear model relating field- and lidar-measured heights of the linked trees located manually and with the automatic location algorithm were 0.90 and 0.88, respectively.     

Combining high spatial resolution multi-temporal satellite data with leaf-on LiDAR to enhance tree species discrimination at the crown level

ABSTRACT

The long-standing goal of discriminating tree species at the crown-level from high spatial resolution imagery remains challenging. The aim of this study is to evaluate whether combining (a) high spatial resolution multi-temporal images from different phenological periods (spring, summer and autumn), and (b) leaf-on LiDAR height and intensity data can enhance the ability to discriminate the species of individual tree crowns of red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina) in the Fernow Experimental Forest, West Virginia, USA. We used RandomForest models to measure a loss of classification accuracy caused by iteratively removing from the classification one or more groups from six groups of variables: spectral reflectance from all multispectral bands in the (1) spring, (2) summer, and (3) autumn images, (4) vegetation indices derived from the three multispectral datasets, (5) canopy height and intensity from the LiDAR imagery, and (6) texture related variables from the panchromatic and LiDAR datasets. We also used ANOVA and decision tree analyses to elucidate how the multispectral and LiDAR datasets combine to help discriminate tree species based on their unique phenological, spectral, textural, and crown architectural traits. From these results, we conclude that combing high spatial resolution multi-temporal satellite data with LiDAR datasets can enhance the ability to discriminate tree species at the crown level.