Assessment of long-range kinematic GPS positioning errors by comparison with airborne laser altimetry and satellite altimetry

Long-range airborne laser altimetry and laser scanning (LIDAR) or airborne gravity surveys in, for example, polar or oceanic areas require airborne kinematic GPS baselines of many hundreds of kilometers in length. In such instances, with the complications of ionospheric biases, it can be a real challenge for traditional differential kinematic GPS software to obtain reasonable solutions. In this paper, we will describe attempts to validate an implementation of the precise point positioning (PPP) technique on an aircraft without the use of a local GPS reference station. We will compare PPP solutions with other conventional GPS solutions, as well as with independent data by comparison of airborne laser data with “ground truth” heights. The comparisons involve two flights: A July 5, 2003, airborne laser flight line across the North Atlantic from Iceland to Scotland, and a May 24, 2004, flight in an area of the Arctic Ocean north of Greenland, near-coincident in time and space with the ICESat satellite laser altimeter. Both of these flights were more than 800 km long. Comparisons between different GPS methods and four different software packages do not suggest a clear preference for any one, with the heights generally showing decimeter-level agreement. For the comparison with the independent ICESat- and LIDAR-derived “ground truth” of ocean or sea-ice heights, the statistics of comparison show a typical fit of around 10 cm RMS in the North Atlantic, and 30 cm in the sea-ice region north of Greenland. Part of the latter 30 cm error is likely due to errors in the airborne LIDAR measurement and calibration, as well as errors in the “ground truth” ocean surfaces due to drifting sea-ice. Nevertheless, the potential of the PPP method for generating 10 cm level kinematic height positioning over long baselines is illustrated.     

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.     

利用机载LIDAR双次回波高程之差分类激光脚点

机载LIDAR技术已经引起了测绘界的浓厚兴趣,有可能给测绘领域带来一场新的技术革命。机载LI-DAR技术的硬件设备在国外已相对成熟,而机载LIDAR的数据后处理算法仍然处于研究发展阶段,还有诸多问题没有得到解决,其关键之一就是机载LIDAR数据的滤波与分类。本文首先对已有的滤波分类方法进行了综合评价,并指出了各自的局限。然后提出利用两次回波信号的高程数据来实现对机载LIDAR数据的分类。首次分类后得到植被激光脚点点集和地面及房屋激光脚点点集。而房屋上的激光脚点要高出地面上的激光脚点数米之多,简单利用阈值法就可以进一步分类出房屋激光脚点和地面激光脚点。也可以先经过滤波处理将地面激光脚点去掉,然后利用两次回波信号的高程数据来分类自然植被激光脚点和人工地物激光脚点。实验证明所提方法简单有效,算法简单实用,特别适用于分类植被激光脚点。     

LIDAR Data Filtering and DTM Interpolation Within GRASS

LIDAR (Light Detection and Ranging) is one of the most recent technologies in surveying and mapping. LIDAR is based on the combination of three different data collection tools: a laser scanner mounted on an aircraft, a Global Positioning System (GPS) used in phase differential kinematic modality to provide the sensor position and an Inertial Navigation System (INS) to provide the orientation. The laser sends towards the ground an infrared signal, which is reflected back to the sensor. The time employed by the signal, given the aircraft position and attitude, allows computation of the earth point elevation. In standard conditions, taking into account the flight (speed 200–250 km/hour, altitude 500–2,000 m) and sensor characteristics (scan angle ± 10–20 degrees, emission rate 2,000–50,000 pulses per second), earth elevations are collected within a density of one point every 0.5–3 m. The technology allows us therefore to obtain very accurate (5–20 cm) and high resolution Digital Surface Models (DSM). For many applications, the Digital Terrain Model (DTM) is needed: we have to automatically detect and discard from the previous DSM all the features (buildings, trees, etc.) present on the terrain. This paper describes a procedure that has been implemented within GRASS to construct DTMs from LIDAR source data.     

Spatial Scale Management Experiments Using Optical Aerial Imagery and LIDAR Data Synergy

Computational trends toward shared services suggest the need to automatically manage spatial scale for overlapping applications. In three experiments using high-spatial-resolution optical imagery and LIDAR data to extract impervious, forest, and herbaceous classes, this study optimized C5.0 rule sets according to: (1) spatial scale within an image tile; (2) spatial scale within spectral clusters; and (3) stability of predicted accuracies based on cross validation. Alteration of the image segmentation scale parameter affected accuracy as did synergy with LIDAR derivatives. Within the tile examined, forest and herbaceous areas benefited more from optical and LIDAR synergy than did impervious surfaces.     

关于新一代激光雷达系统

激光雷达系统配备有当今世界上最高分辨率的彩色数码相机和多光谱航拍相机。它同时集常用测量装置和全球定位装置于一身 ,当飞机机载该装置飞越地球表面时 ,它们能在获取所需图像及数据的同时 ,计算出传感器的精确位置和取向 ,给出数字高程图 (DEM)及全数码彩色影像。数码相机的底部配备着一个超大像幅的CCD传感器。该激光雷达系统还配置有一个高分辨率多光谱成形相机 ,因此多谱段像素直接与XYZ坐标值相对应 ,得以自动清晰地区分道路、建筑、树林、河流和其他地貌     

An Automatic Registration Method for Adjustment of Relative Elevation Discrepancies between Lidar Data Strips

Despite the recent development in light detection and ranging (LIDAR) systems, discrepancies between strips in overlapping areas persist because of systematic errors. During the past decade, several methods have been developed for compensating for errors, such as checking the coincidence of conjugate features extracted from overlapping LIDAR strips or comparing interpolated range and intensity images. However, these approaches rely upon the ability to detect and extract suitable conjugate features within the overlap area and/or during the preprocessing of raw LIDAR data in, for example, interpolation or segmentation. Such procedures make the overall process complex and may impose limitations on the development of an automated method. Furthermore, some of the preprocessing techniques, such as raster interpolation, may induce errors in raw LIDAR data when dealing with large-scale coverage over an urban area. This paper therefore presents an automated approach, working with raw LIDAR data without the restrictions associated with using conjugate features and without any preprocessing. We present an approach using changes in local height variations that occur within the overlap area between two neighboring strips. In this case, local height variations of the LIDAR data in the overlap area increase if there are discrepancies. This scheme can be helpful in determining an appropriate transformation for the adjustment of discrepancies between neighboring LIDAR strips in a way that minimizes the local height variation. A contour tree (CT) was used to represent the local height variations and to find an appropriate initial transformaunction. The iterative closest point (ICP) procedure was then applied to refine the function parameters. Following transformation, LIDAR strips were registered with each other, and the discrepancies were measured again to determine whether they had been resolved. The statistical evaluation of the results revealed that the discrepancies were significantly reduced.     

Building extraction from LIDAR based semantic analysis

Extraction of buildings from LIDAR data has been an active research field in recent years. A scheme for building detection and reconstruction from LIDAR data is presented with an object-oriented method which is based on the buildings' semantic rules. Two key steps are discussed: how to group the discrete LIDAR points into single objects and how to establish the buildings' semantic rules. In the end, the buildings are reconstructed in 3D form and three common parametric building models (flat, gabled, hipped) are implemented.     

机载LIDAR点云高程数据精度检核及误差来源分析

利用GPSRTK系统对点云高程数据进行质量检核,通过对检核数据的分析,得出实际测量中不同地形情况下LIDAR测高数据能达到的精度,并分析了误差的来源。     

Baltic Sea Level project with GPS

In order to study the Baltic Sea Level change and to unify national height systems a two week GPS campaign was performed in the region in Autumn 1990. Parties from Denmark, Finland, Germany, Poland and Sweden carried out GPS measurements at 26 tide gauges along the Baltic sea and 8 VLBI and SLR fiducial stations with baseline lengths ranging from 230 km to 1600 km. The observations were processed in the network mode with the Bernese version 3.3 software using orbit improvement techniques. To get rid of the scale error introduced by the ionospheric refraction from single-frequency data, the local models of the ionosphere were estimated using L4 observations. The tropospheric zenith corrections were also considered. The preliminary results show average root mean square (RMS) errors of about ±3 cm in the horizontal position and ±7 cm in the vertical position relative to the Potsdam SLR station in ITRF89 system. After transformation of the GPS results to geoid heights using the levelled heights, an absolute comparison with gravimetric geoid heights using the least squares modification of Stokes' formula (LSMS), the modified Molodensky and the NKG Scandinavian geoid 1989 (NGK-89) models gives a standard deviation of the difference of ±7cm to ±9cm for the NKG-89 model and of ±9cm to ±30cm for the LSMS and the modified Molodensky model. The Swedish height system is found to be about 8-37cm higher than those of the other Baltic countries for NKG-89 model.     

Building Extraction from LIDAR Based Semantic Analysis

IntroductionThanks to a large number of applications ,suchas map updating, urban planning or land useanalysis ,extraction of artificial objects ,such asbuildings ,roads fromthei mages has been an ac-tive research field for many years . Since build-ings ar…     

机载LIDAR技术的优势及应用前景研究

机载三维激光雷达测量(以下简称机载LIDAR—Light Detection and Ranger)技术是继GPS以来在测绘领域的又一场技术革命,它以高精度、高密度、高效率、产品丰富等特点在测绘领域得到较好的应用和广泛的关注,具有巨大的发展空间和潜力,它代表了测绘领域又一个新时代的到来。本文着重介绍了机载LIDAR的测量原理、技术特点,进而总结出机载LIDAR技术优势和应用前景。     

LIDAR数据的过滤方法探讨

过滤是LIDAR数据预处理的重要步骤,过滤的目的是把LIDAR数据分成地面点和非地面点,它是获取高精度数字高程模型的关键。详细分析、研究了常用的LIDAR数据过滤方法,并对其中的最大局部倾斜度过滤方法进行了一定的改进。     

3D segmentation of single trees exploiting full waveform LIDAR data

This paper highlights a novel segmentation approach for single trees from LIDAR data and compares the results acquired both from first/last pulse and full waveform data. In a first step, a conventional watershed-based segmentation procedure is set up, which robustly interpolates the canopy height model from the LIDAR data and identifies possible stem positions of the tallest trees in the segments calculated from the local maxima of the canopy height model. Secondly, this segmentation approach is combined with a special stem detection method. Stem positions in the segments of the watershed segmentation are detected by hierarchically clustering points below the crown base height and reconstructing the stems with a robust RANSAC-based estimation of the stem points. Finally, a new three-dimensional (3D) segmentation of single trees is implemented using normalized cut segmentation. This tackles the problem of segmenting small trees below the canopy height model. The key idea is to subdivide the tree area in a voxel space and to set up a bipartite graph which is formed by the voxels and similarity measures between the voxels. Normalized cut segmentation divides the graph hierarchically into segments which have a minimum similarity with each other and whose members (= voxels) have a maximum similarity. The solution is found by solving a corresponding generalized eigenvalue problem and an appropriate binarization of the solution vector. Experiments were conducted in the Bavarian Forest National Park with conventional first/last pulse data and full waveform LIDAR data. The first/last pulse data were collected in a flight with the Falcon II system from TopoSys in a leaf-on situation at a point density of 10 points/m². Full waveform data were captured with the Riegl LMS-Q560 scanner at a point density of 25 points/m² (leaf-off and leaf-on) and at a point density of 10 points/m² (leaf-on). The study results prove that the new 3D segmentation approach is capable of detecting small trees in the lower forest layer. So far, this has been practically impossible if tree segmentation techniques based on the canopy height model were applied to LIDAR data. Compared to a standard watershed segmentation procedure, the combination of the stem detection method and normalized cut segmentation leads to the best segmentation results and is superior in the best case by 12%. Moreover, the experiments show clearly that using full waveform data is superior to using first/last pulse data.     

机载LIDAR系统的回波探测原理及其测高精度分析

从回波探测的原理和激光测高的计算模型出发,分析了激光测高的误差来源,认为主要影响因子是GPS差分精度、IMU精度和激光测距精度。通过对航高在1000m以下的实际检测数据进行统计分析,得出如下结论:1)单个激光点的精度可以达到15cm,通过地面改正,最优精度可以达到5cm;2)在植被比较密集区域,根据激光点的扫面密度不同,其生成的DEM精度可以达到0.20m～1m。     

快速获取地面三维数据的LIDAR技术系统

机载激光扫描获取地面三维数据的 L IDAR系统是最近几年出现的高新技术系统之一。L IDAR系统是一个先进的主动传感系统 ,该系统本身发射受控制的激光以照射地面和地面上的目标 ,不依赖太阳光照 ,所以它是一个全天时日夜可以获得地面三维数据的系统。它直接获取地面三维数据比传统测量方法具有高精度、高密集、高效率和成本低的优点。该系统强大的后处理技术可以把地面和其上的植被、建筑物分离开来 ,可同时取得 DTM、DSM和植被参数     

First Results for the GPS Survey of South Africa Tide Gauges

Tide gauge measurements are used for a variety of scientific purposes, not least of which are the definition of vertical data and the detection of long-term variations in mean sea level. GPS measurements at tide gauge sites provide a means of separating local verticl motions from sea level rise, and a means of unifying vertical data in a single reference system. This paper describes a GPS survey to determine the positions and heights of reference stations at South African tide gauge sites. The data were processed in baseline mode using a commercial software package. The heights of the tide gauge stations relative to the fixed ITRF reference station HRAO were determined at a precision of around 3 cm – better than 0.1 ppm. Analysis of the error sources showes that use of the precise ephemeris contributed to a substantial improvement in accuracy, as did the use of ionosphere-free fixed integer baseline solutions. Variations in the antenna phase centers also contributed significant changes in height. ? 2001 John Wiley & Sons, Inc.     

Mutual validation of GNSS height measurements and high-precision geometric-astronomical leveling

The method of geometric-astronomical leveling is presented as a suited technique for the validation of GNSS (Global Navigation Satellite System) heights. In geometric-astronomical leveling, the ellipsoidal height differences are obtained by combining conventional spirit leveling and astronomical leveling. Astronomical leveling with recently developed digital zenith camera systems is capable of providing the geometry of equipotential surfaces of the gravity field accurate to a few 0.1 mm per km. This is comparable to the accuracy of spirit leveling. Consequently, geometric-astronomical leveling yields accurate ellipsoidal height differences that may serve as an independent check on GNSS height measurements at local scales. A test was performed in a local geodetic network near Hanover. GPS observations were simultaneously carried out at five stations over a time span of 48 h and processed considering state-of-the-art techniques and sophisticated new approaches to reduce station-dependent errors. The comparison of GPS height differences with those from geometric-astronomical leveling shows a promising agreement of some millimeters. The experiment indicates the currently achievable accuracy level of GPS height measurements and demonstrates the practical applicability of the proposed approach for the validation of GNSS height measurements as well as the evaluation of GNSS height processing strategies.     

The Geoid Slope Validation Survey 2014 and GRAV-D airborne gravity enhanced geoid comparison results in Iowa

Three Geoid Slope Validation Surveys were planned by the National Geodetic Survey for validating geoid improvement gained by incorporating airborne gravity data collected by the “Gravity for the Redefinition of the American Vertical Datum” (GRAV-D) project in flat, medium and rough topographic areas, respectively. The first survey GSVS11 over a flat topographic area in Texas confirmed that a 1-cm differential accuracy geoid over baseline lengths between 0.4 and 320 km is achievable with GRAV-D data included (Smith et al. in J Geod 87:885–907, 2013). The second survey, Geoid Slope Validation Survey 2014 (GSVS14) took place in Iowa in an area with moderate topography but significant gravity variation. Two sets of geoidal heights were computed from GPS/leveling data and observed astrogeodetic deflections of the vertical at 204 GSVS14 official marks. They agree with each other at a \({\pm }1.2\,\, \hbox {cm}\) level, which attests to the high quality of the GSVS14 data. In total, four geoid models were computed. Three models combined the GOCO03/5S satellite gravity model with terrestrial and GRAV-D gravity with different strategies. The fourth model, called xGEOID15A, had no airborne gravity data and served as the benchmark to quantify the contribution of GRAV-D to the geoid improvement. The comparisons show that each model agrees with the GPS/leveling geoid height by 1.5 cm in mark-by-mark comparisons. In differential comparisons, all geoid models have a predicted accuracy of 1–2 cm at baseline lengths from 1.6 to 247 km. The contribution of GRAV-D is not apparent due to a 9-cm slope in the western 50-km section of the traverse for all gravimetric geoid models, and it was determined that the slopes have been caused by a 5 mGal bias in the terrestrial gravity data. If that western 50-km section of the testing line is excluded in the comparisons, then the improvement with GRAV-D is clearly evident. In that case, 1-cm differential accuracy on baselines of any length is achieved with the GRAV-D-enhanced geoid models and exhibits a clear improvement over the geoid models without GRAV-D data. GSVS14 confirmed that the geoid differential accuracies are in the 1–2 cm range at various baseline lengths. The accuracy increases to 1 cm with GRAV-D gravity when the west 50 km line is not included. The data collected by the surveys have high accuracy and have the potential to be used for validation of other geodetic techniques, e.g., the chronometric leveling. To reach the 1-cm height differences of the GSVS data, a clock with frequency accuracy of \(10^{-18}\) is required. Using the GSVS data, the accuracy of ellipsoidal height differences can also be estimated.