Calibration of area based diameter distribution with individual tree based diameter estimates using airborne laser scanning

Diameter distribution is essential for calculating stem volume and timber assortments of forest stands. A new method was proposed in this study to improve the estimation of stem volume and timber assortments, by means of combining the Area-based approach (ABA) and individual tree detection (ITD), the two main approaches to deriving forest attributes from airborne laser scanning (ALS) data. Two methods, replacement, and histogram matching were employed to calibrate ABA-derived diameter distributions with ITD-derived diameter estimates at plot level. The results showed that more accurate estimates were obtained when calibrations were applied. In view of the highest accuracy between ABA and ITD, calibrated diameter distributions decreased its relative RMSE of the estimated entire growing stock, saw log and pulpwood fractions by 2.81%, 3.05% and 7.73% points at best, respectively. Calibration improved pulpwood fraction significantly, which contributed to the negligible bias of the estimated entire growing stock.     

An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds

An algorithm for automatic extraction of pole-like street furniture objects using Mobile Laser Scanner data was developed and tested. The method consists in an initial simplification of the point cloud based on the regular voxelization of the space. The original point cloud is spatially discretized and a version of the point cloud whose amount of data represents 20–30% of the total is created. All the processes are carried out with the reduced version of the data, but the original point cloud is always accessible without any information loss, as each point is linked to its voxel. All the horizontal sections of the voxelized point cloud are analyzed and segmented separately. The two-dimensional fragments compatible with a section of a target pole are selected and grouped. Finally, the three-dimensional voxel representation of the detected pole-like objects is identified and the points from the original point cloud belonging to each pole-like object are extracted.The algorithm can be used with data from any Mobile Laser Scanning system, as it transforms the original point cloud and fits it into a regular grid, thus avoiding irregularities produced due to point density differences within the point cloud.The algorithm was tested in four test sites with different slopes and street shapes and features. All the target pole-like objects were detected, with the only exception of those severely occluded by large objects and some others which were either attached or too close to certain features.     

Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint

This paper presents a method for using the intensity of returns from a scanning light detection and ranging (lidar) system from a single viewing point to identify the location and measure the diameter of tree stems within a forest. Such instruments are being used for rapid forest inventory and to provide consistent supporting information for airborne lidars. The intensity transect across a tree stem is found to be consistent with a simple model parameterised by the range and diameter of the trunk. The stem diameter is calculated by fitting the model to transect data. The angular span of the stem can also be estimated by using a simple threshold where intensity values are tested against the expected intensity for a stem of given diameter. This is useful when data are insufficient to fit the model or the stem is partially obscured. The process of identifying tree positions and trunk diameters is fully automated and is shown to be successful in identifying a high proportion of trees, including some that are partially obscured from view. The range and bearing to trees are in excellent agreement with field data. Trunk angular span and diameter estimations are well correlated with field measurements at the plot scale. The accuracy of diameter estimation is found to decrease with range from the scanning position and is also reduced for stems subtending small angles (less than twice the scanning resolution) to the instrument. A method for adjusting survey results to compensate for trees missed due to obscuration from the scanning point and the use of angle count methods is found to improve basal area estimates and achieve agreement within 4% of field measurements.     

基于机载LiDAR数据的建筑物轮廓规则化方法

在点云数据分类和建筑物轮廓提取的基础上,提出一种基于最小外包矩形的建筑物轮廓规则化方法。以任意两相邻轮廓点构建初始外包矩形,以迭代的方式将内点数最多者作为最终外包矩形,继而以面积比率、距离和均方根作为指标对规则化结果给出评判,最后以实际数据进行实验。实验结果表明,文中提出的方法能够有效对分类后的建筑物轮廓点进行规则化。     

基于罗德里格矩阵的三维激光扫描点云配准算法

本文提出了一种基于罗德里格矩阵的激光扫描点云配准直接计算方法.利用反对称矩阵和罗德里格矩阵的性质,用3个独立参数代替3个旋转角参数建立一种新旋转矩阵解算模型,推导出旋转变换误差方程,确定平移参数的计算公式.通过实验分析了坐标转换模型的精度和点云配准效果,结果表明该算法精度高,计算过程简单,可以准确地解算出三维坐标转换参数.     

自适应移动盒子的机载LiDAR点云去噪算法

针对现有LiDAR点云去噪算法难以实现自适应的问题,该文提出了基于空间盒子结构的自适应移动盒子去噪算法。基于经验模型建立盒子自适应准则,较好规避了现有算法的去噪阈值确定问题;采用聚类分析实现噪声点与有效点的精确标示。基于不同类型噪声的点云数据实验结果表明:与现有去噪算法相比,基于自适应移动盒子的去噪算法具有更好的适应性和去噪效果。     

An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy

The filtering of point clouds is a ubiquitous task in the processing of airborne laser scanning (ALS) data; however, such filtering processes are difficult because of the complex configuration of the terrain features. The classical filtering algorithms rely on the cautious tuning of parameters to handle various landforms. To address the challenge posed by the bundling of different terrain features into a single dataset and to surmount the sensitivity of the parameters, in this study, we propose an adaptive surface filter (ASF) for the classification of ALS point clouds. Based on the principle that the threshold should vary in accordance to the terrain smoothness, the ASF embeds bending energy, which quantitatively depicts the local terrain structure to self-adapt the filter threshold automatically. The ASF employs a step factor to control the data pyramid scheme in which the processing window sizes are reduced progressively, and the ASF gradually interpolates thin plate spline surfaces toward the ground with regularization to handle noise. Using the progressive densification strategy, regularization and self-adaption, both performance improvement and resilience to parameter tuning are achieved. When tested against the benchmark datasets provided by ISPRS, the ASF performs the best in comparison with all other filtering methods, yielding an average total error of 2.85% when optimized and 3.67% when using the same parameter set.     

Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data

Site productivity is essential information for sustainable forest management and site index (SI) is the most common quantitative measure of it. The SI is usually determined for individual tree species based on tree height and the age of the 100 largest trees per hectare according to stem diameter. The present study aimed to demonstrate and validate a methodology for the determination of SI using remotely sensed data, in particular fused airborne laser scanning (ALS) and airborne hyperspectral data in a forest site in Norway. The applied approach was based on individual tree crown (ITC) delineation: tree species, tree height, diameter at breast height (DBH), and age were modelled and predicted at ITC level using 10-fold cross validation. Four dominant ITCs per 400 m² plot were selected as input to predict SI at plot level for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.). We applied an experimental setup with different subsets of dominant ITCs with different combinations of attributes (predicted or field-derived) for SI predictions. The results revealed that the selection of the dominant ITCs based on the largest DBH independent of tree species, predicted the SI with similar accuracy as ITCs matched with field-derived dominant trees (RMSE: 27.6% vs 23.3%). The SI accuracies were at the same level when dominant species were determined from the remotely sensed or field data (RMSE: 27.6% vs 27.8%). However, when the predicted tree age was used the SI accuracy decreased compared to field-derived age (RMSE: 27.6% vs 7.6%). In general, SI was overpredicted for both tree species in the mature forest, while there was an underprediction in the young forest. In conclusion, the proposed approach for SI determination based on ITC delineation and a combination of ALS and hyperspectral data is an efficient and stable procedure, which has the potential to predict SI in forest areas at various spatial scales and additionally to improve existing SI maps in Norway.     

车载激光点云道路边界提取的Snake方法

针对车载激光点云中道路边界提取困难,自动化程度低的问题,提出一种基于离散点Snake的车载激光点云道路边界提取方法。不同于传统基于图像建立Snake,本文直接基于离散点建立Snake模型。先利用伪轨迹点数据,确定初始轮廓位置,参数化不同类型的道路边界初始轮廓;然后基于离散点构建适合多类型道路边界的Snake模型,定义模型内部、外部和约束能量,通过能量函数最小化推动轮廓曲线移动到显著道路边界特征点处,实现不同道路边界的精细提取。本文试验采用3份不同城市场景的车载激光点云数据验证本文方法的有效性,道路边界提取结果的准确率达到97.62%,召回率达到98.04%,F1-Measure值达到97.83%以上,且提取的道路边界结果与软件交互提取的结果有较好的吻合度。试验结果表明,本文方法能够修正噪声、断裂等数据质量对道路边界提取的影响,能够实现各类复杂城市环境中不同形状道路边界的提取,具有较强的稳健性和适用性。     

Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains,California

Improved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth modeling. Airborne Laser Scanning (ALS) can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy terrains. This study proposed an ALS-based framework to quantify tree growth and competition. Bi-temporal ALS data were used to quantify tree growth in height (ΔH), crown area (ΔA), crown volume (ΔV), and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada forests. We analyzed the correlations between tree growth attributes and controlling factors (i.e. tree sizes, competition, forest structure, and topographic parameters) at multiple levels. At the individual tree level, ΔH had no consistent correlations with controlling factors, ΔA and ΔV were positively related to original tree sizes (R?>?0.3) and negatively related to competition indices (R?R|?>?0.7), ΔV was positively related to original tree sizes (|R|?>?0.8). Multivariate regression models were simulated at individual tree level for ΔH, ΔA, and ΔV with the R² ranged from 0.1 to 0.43. The ALS-based tree height estimation and growth analysis results were consistent with field measurements.     

A hybrid framework for single tree detection from airborne laser scanning data: A case study in temperate mature coniferous forests in Ontario,Canada

This study presents a hybrid framework for single tree detection from airborne laser scanning (ALS) data by integrating low-level image processing techniques into a high-level probabilistic framework. The proposed approach modeled tree crowns in a forest plot as a configuration of circular objects. We took advantage of low-level image processing techniques to generate candidate configurations from the canopy height model (CHM): the treetop positions were sampled within the over-extracted local maxima via local maxima filtering, and the crown sizes were derived from marker-controlled watershed segmentation using corresponding treetops as markers. The configuration containing the best possible set of detected tree objects was estimated by a global optimization solver. To achieve this, we introduced a Gibbs energy, which contains a data term that judges the fitness of the objects with respect to the data, and a prior term that prevents severe overlapping between tree crowns on the configuration space. The energy was then embedded into a Markov Chain Monte Carlo (MCMC) dynamics coupled with a simulated annealing to find its global minimum. In this research, we also proposed a Monte Carlo-based sampling method for parameter estimation. We tested the method on a temperate mature coniferous forest in Ontario, Canada and also on simulated coniferous forest plots with different degrees of crown overlap. The experimental results showed the effectiveness of our proposed method, which was capable of reducing the commission errors produced by local maxima filtering, thus increasing the overall detection accuracy by approximately 10% on all of the datasets.     

基于激光反射率的点云与图像自动融合

提出一个基于激光反射率的点云图像自动融合算法,数字图像与点云反射率图像特征点匹配,得到数字图像特征点像点坐标和相应物点物方空间坐标对,采用直接线性变换算法(DLT)建立点云数据反投影至二维图像点的投影模型,实现点云图像自动融合,得到目标表面的全面信息,包括物体表面三维坐标和纹理信息。自动地实现点云图像融合,无需人工参与,提高效率和准确率,同时结合实验验证该算法的可行性。     

激光雷达数据的建筑物光伏潜力评估

针对如何规模化、定量化评估城市建筑物尺度光伏发电潜力,充分、有效利用太阳能资源,实现建筑物能源自给自足目标的问题,该文通过利用机载激光雷达数据,批量获取建筑物屋顶相关信息,借助三维立体模型确定太阳能板的安装区域,进行区域建筑物表面光伏发电潜力评估。以山东建筑大学校园内13栋建筑物屋顶为例,基于ArcGISPro提供的太阳辐射分析工具获取实验区域2016年逐月太阳辐射数据。实验结果显示,该区域年均太阳辐射强度为1160.7kW·h/m2,通过实例分析验证了基于建筑物表面的光伏发电容量在建筑物能源供给中具有重要作用。同时,文章对实验区域建筑物光伏发电潜力月度容量进行分析,得出一年中的高峰时段为5—7月,而冬季的11月份到次年2月份是低谷期。文章利用机载激光雷达数据批量获取建筑物屋顶定量信息,对区域建筑物尺度光伏发电潜力评估做了初步的尝试及若干思考,实践证明该方法是引导太阳能资源开发利用的一种有效途径。     

Building roof modeling from airborne laser scanning data based on level set approach

This paper presents a novel approach to building roof modeling, including roof plane segmentation and roof model reconstruction, from airborne laser scanning data. Segmentation is performed by minimizing an energy function formulated as multiphase level set. The energy function is minimized when each segment corresponds to one or several roof plans of the same normal vector. With this formulation, maximum n regions are segmented at a time by applying log₂n level set functions. The roof ridges or step edges are then delineated by the union of the zero level contours of the level set functions. In the final step of segmentation, coplanar and parallel roof segments are separated into individual roof segments based on their connectivity and homogeneity. To reconstruct a 3D roof model, roof structure points are determined by intersecting adjacent roof segments or line segments of building boundary and then connected based on their topological relations inferred from the segmentation result. As a global solution to the segmentation problem, the proposed approach determines multiple roof segments at the same time, which leads to topological consistency among the segment boundaries. The paper describes the principle and solution of the multiphase level set approach and demonstrates its performance and properties with two airborne laser scanning data sets.     

Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis

The aim of this study is to present an automatic approach for olive tree dendrometric parameter estimation from airborne laser scanning (ALS) data. The proposed method is based on a unique combination of the alpha-shape algorithm applied to normalized point cloud and principal component analysis. A key issue of the alpha-shape algorithm is to define the α parameter, as it directly affects the crown delineation results. We propose to adjust this parameter based on a group of representative trees in an orchard for which the classical field measurements were performed. The best value of the α parameter is one whose correlation coefficient of dendrometric parameters between field measurements and estimated values is the highest. We determined crown diameters as principal components of ALS points representing a delineated crown. The method was applied to a test area of an olive orchard in Spain. The tree dendrometric parameters estimated from ALS data were compared with field measurements to assess the quality of the developed approach. We found the method to be equally good or even superior to previously investigated semi-automatic methods. The average error is 19% for tree height, 53% for crown base height, and 13% and 9% for the length of the longer diameter and perpendicular diameter, respectively.     

基于三维激光扫描仪的校园建筑物建模研究

三维激光扫描仪可以连续、自动、快速获取目标物表面的采样点数据。论述三维激光扫描仪工作原理、数据处理流程。以校园建筑物为例,给出三维数据获取、数据处理、模型建立的基本方法和结果。探讨采用点云数据进行数字校园的方法。     

利用机载LiDAR点云提取复杂城市建筑物面域

机载LiDAR技术为探测建筑物提供了大量三维点云坐标.为了能从植被中有效识别建筑物面域,首先利用渐进式TIN加密法识别非地面点云,经过移除低于地面3 m的点云和孤立点云后生成菲地面点云的二值化格网,依据自定义的分割算子打断建筑物和植被间的可能连接;然后通过区域生成算法以高差阈值来聚类二者的面域,并使用大坡度密度阈值来提取建筑物的面域;最后使用形态学闭算子填充面域孔洞并平滑其边缘.选取3个典型的复杂城市区域进行测试,结果显示,各区域的提取质量与完成率均高于91％,表明该算法能够达到自动识别建筑物的目的.     

基于机载激光扫描数据提取建筑物的研究现状

机载激光扫描系统是集成了GPS、惯性导航系统(INS)和扫描激光测距系统并利用飞机作为运行平台,来获取地面的三维位置,进而快速生成数字表面模型(DSM)。随着机载扫描激光测距系统的不断完善和发展,获取城市DSM数据也变得越来越快速,而且方便和经济可靠,地面激光点的密度也大大提高。目前国外激光扫描系统的激光点密度一般都达到了1～20点/m2,因此利用机载激光扫描系统获取的城市DSM提取建筑物也渐渐受到重视。利用激光扫描数据提取建筑物可以分为两大类,第一类是单纯以获取的机载激光测距数据来提取建筑物,第二类是融合激光测距数据和其他相关信息的建筑物提取,如融合航空影像、融合IKONOS高分辨率卫星影像来提取建筑物。本文对国际上利用机载激光扫描测距数据进行建筑物提取的最新研究进展进行了一些分析,同时也给出了应用我国研制的机载激光扫描数据提取建筑物的试验研究和初步结果。     

摄影测量激光点云空洞修补

为修补三维激光点云中的复杂空洞,本文提出了一种基于摄影测量原理的修补算法。与常规利用空洞与空洞周围点云的关系修补空洞的方法相比,该方法对于复杂空洞的修补可靠性更高,通用性更强。实验结果表明,该空洞修补算法适用于大面积、复杂的点云空洞,能较好地保持原三维物体的细节特征。     

先验知识引导的车载激光扫描点云道路信息提取

文章研究利用先验知识从车载激光扫描点云数据中提取道路信息:使用改进的不规则三角网渐进加密滤波方法对点云滤波;将地面点投影到水平面上,利用行车轨迹提取道路主轴线;然后生成高程差分特征图像,以主轴线为基准进行平面生长获得道路面;并根据反射强度,进一步获得道路边界和道路标识线等信息.最后使用两景车载激光扫描点云数据验证了方法的可行性和有效性.