Vegetation filtering of waveform terrestrial laser scanner data for DTM production

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<ul><li><p>ORIGINAL PAPER</p><p>Vegetation filtering of waveform terrestrial laser scanner datafor DTM production</p><p>Francesco Pirotti &amp; Alberto Guarnieri &amp; Antonio Vettore</p><p>Received: 5 October 2012 /Accepted: 30 September 2013 /Published online: 23 October 2013# Societ Italiana di Fotogrammetria e Topografia (SIFET) 2013</p><p>Abstract In this article, we present an investigation regardingthe differences between a full-waveform and a discrete-returnterrestrial laser scanner employed in a survey of a mountainarea with dense vegetation. The Riegl LMS-Z620 providesdiscrete returns whereas the Riegl VZ-400 provides multiplereturns with associated width and amplitude of the peaksextracted by online waveform processing. The uncertaintyabout the stability of the terrain underlying the mountainslope, which was affected by a landslide in 1966, gives aparticular importance to an accurate representation of theterrain surface, thus to a robust filtering of the vegetationcomponent. The VZ-400 scans were pre-filtered by exploitingthe calibrated relative reflectance readings and the multi-target capability provided by this laser scanning system. In thenext step, two spatial filters were applied to both geo-referenced 3D models in order to eliminate vegetation usingan iterative filter and a custom morphological filter. Resultsshow that the use of the iterative morphological filter performsquite well in eliminating the vegetation from both datasets.Vegetation in sloped terrain does still limit the completeremoval of the above-ground elements, thus a completelyautomatic procedure is still not applicable. Stem and canopygrowing direction with respect to ground is a factor whichshould be taken into account in future developments of theprocedure. Differences between the two results show that ahigher point density is obtained from the VZ-400 due to itsmulti-return capabilities and the added characteristicsextracted from the online waveform processing give addedvalue for filtering more accurately. Results demonstrate that a</p><p>TLS with multi-target capability can potentially provide amore detailed DTM in presence of dense vegetation.</p><p>Keywords Terrestrial laser scanner . DTM . Onlinefull-waveform analysis . Vegetation filtering .Multi-targetcapability</p><p>Introduction</p><p>Extracting accurate digital terrain models (DTMs) and digitalsurface models (DSMs) from terrestrial laser scanner (TLS)surveys is a process that presents several key issues related toscan geometry, sensor characteristics and surface features,especially in areas covered with vegetation. Surface featuresimply the morphology of the scanned area and the character-istics of above-ground elements which somehow prevent thelaser beam from reaching the ground surface. The result is tohave a certain number of dark areas where no ground-pointshave been sampled by the laser pulse. The presence of vege-tation is an issue which has to be considered when surveyingwith TLS for producing DTMs. Vegetation allows a certainamount of penetration of the laser pulse; therefore, there is aprobability of sampling the ground surface also under a veg-etation cover. This is true if the sensor is able to record morethan a single return echo, which is nowadays always the casewith airborne sensors, but not always the case with terrestrialsensors. The amount of penetration depends on the laserbeams divergence angle, which, depending on range betweensensor and target, determines the size (diameter) of the lasersprojection on the target surface, the energy in the outgoinglaser pulse (Hopkinson 2007) and the vegetation structure(e.g. canopy density, leaf morphology). In many fields ofstudy (e.g. forestry and agriculture), the diverse degree of laserpenetrating the canopy is an additional advantage for corre-lating forestry metrics and for ecological analysis (Pirotti et al.</p><p>F. Pirotti (*) :A. Guarnieri :A. VettoreCIRGEOInterdepartment Research Center for Geomatics,University of Padova,viale dellUniversit 16, 35020 Legnaro, PD, Italye-mail: francesco.pirotti@unipd.it</p><p>Appl Geomat (2013) 5:311322DOI 10.1007/s12518-013-0119-3</p></li><li><p>2012). In this case-study, vegetation represents a key factor inthe process of extracting accurate DTMs as it is the mainelement that needs to be detected and filtered to discriminatebetween ground and off-ground points. Most investigationson methods up to now have been applied to datasets derivedfrom Airborne Laser Scanner (ALS) whereas less interest hasbeen shown towards the application to TLS datasets because,until recently, TLS sensors had much shorter ranges andsurveys suffered from large empty areas due to shadowingeffect of obstructions. Very few tests have been made on thepossibility of using both ALS and TLS data for surveying,mostly the TLS data has been used to analyse the metrics ofthe ALS data (Doneus et al. 2010). The extraction of accurateDTMs and DSMs as well as correct information on vegetationdensity and height is a challenging task which calls for con-sideration on some significant aspects, for example in denselyvegetated areas where the obstruction of the laser beam isparticularly relevant, as well as the significant drop of pointdensity as the distance from the laser sensor increases. Forsuch reasons, different issues, with respect to ALS applica-tions, must be taken into account in the development ofmethods for the processing of TLS data acquired in thesenatural environments. The applications are really broad, rang-ing from setting up networks of sensors for monitoring groundmovements (Castagnetti et al. 2013), applications of monitor-ing slow surface movement (Corsini et al. 2013) to all thevarious applications connected to the use of the final products(DTM and DSM).</p><p>The registration and georeferencing steps are important inthe pre-processing phase and have been described in depth. Inthis case, georeferenced backsight targets were used to directlyorient and position the sensor. This approach has been suc-cessfully tested and used in comparable study cases (Lichti andGordon 2004; Scaioni 2005; Bertacchini et al. 2012).</p><p>Ground filtering methods have been applied to aerial laserscanner point-clouds with the following three main types ofmethods (Maas 2010): mathematical morphology, progressivedensification of a triangle mesh and linear prediction plushierarchic robust interpolation . The first method derives fromthe work of Haralick and Shapiro (1992) where proof wasshown that erosion (e ) and dilation (d ) operators in successionfor opening (d e ) or closing (e d ) operations cansuccessfully be used for improving object filtering in grayscale images. The second group of methods is based on theprogressive densification of a triangular mesh. The initialtriangle network is created using a set of local minima pointsover an area of user-defined size; points are then added using acriteria on the new triangle slope (Axelsson 1999). The lastgroup is based on a method proposed by (Kraus and Pfeifer2001) where a surface model is defined using linear predictionand hierarchic robust interpolation.</p><p>In recent years, the Riegl company has developed a newline of terrestrial laser scanners (VZ-series), based on pulsed</p><p>time-of-flight (TOF) technology (Riegl 2012), providing ad-ditional features which may help to solve the problem ofgenerating reliable DTMs in forested areas. These instrumentsprovide online waveform processing, combining the advan-tages of analogue detection systems (immediate results with-out the need for post-processing) with those of airborne echodigitizing systems (multi-target capability).</p><p>In this paper, we present the results of the application oftwo filters, related to the first and second class of groundfiltering methods mentioned above, for the removal of thevegetation present in the datasets acquired with two terrestriallaser scanners in a dense forested area, in the Italian Alps. Theemployed laser scanners are both based on pulsed TOF tech-nology but they adopt different measurement recording ap-proaches: analogue discrete return (Riegl LMS-Z620) andecho-digitizing capability (Riegl VZ-400).</p><p>Study site</p><p>The study area is located on the north side of the Brustolmountain, in front of the small town of Arsiero (Vicenza,northern Italy, Fig. 1). The exploitation of the area as asignificantly large excavation to produce building materialhas ignited strong debates between the local population andlocal government authorities. The key point of the debate isthe assessment of the stability of the underlying mountainslope, which has been overrun, in 1966, by a landslide. Afterthe event the area has been continuously monitored by meansof sensors apt to measure the spatial dynamics of terrainmovements. The landslide has interested approximately asurface of 600,000 m2, with an extent of around 400 m inthe vertical direction and 1 km in the horizontal direction at thelower part of the slope. Elevation ranges from 350 to</p><p>Fig. 1 Study area position</p><p>312 Appl Geomat (2013) 5:311322</p></li><li><p>750 m a.s.l. The main sliding surface is located at a depth of20m at the top and at the bottom of the landslide body, while itreaches a depth of 100 m in its central part. The volume ofmaterial involved in the event of 1966 was estimated to bearound 2030 millions of cubic meters (Bitelli et al. 2009).</p><p>The area previously described was selected as ideal testground for our purpose: evaluating the potential of a full-waveform (FW) TLS system to provide higher quality DTMsin vegetated areas with respect to conventional single-echoTLS systems. Brustol is a relatively small and morphologi-cally complex area in the Italian Alps. It has good accessibilityand presents many patches with dense cover of low andmedium-height vegetation as well as few high-stem trees (upto 11 m). It has also been undergoing geomatic investiga-tions for some years and is a spot of interest for the surround-ing population, therefore this investigation can potentially beused also in a wider project of monitoring in time the unstableslope. Other similar areas have been studied (Costantino andAngelini 2011; Barbarella and Fiani 2012), but never with FWTLS.</p><p>Data acquisition</p><p>The entire study area was surveyedwith a long-range TLS, theRiegl LMS-Z620, on February 2011. In March of the sameyear, a second survey was carried out with a full-waveform(FW) system, the Riegl VZ-400. Both surveys were doneduring the vegetation dormant period. Due to time constraintsand the limited operational range of FW laser scanner (max.600 m, see Table 2), only the lower part of the landslide bodycould be surveyed. In this part, the vegetation land coverincludes coppice, low underbrush some high-stem tree,whereas rocks, cliffs and patches of bare ground are signifi-cantly present. The occurrence of dense vegetation, rangingfrom low bushes to high trees, added complexity to the scan ofthe area as it covered multiple strata above the ground (Fig. 2).Tables 1 and 2 show the main technical features of the LMS-Z620 and of the VZ-400 laser scanners, respectively, whileTable 3 reports some properties of the scans acquired withboth instruments.</p><p>Pre-processing of scanned point clouds</p><p>All acquired scans were processed to bring point-clouds inboth surveys to the same reference frame with minimumerrors. This can be defined as pre-processing of the data. It isusually divided in two processes: the registration of all thepoint-clouds from different scan stations and the geo-referencing of the total survey on a common frame, which isusually cartographic, but can also be user-defined when this isnot required or necessary. The registration procedure can be</p><p>target-based, which uses ad hoc retro-reflective targets posi-tioned by the user, or surface-based, which directly exploitselements on the overlapping area. In both cases, an iterativeleast-squares algorithm is used to minimize error metrics. Themost common algorithm is the iterative closest point (ICP)algorithm (Chen and Medioni 1992). Alignment methods useprimitives, which can be extracted keypoints (e.g. using SIFTor spin-images, Huber and Hebert 2003), segments, corners,local planes, or specific shapes like spheres or cubes. Surface-based methods use geometries derived from the scan itself,either directly using objects which have regular geometries(planes, building corners, roofs) or virtual geometries derivedfrom intersections and other mathematical procedures (Theilerand Schindler 2012). The georeferencing step requires objectswhich can easily be recognized both in the point-cloud and onthe ground. Ad hoc targets can be used, like in the registrationstep, which have the advantage of being easily recognizedbecause of their shape and high retro-reflectivity, but aresometimes hard to place in areas with low accessibility (i.e.in our study case). Where targets are not used, natural targets</p><p>Fig. 2 View of the bottom side of the landslide</p><p>Table 1 Technical specifications of the Riegl LMSZ620</p><p>Field of view 360 (H)80 (V)</p><p>Max. range Refl. &gt;10 % up to 650 ma</p><p>Refl.&gt;80 % up to 2,000 ma</p><p>Beam divergence 0.15 mrad</p><p>Measurement rate Up to 11 kHz at low scanning rate(oscillating mirror)</p><p>Up to 8 kHz at high scanning rate(rotating mirror)</p><p>Wavelength Near infrared</p><p>Accuracy 10 mm</p><p>Repeatability 10 mm (single shot)</p><p>5 mm (average)</p><p>a Depends on target reflectivitythe more reflective the target the longerthe range</p><p>Appl Geomat (2013) 5:311322 313</p></li><li><p>can be sought in the area and in the point cloud (Goshtasby2005). Often a high-resolution scan of the artificial or naturaltarget is carried out by the surveyor for easier recognition inthe point cloud and for improving the result of the registrationand georeferencing process.</p><p>In this survey, registration and georeferencing were donedirectly using back-sight targets with known coordinates mea-sured with higher accuracy instruments (Differential GNSS).Error sources in this case come from instrument positioning,levelling and centring, target placement and centring, scannerinternal noise and scanner systematic errors due to poor cali-bration (Lichti et al. 2005). This method is often used wherenot all scans share a large overlapping area (Alba et al. 2005)and when the area does not contain natural geometric ele-ments. It is an issue in the case of very high accuracy posi-tioning the correct definition of the reference frame, in thiscase ETRF89-2000, in relationship with the GNSS receiversused is important (Castagnetti et al. 2009), and has to beaccounted for by end-users.</p><p>Scan registration</p><p>The first step of registration with backsight targets consisted inoptically centring each laser sensor over a point with knowncoordinates and then levelled through the built-in dual axiscompensator. For this task, the point was accurately measuredwith an estimated accuracy of 4.5mm in all three dimensions</p><p>using DGPS. The remaining degree-of-freedom, i.e. rotationabout the vertical axis (Z ), was fixed by orienting the instru-ment reference system (IRS) towards a second point withknown coordinates. This last task was accomplished by scan-ning a retro-refle...</p></li></ul>

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