Optimal use of high‐resolution topographic data in flood inundation models

In this paper we explore the optimum assimilation of high‐resolution data into numerical models using the example of topographic data provision for flood inundation simulation. First, we explore problems with current assimilation methods in which numerical grids are generated independent of topography. These include possible loss of significant length scales of topographic information, poor representation of the original surface and data redundancy. These are resolved through the development of a processing chain consisting of: (i) assessment of significant length scales of variation in the input data sets; (ii) determination of significant points within the data set; (iii) translation of these into a conforming model discretization that preserves solution quality for a given numerical solver; and (iv) incorporation of otherwise redundant sub‐grid data into the model in a computationally efficient manner. This processing chain is used to develop an optimal finite element discretization for a 12 km reach of the River Stour in Dorset, UK, for which a high‐resolution topographic data set derived from airborne laser altimetry (LiDAR) was available. For this reach, three simulations of a 1 in 4 year flood event were conducted: a control simulation with a mesh developed independent of topography, a simulation with a topographically optimum mesh, and a further simulation with the topographically optimum mesh incorporating the sub‐grid topographic data within a correction algorithm for dynamic wetting and drying in fixed grid models. The topographically optimum model is shown to represent better the ‘raw’ topographic data set and that differences between this surface and the control are hydraulically significant. Incorporation of sub‐grid topographic data has a less marked impact than getting the explicit hydraulic calculation correct, but still leads to important differences in model behaviour. The paper highlights the need for better validation data capable of discriminating between these competing approaches and begins to indicate what the characteristics of such a data set should be. More generally, the techniques developed here should prove useful for any data set where the resolution exceeds that of the model in which it is to be used. Copyright © 2002 John Wiley & Sons, Ltd.     

基于移动车载测量模式下的测绘一体化应用

介绍了一种以机动车为设备搭载和作业平台,搭载测绘保障通用设备、卫星导航、数据传输和测量数据处理系统,实现数据采集、传输、处理、检核、发布与服务的测量内外业一体化应用,改变了传统测绘技术手段单一、分散及内外业相互脱节的作业模式。     

Development of a Vector-Based Method for Coastal Bluffline Mapping Using LiDAR Data and a Comparison Study in the Area of Lake Erie

This paper introduces a new method for bluffline extraction based on analysis of surface normal vectors derived from a 2.5-dimensional Delaunay triangulation network generated using a set of LiDAR points. The developed method has been implemented and tested for the extraction of blufflines in a section of the Lake Erie coastline near Painesville, Ohio. Comparison of the research results with those generated by a method for bluffline extraction based on slope information along elevation profiles on transects shows an improvement in both horizontal and vertical accuracies in situations where the bluff has a relatively sharp edge formation.     

增量型编码器结合GPS在道路检测车中的应用

介绍了增量型编码器以及GPS的工作原理及性能参数,阐述了两者在道路检测车中的使用方法和理论依据,得出了两者同时使用能有较高的测量精度的结果,同时也指出了两者的不足,为后续的改进提供参考。     

无人机载双拼相机低空航测系统技术研究

目前国内大多数无人机航测系统所采用的单相机平面精度可达到要求,但高程精度无法满足大比例尺测图要求。本文主要介绍一种无人机载双拼组合宽角相机低空航测系统,其组成结构、双拼组合宽角数码相机原理、数据处理软件,以及利用该系统进行大比例尺测图的生产流程。它与单相机系统的最大不同点是,通过双拼组合扩大了成像系统的旁向视场角,使得在等同航高条件下,航带影像地面覆盖宽度增加一倍,从而达到提高效率的目的。     

基于TIN渐次加密的LiDAR点云数据滤波

传统的滤波算法通常是针对具有连续表面的简单区域来进行,因此带有一定的局限性,且不能解决复杂城区地形准确提取的难题。因此,本文提出了一种改进的基于TIN渐次加密的LiDAR点云数据滤波方法,该方法先对原始点云利用多尺度虚拟网格筛选地面种子点;然后,对种子点构建初始TIN表面,在此基础上进行向上加密;最后,得到的TIN三角网则为真实地形表面。实验结果表明,该方法能有效地滤除建筑物、植被和其他地物,并较好地保持地形特征。     

Aboveground total and green biomass of dryland shrub derived from terrestrial laser scanning

Sagebrush (Artemisia tridentata), a dominant shrub species in the sagebrush-steppe ecosystem of the western US, is declining from its historical distribution due to feedbacks between climate and land use change, fire, and invasive species. Quantifying aboveground biomass of sagebrush is important for assessing carbon storage and monitoring the presence and distribution of this rapidly changing dryland ecosystem. Models of shrub canopy volume, derived from terrestrial laser scanning (TLS) point clouds, were used to accurately estimate aboveground sagebrush biomass. Ninety-one sagebrush plants were scanned and sampled across three study sites in the Great Basin, USA. Half of the plants were scanned and destructively sampled in the spring (n = 46), while the other half were scanned again in the fall before destructive sampling (n = 45). The latter set of sagebrush plants was scanned during both spring and fall to further test the ability of the TLS to quantify seasonal changes in green biomass. Sagebrush biomass was estimated using both a voxel and a 3-D convex hull approach applied to TLS point cloud data. The 3-D convex hull model estimated total and green biomass more accurately (R² = 0.92 and R² = 0.83, respectively) than the voxel-based method (R² = 0.86 and R² = 0.73, respectively). Seasonal differences in TLS-predicted green biomass were detected at two of the sites (p < 0.001 and p = 0.029), elucidating the amount of ephemeral leaf loss in the face of summer drought. The methods presented herein are directly transferable to other dryland shrubs, and implementation of the convex hull model with similar sagebrush species is straightforward.     

A dual quaternion-based,closed-form pairwise registration algorithm for point clouds

The representation of similarity transformation in three-dimensional (3D) space, especially of orientation, is a crucial issue in navigation, geodesy, photogrammetry, robot arm manipulation, etc. Considering the large amount of computer resources required by iterative algorithms designed for spatial similarity transformation, the high dependence on initial values of unknown parameters, and the instability of solving transformation parameters for large-angle registration, a closed-form solution for pairwise light detection and ranging (LiDAR) point cloud registration is proposed. In this solution, dual-number quaternions are used to represent the 3D rotation. The relationship between the rotation matrix-based representation of similarity transformation and the dual quaternion-based representation is described first. Considering that the same features from two neighboring stations coincide after pairwise registration, a dual quaternion-based error norm, which is associated with the sum of the position errors, is constructed. Based on theory of least squares and by extreme value analysis of the error norm, detailed derivations of the model and the main formulas are obtained. Once the similarities between the same features from the two neighboring LiDAR stations are constructed, the rotation matrix, the scale parameter, and the translation vector are simultaneously derived. Two experiments are conducted to verify the feasibility and effectiveness of the proposed algorithm. The proposed algorithm has the advantages of simplicity and ease of implementation, making it better than the traditional methods that use matrices to describe spatial rotation. Moreover, it solves the transformation parameters without the initial estimates of unknown parameters, making it better than iterative algorithms. Most importantly, in contrast to unit quaternion-based algorithms, the proposed algorithm solves seven unknown parameters simultaneously. Therefore, it effectively avoids the accumulation of introduced error in calculation and the negative impact from the inappropriate choice of initial values.     

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.     

A large volume particulate and water multi-sampler with in situ preservation for microbial and biogeochemical studies

A new tool was developed for large volume sampling to facilitate marine microbiology and biogeochemical studies. It was developed for remotely operated vehicle and hydrocast deployments, and allows for rapid collection of multiple sample types from the water column and dynamic, variable environments such as rising hydrothermal plumes. It was used successfully during a cruise to the hydrothermal vent systems of the Mid-Cayman Rise. The Suspended Particulate Rosette V2 large volume multi-sampling system allows for the collection of 14 sample sets per deployment. Each sample set can include filtered material, whole (unfiltered) water, and filtrate. Suspended particulate can be collected on filters up to 142 mm in diameter and pore sizes down to 0.2 μm. Filtration is typically at flowrates of 2 L min⁻¹. For particulate material, filtered volume is constrained only by sampling time and filter capacity, with all sample volumes recorded by digital flowmeter. The suspended particulate filter holders can be filled with preservative and sealed immediately after sample collection. Up to 2 L of whole water, filtrate, or a combination of the two, can be collected as part of each sample set. The system is constructed of plastics with titanium fasteners and nickel alloy spring loaded seals. There are no ferrous alloys in the sampling system. Individual sample lines are prefilled with filtered, deionized water prior to deployment and remain sealed unless a sample is actively being collected. This system is intended to facilitate studies concerning the relationship between marine microbiology and ocean biogeochemistry.