美国的板块边界观测(PBO)计划

板块边界观测(PB0)计划是美国科学项目“地球透镜计划”(Earthscope)的组成部分。该计划的基本内容是沿板块边界布设由GPS、钻孔应变和激光应变观测点构成的台网，用于研究北美大陆西部板块边缘地区的变形。本文简要介绍了该计划产生的背景、拟解决的科学问题、所采用的观测手段以及管理和经费预算等情况。     

地震长期效应对黄土力学性质的影响

黄土高原地区,具有大孔隙、弱胶结特征的黄土遭遇地震荷载影响后,其内部结构受到扰动,微结构的变化会在后期物理力学性质中集中反映出来。通过动三轴试验对原状黄土试样进行模拟历史地震预处理,对处理试样依次进行饱和、固结、不排水剪切试验。结果表明:黄土试样的初始含水率为10%时,经历历史地震扰动的黄土较未扰动黄土相比,其偏应力明显增加,孔隙水压力降低,抗剪强度指标值增大3.42 kPa和2.67°;当初始含水率增加至15%时,历史地震产生的长期效应将引起后期黄土强度持续增大,增幅远远超过10%的黄土试样,黏聚力和内摩擦角达到3.98 kPa和3.02°。通过扫描电镜试验和压汞试验,从微观角度分析地震长期效应对黄土力学性质影响的内在机制,结果发现历史地震引起黄土内部颗粒移动、孔隙结构变化是黄土强度增加的重要因素。     

工业遗址风貌三维景观精细化测绘关键技术研究

以广钢新城地块工业遗址风貌区测绘为试点,将GZCORS、地面Li DAR、轻型无人机Li DAR等先进快速测绘设备有效组合,地空一体化快速测绘,实现遗址风貌区构筑物数字化快速采集、建模,丰富了测绘信息表达内容与形式,为规划决策提供新型智力支持。本文针对工业遗址历史风貌区构筑物快速数字化提供了一套完整的技术方案。     

基于移动激光扫描点云数据和遥感图像的建筑物三维模型快速建模方法

建筑物的三维建模是城市三维建模和可视化的重要组成部分。本文提出一种基于点云数据与遥感图像的建筑物三维模型快速建模方法。首先,运用改进的RANSAC法从点云数据中提取建筑立面,根据立面区分平顶建筑与人字形屋顶建筑;在此基础上,进一步对建筑物的高度进行提取;之后,利用区域增长法从遥感图像中提取建筑物屋顶轮廓,利用形态学方法对提取出的轮廓进行规则化处理,并基于Freeman链码提取轮廓角点,得到规整的轮廓;最后,根据提取出的建筑高度属性对屋顶轮廓拉伸并进行纹理映射,实现对建筑物的三维重建。通过实例证明,提出的方法能快速、高效地实现建筑物三维模型的重建。     

卫星激光反射器质心改正的概率模型

卫星激光测距通过测量激光脉冲在地面观测站和卫星之间的往返时间来计算卫星到测站的距离。激光反射器位置到卫星质心的距离即质心改正(CoM)需要精确标定,以提高卫星测距精度。卫星激光反射器的质心改正误差主要由角反射器分布效应引起,质心改正与激光束的入射角、角反射器排列结构和地面测距站位置有关。卫星角反射器对光子的反射概率与反射器的有效雷达截面积成正比,本文对角反射器的有效雷达截面面积进行拟合,建立以入射角为随机变量的概率模型,计算了球形LAGEOS-1/2的质心改正值,基于长期观测数据使用不同质心改正值进行了精密定轨,分析了其加权残差变化。同时,对BeiDou-M3的角反射器为平面阵列的情况进行了讨论,计算了质心改正值,用一个月的数据进行精密定轨。试验结果表明,基于概率理论的模型在精密轨道中与国际激光测距服务(ILRS)公布的结果相当,说明概率模型适用于球型卫星或非球型卫星。     

Detection of gullies in roughly textured terrain using airborne laser scanning data

Airborne laser scanning data contain information about surface features, some of which are of subtle form. These features are usually embedded within the terrain, and rarely form distinct shape-transition to their surroundings. While some efforts have been made in extracting linear elements from laser scanning data, attention was mostly turned to dominant elements that are very clear and distinct. We present in this paper a detection model for gullies of various dimensions using airborne laser scanning data. Gullies are regarded as one of the main landform-reshaping agents, having a pejorative effect on the environment and on regional development. They are commonly observed along receding lakes as a common response to water-level drop. The paper demonstrates how a multi-scale approach enables the extraction of various gully forms, from well developed to subtle. It then proposes an optimization driven model for handling fragmentation in the detection. Results show that using the proposed model, gully networks can be reconstructed and ∼30 cm deep features can be identified and separated from their surroundings using moderate point density data.     

Predicting individual tree attributes from airborne laser point clouds based on the random forests technique

This paper depicts an approach for predicting individual tree attributes, i.e., tree height, diameter at breast height (DBH) and stem volume, based on both physical and statistical features derived from airborne laser-scanning data utilizing a new detection method for finding individual trees together with random forests as an estimation method. The random forests (also called regression forests) technique is a nonparametric regression method consisting of a set of individual regression trees. Tests of the method were performed, using 1476 trees in a boreal forest area in southern Finland and laser data with a density of 2.6 points per m². Correlation coefficients (R) between the observed and predicted values of 0.93, 0.79 and 0.87 for individual tree height, DBH and stem volume, respectively, were achieved, based on 26 laser-derived features. The corresponding relative root-mean-squared errors (RMSEs) were 10.03%, 21.35% and 45.77% (38% in best cases), which are similar to those obtained with the linear regression method, with maximum laser heights, laser-estimated DBH or crown diameters as predictors. With random forests, however, the forest models currently used for deriving the tree attributes are not needed. Based on the results, we conclude that the method is capable of providing a stable and consistent solution for determining individual tree attributes using small-footprint laser data.     

Parameter de-correlation and model-identification in hybrid-style terrestrial laser scanner self-calibration

One of the important systematic error parameters identified in terrestrial laser scanners is the collimation axis error, which models the non-orthogonality between two instrumental axes. The quality of this parameter determined by self-calibration, as measured by its estimated precision and its correlation with the tertiary rotation angle κ of the scanner exterior orientation, is strongly dependent on instrument architecture. While the quality is generally very high for panoramic-type scanners, it is comparably poor for hybrid-style instruments. Two methods for improving the quality of the collimation axis error in hybrid instrument self-calibration are proposed herein: (1) the inclusion of independent observations of the tertiary rotation angle κ; and (2) the use of a new collimation axis error model. Five real datasets were captured with two different hybrid-style scanners to test each method’s efficacy. While the first method achieves the desired outcome of complete decoupling of the collimation axis error from κ, it is shown that the high correlation is simply transferred to other model variables. The second method achieves partial parameter de-correlation to acceptable levels. Importantly, it does so without any adverse, secondary correlations and is therefore the method recommended for future use. Finally, systematic error model identification has been greatly aided in previous studies by graphical analyses of self-calibration residuals. This paper presents results showing the architecture dependence of this technique, revealing its limitations for hybrid scanners.     

车载GPS／IMU／LS激光成像系统外方位元素的动态标定

提出一种带有微小转角修正的车载激光成像系统外方位元素标定方法.针对全球定位系统(GPS)、惯性测量单元(IMU)和激光扫描仪(LS)集成的车载激光成像系统(车载移动测绘系统),该方法使用6个微小旋转角参数和6个微小平移参数对传统标定模型参数进行修正.试验数据表明,方法可对系统的安装误差和时间同步误差进行有效改正.     

Pattern detection in airborne LiDAR data using Laplacian of Gaussian filter

Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology.However,it is still one of the unsolved problems in LiDAR data processing due to difficulty of texture and structure information extraction in unevenly sampled points.The paper analyzes the characteristics of Laplacian of Gaussian(LoG) Filter and its potential use for structure detection in LiDAR data.A feature detection method based on LoG filtering is presented and ex-perimented on the unstructured points.The method filters the elevation value(namely,z coordinate value) of each point by convo-lution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground ob-jects/features.The experiments are carried on a point cloud dataset acquired from a neighborhood area.The results demonstrate patterns detected at different scales and the relationship between standard deviation that defines LoG kernel and neighborhood size,which specifies the local area that is analyzed.