Distinguishing vegetation characteristics in a terrestriallaser scanner dataset is an interesting issue for environmental assessment. Methods for filtering vegetation points to distinguish them from ground class have been widely studied mostly on datasets derived from airborne laser scanner, less so for terrestriallaserscanners (TLS). Recent developments in terrestriallaser sensors – further ranges, faster acquisition and multiple return echoes for some models – has risen interest for surface modelling applications. The downside of TLS is that a typical dataset has a very dense cloud, with obvious side-effects on post-processing time. Here we use a scan from a sensor which provides evaluation of multiple target echoes providing with more than 70 million points on our study area. The area presents a complex set of features ranging from dense vegetation undergrowth to very steep and uneven terrain. The method consists on a first step which subsets the original points to define ground candidates by taking into account the ordinal return number and the amplitude. Next a custom progressive morphological filter (closing operation) is applied on ground candidate points using multidimensional (varying resolutions) grids and a structure element to determine cell values. Vegetation density mapping over the area is then estimated using a weighted ration of point counts in the tri-dimensional space over each cell. The overall result is a pipeline for processing TLS points clouds with minimal user interaction, producing a Digital Terrain Model (DTM), a Digital Surface Model (DSM) a vegetation density map and a derived canopy height model (CHM). Results on DTM show an accuracy (RMSE) of 0.307 m with a mean error of 0.0573 m compared to a control DTM extracted from Terrascan ’ s progressive triangulation procedure. The derived CHM was tested over 30 tree heights resulting in 27 trees having an absolute error value below 0.2 m (three were just below 0.7 m).
In this paper we present the results of the comparison between two terrestriallaserscanners (TLS), a discrete return system (Riegl LMS-Z620) and an echo-digitizing system (Riegl VZ-400), employed for the survey of a dense forested area, in the italian Alps. The site is actually undergoing a strong debate among the inhabitants and local government authorities about the exploitation of the area as a huge quarry to produce building material. The dispute originates from the uncertainty about the instability of the underlying mountain slope, which was interested in 1966 by a landslide. The whole area was surveyed with the two laserscanners on February 2011 during the vegetation dormant period. A slight different processing workflow was applied to the collected datasets: the VZ-400 scans were pre-filtered by exploiting the “calibrated relative reflectance” readings and the multi-target capability provided by this laser scanning system. Next, two different spatial filters were applied to both the resulting georeferenced 3D models, in order to eliminate as much vegetation as possible: iterative filter and a custom morphological filter, developed by the authors. Achieved results show that for both datasets, the iterative and the morphological filters perform quite well for eliminating the vegetation, though some manual editing is still required since vegetation does not feature a prevalent growing direction. Furthermore, the comparison between the number of points left in the final DTMs shows that the VZ-400 provided a one order of magnitude denser point cloud wrt. the LMS-Z620. This demonstrates that a TLS with multi-target capability can potentially provide a more detailed DTM even in presence of dense vegetation.
Systematic errors are present in laser scanning system observations due to manufacturer imperfections, wearing over time, vibrations, changing environmental conditions and, last but not least, involuntary hits. To achieve maximum quality and rigorous measurements from terrestriallaserscanners, a least squares estimation of additional calibration parameters can be used to model the a priori unknown systematic errors and therefore improve output observations. The selection of the right set of additional parameters is not trivial and requires laborious statistical analysis. Based on this requirement, this article presents an approach to determine the best set of additional parameters which provides the best mathematical solution based on a dimensionless quality index. The best set of additional parameters is the one which provides the maximum quality index (i.e. minimum value) for the group of observables, exterior orientation parameters and reference points. Calibration performance is tested using both a phase shift continuous wave scanner, FARO PHOTON 880, and a pulse-based time-of-flight system, Leica HDS3000. The improvement achieved after the geometric calibration is 30% for the former and 70% for the latter.
3D modelling of architectural structures for monitoring, conservation and restoration alterations in heritage sites has special challenges for data acquisition and processing. The accuracy of created 3D model is very important. In general, because of the complexity of the structures, 3D modelling can be time consuming and may include some difficulties. 3D terrestriallaser scanning technique is a reliable and advantageous method for reconstruction and conservation of monuments. This technique is commonly acknowledged due to its accuracy, speed and flexibility. Terrestriallaserscanners can be used for documentation of the cultural heritage for the future. But it is also important to understand the capabilities and right conditions of use and limitations of this technology.
We propose a complete methodology for the fine registration and referencing of kilo-station networks of terrestriallaser scanner data currently used for many valuable purposes such as 3D as-built reconstruction of Building Information Models (BIM) or industrial as- built mock-ups. This comprehensive target-based process aims to achieve the global tolerance below a few centimetres across a 3D network including more than 1,000 laser stations spread over 10 floors. This procedure is particularly valuable for 3D networks of indoor congested environments. In situ, the use of terrestriallaserscanners, the layout of the targets and the set-up of a topographic control network should comply with the expert methods specific to surveyors. Using parametric and reduced Gauss-Helmert models, the network is expressed as a set of functional constraints with a related stochastic model. During the post-processing phase inspired by geodesy methods, a robust cost function is minimised. At the scale of such a data set, the complexity of the 3D network is beyond comprehension. The surveyor, even an expert, must be supported, in his analysis, by digital and visual indicators. In addition to the standard indicators used for the adjustment methods, including Baarda’s reliability, we introduce spectral analysis tools of graph theory for identifying different types of errors or a lack of robustness of the system as well as in fine documenting the quality of the registration.
For all campaigns, the terrestriallaser scanner Riegl LMS-Z420i was used, which applies the time-of-flight method (Riegl GmbH, 2010) (Fig. 1a)). From the known position of the scanner, the position of targets is calculated by measuring the distance through the time shift between transmitting and receiving a pulsed signal and the respective direction. The laser beam is generated in the bottom of the device with a measurement rate of up to 11,000 points/sec. Parallel scan lines are achieved with a rotating multi-facet polygon mirror and the rotation of the scanners head. Thereby a wide field of view can be achieved, up to 80° in vertical and 360° in horizontal direction. Furthermore, a digital camera, Nikon D200, was mounted on the laser scanner. From the recorded RGB-images, the point clouds recorded by the scanner can be colorized and the corresponding surfaces can be textured.
The calibrated intensities of plots 1 and 2 (the intensity at each test plot is shown in the graphs in Figs. 4 and 5) reacted more strongly to the changes in the snow crystals and brightness than the intensity of plot 3, which was further away from the laser scanner. This is because the distance effect causes the intensities to decrease further from the scanner. An increase in the incidence angle also decreases the intensity values, which decreases the relative effect of the surface properties. The effect of incidence angle on TLS intensity from snow is presented in Fig. 8 for different snow types. There is large variation in both air temperatures and snow grain properties between each date: snow grain size ranging from 0.5 mm to several millimetres, and the grain shapes from hexagonal (Nov 22) to strongly rounded an aggregated grains at the wet conditions (Jan 10, Jan 18). While the intensity in wet conditions (Jan 10 and Jan 18 measurements) appears slightly lower, the effect of incidence angle does not show a strong dependence on snow grain properties (and hence the snow type). More data including measurements with smaller incidence angles is needed to better understand the effect of incidence angle on the intensity of the laser backscattering from different snow surfaces.
For the investigation two different acquisition systems were applied, active and passive methods. The terrestriallaser scanner FARO Focus 3D was used as active sensor, working at the wavelength of 905 nm. For the case of passive sensors, a Nikon D-5000 and a 6- bands Mini-MCA multispectral camera (530-801 nm) were applied covering visible and near infrared spectral range. This analysis allows assessing the sensor, or sensors combination, suitability for pathologies detection, addressing the limitations according to the spatial and spectral resolution. Moreover, the pathology detection by unsupervised classification methods is addressed in order to evaluate the automation capability of this process.
“During the Byzantine period, the Land Walls had already undergone several transformations - an indicator of their dynamic relationship with the urban fabric of the city and with the larger transformations of the Byzantine state. Furthermore, during the Ottoman conquest of the city, the Land Walls continued serving the capital as its urban limits and defenses. In the statement of the Outstanding Universal Value (OUV), the site was described as ‘the area along both sides of the Theodosian land walls including remains of the former Blachernae Palace’. Moreover, in the OUV, it was underlined that “the 6,650 meter terrestrial wall of Theodosius II with its second line of defense, created in 447, was one of the leading references for military architecture”” (Çorakba ş , et al., 2014).
Compared to the typical dynamics of a road vehicle mounted Mobile Mapping system, a dedicated rail mapping system operates under conditions that are much more challenging for a high accuracy GNSS/IMU trajectory determination. Furthermore, the typical rail mapping tasks, like the exact measurement of the rail track geometry, require the operation of the most accurate laserscanners and of specialized post-processing software.
the instrument can be measured. Many architectural elements are designed to produce shadows which contribute to the character of the work. A complete survey of their geometry would often re- quire an impractical number of stations. Capitals, pediments and mouldings are particularly difficult elements to measure. Another limitation is related to the sampling process. Laserscanners, like CD recorders, attempt to produce an image of a continuous pro- cess. But there is no such thing as continuous recordings. Doc- umentation is always a process of discretisation. As it is well known from the Nyquist-Shannon sampling theorem, the high- est frequency that can be recorded depends of the sampling rate. This is true in time and in space. Laser scanner measure points, they often fail to record accurately edges and fine details. If auto- matic processes are used to produce surfaces from the point mea- surements, there is also a risk of aliasing. Dealing with electric signals, it is customary to pass the data though filters before sam- pling to eliminate non interesting frequencies (above the limits of perception), avoid aliasing and moire patterns. This is not often practical with laserscanners. In theory, these limitations can be overcome: increasing resolution and number of stations. In prac- tice, it is often more efficient (in time, resources and money) and interesting to consider that these techniques provide data (mea- surements) rather than models (section 3). Other techniques, and work in situ can help understanding the object and finalising the process of models construction.
Laser scanning systems have been commonly employed in a wide variety of applications such as digital building model generation, industrial site modelling, cultural heritage documentation, and other civilian and military needs. Usually, laser scans are acquired over complex scenes that might contain buildings, roads, trees, light poles, and many human-made and natural objects. The raw point cloud does not provide any semantic information about the type of the scanned features (i.e., planar, linear, or cylindrical features). Therefore, it should undergo a segmentation process to extract the required information for the aforementioned applications. The segmentation procedure aims at extracting features of interest from the laser scanning data and reducing the scene complexity by disassembling it into meaningful categories. Thus far, various segmentation methods with different target functions and processing procedures have been introduced and utilized. In general, any segmentation process is expected to have some artefacts due to the variations in the internal characteristics of laser scanning data (point density and noise level) and the strict/relaxed selection of segmentation thresholds. In other words, no segmentation approach is exempted from possible anomalies. Therefore, quality control procedures are required to evaluate the performance of the laser scanning data segmentation approaches.
In order to be able to compare the results of terrestrial laserscanning and dense image matching, direct georeferencing by post-processing using NovAtel Inertial Explorer had to be improved. Since not only position updates were required (Eugster et al., 2012), but also the attitude angles needed to be updated, bundle adjustment using PhotoScan was performed and the projection center coordinates of direct georeferencing served as initial values. As can be seen in Table 2, deviations of direct georeferencing and bundle adjustment lie in the range of a few decimetres, mainly caused by GNSS signal outages. 3D georeferencing accuracy on the totally used 50 ground control points (GCP) amounts to around 2-3 cm or circa 1/3 pixel (see Table 3). An overall projections error of 0.67 pixel was computed for image sequence 1, 0.65 pixel for image sequence 2 and 0.76 pixel for image sequence 3.
Nevertheless, a closer examination of Figure 6 indicates that while the lasers don’t appear to have an initial warm-bias, there is a pronounced range bias walk for two of the lasers plotted (12 and 13). It was previously assumed (Glennie and Lichti, 2011), that range walk in the Velodyne scanners, which was only observed in the initial warm-up of the instrument, was correlated with the internal temperature of the laser. However, the internal temperature of the VLP-16, plotted on the secondary horizontal axis in Figure 6, does not appear to have significant correlation with the range walk present in either lasers 12 or 13. Indeed, it could be argued that laser 12 (and others not displayed) exhibit range walk over the entire three hour static collection. In general, this range walk is evident in a number of the lasers over the static datasets collected, although the magnitude and period of the walk appears to change. However, while significant, the range walk is still smaller in magnitude than the VLP-16 ranging accuracy specifications. This temporal instability in the range biases is likely the reason that the geometric calibration of the unit is not consistent.
Hence, data is natively structured according to acquisition geom- etry, so splitting the data according to the acquisition order has a physical sense. It could be seen as a drawback that this way, scans of the same objects acquired at different time will not be processed together. We see that as an advantage because urban areas are dynamic environments, and many things might have changed between two scans of the same area: cars and pedes- trians might have moved, windows and doors might have been opened or closed,... All this advocates to consider two scans of the same place as different objects, which simply lie at the same geographic position. Obviously, these objects should be asso- ciated (but not assimilated) for registration or change detection purposes, but not for primitive extraction or object detection. Facade detection in 2D: To tackle the problem of facade de- tection in laser scans, some assumptions are commonly made (Rutzinger et al., 2009):
Glacial and periglacial environments are highly sensitive to climatic changes. Processes of cryosphere degradation may strongly impact human activities and infrastructures, and need to be monitored for improved understanding and for mitigation/adaptation. Studying glacial and periglacial environments using traditional techniques may be difficult or not feasible, but new remote sensing techniques like terrestrial and aerial laser scanner opened new possibilities for cryospheric studies. This work presents an application of the terrestriallaser scanner (TLS) for monitoring the current rapid changes occurring on the Montasio Occidentale glacier (Eastern Italian alps), which is representative of low-altitude, avalanche-fed and debris-cover glaciers. These glaciers are quite common in the Alps but their reaction to climate changes is still poorly known. The mass balance, surface velocity fields, debris cover dynamics and effects of meteorological extremes were investigated by repeat high-resolution TLS scanning from September 2010 to October 2012. The results were encouraging and shed light on the peculiar response of this glacier to climatic changes, on its current dynamics and on the feedback played by the debris cover, which is critical for its preservation. The rapid transformations in act, combined with the unstable ice mass, large amount of loose debris and channeled runoff during intense rainfalls, constitute a potential area for the formation of large debris flows, as shown by field evidences and documented by the recent literature.
Laserscanners increase the accuracy and speed of 3dimensional (3D) data acquisition of the digital as-built generation process. There is a new trend in the use of laserscanners to acquire reliable and accurate data for building information building (Bosche et al.,2015). The survey yields a digital data set, which essentially a dense “point cloud”, where each point is represented by a coordinate in 3D space. The most important advantage of the method is that a higher point density can be achieved, in order of 5 to 10 mm resolution. The scanner records thousands of points per second and each point have intelligence, or location coordinates and elevation information (Sepasgozar et al.,2014). All of these points are placed into the same local coordinate system to make up a point cloud which represents the area, building, or object being scanned in a 3D space. Most modern scanners are rated to have their best accuracy at distances out to 100-130 meters. This means that objects and areas can be scanned from a distance if the areas is inaccessible. When first introduced, the benefits of the technology were immediately apparent in the survey industry.
The use of T.O.F. laserscanners for the surveying of very large statues or of regular parts of large statues is not new. These surveyings are generally integrated by close range scans, obtained by triangulation instruments, for the irregular surfaces (Ressl, 2007). The surveying of a complex statue has been generally performed by using several scans, retro-reflecting targets and images. The most diffused software give automatically low precision models. The effort for obtaining more accurate results is generally very high (Vozikis et al., 2004).
4.4.1. Terratriangulation: 10 images were selected from all of the acquired digital images. These were joined into stereograms in such a way, that the right image of the first stereogram is the left image of the next stereogram. These images were acquired from one plane parallel to the imaged wall, from neighboring positions, so that the overlap between images is no less than 60%. The stereogram orientation was conducted in Topcon Image Master software. The coordinates of the reference points were determined from point cloud acquired with the terrestriallaser scanning system.