Transcript
Page 1: Vegetation filtering of waveform terrestrial laser scanner data for DTM production

ORIGINAL PAPER

Vegetation filtering of waveform terrestrial laser scanner datafor DTM production

Francesco Pirotti & Alberto Guarnieri & Antonio Vettore

Received: 5 October 2012 /Accepted: 30 September 2013 /Published online: 23 October 2013# Società Italiana di Fotogrammetria e Topografia (SIFET) 2013

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

TLS with multi-target capability can potentially provide amore detailed DTM in presence of dense vegetation.

Keywords Terrestrial laser scanner . DTM . Onlinefull-waveform analysis . Vegetation filtering .Multi-targetcapability

Introduction

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 laserbeam’s divergence angle, which, depending on range betweensensor and target, determines the size (diameter) of the laser’sprojection 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.

F. Pirotti (*) :A. Guarnieri :A. VettoreCIRGEO—Interdepartment Research Center for Geomatics,University of Padova,viale dell’Università 16, 35020 Legnaro, PD, Italye-mail: [email protected]

Appl Geomat (2013) 5:311–322DOI 10.1007/s12518-013-0119-3

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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 “shadowing”effect 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).

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).

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.

In recent years, the Riegl company has developed a newline of terrestrial laser scanners (VZ-series), based on pulsed

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).

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).

Study site

The study area is located on the north side of the Brustolèmountain, 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

Fig. 1 Study area position

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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 20–30 millions of cubic meters (Bitelli et al. 2009).

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. Brustolè 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.

Data acquisition

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.

Pre-processing of scanned point clouds

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

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

Fig. 2 View of the bottom side of the landslide

Table 1 Technical specifications of the Riegl LMS–Z620

Field of view 360° (H)×80° (V)

Max. range Refl. >10 % up to 650 ma

Refl.>80 % up to 2,000 ma

Beam divergence 0.15 mrad

Measurement rate Up to 11 kHz at low scanning rate(oscillating mirror)

Up to 8 kHz at high scanning rate(rotating mirror)

Wavelength Near infrared

Accuracy 10 mm

Repeatability 10 mm (single shot)

5 mm (average)

a Depends on target reflectivity—the more reflective the target the longerthe range

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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.

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.

Scan registration

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

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-reflective back-sighting target, whose positionwas surveyed with DGPS, with the same estimated accuracyas the point at scanner position. This approximate registrationwas further refined by applying the multi station adjustment(MSA Fig. 3) procedure, a variant of the ICP algorithmimplemented in Riegl’s RiscanPro software. This algorithmperforms an initial fitting of a set of planes in the point-cloudsand then tries to align them by finding the best correspondingplanes between scans and performing the best rotation andtranslation.

Georeferencing

Because of the very large number of measurements acquiredwith the Riegl VZ-400, merging and georeferencing togetherboth full laser datasets revealed to be impracticable. There-fore, the data analysis and comparison between the two laserscanners were restricted to a limited area of the landslide,shown on the right side of Fig. 2. To this aim, a specificprocedure was adopted to merge together the correspondingscans. In a first step, the global registration (see the previoussection) was applied separately to both datasets, then only thescans covering the selected area were imported in a newRiscanPro project and registered with the MSA procedure inorder to reduce as much as possible the residual misalign-ments. The use of georeferenced targets provided registrationtowards a scan world in a cartographic reference frame.Therefore at the end of the process the point clouds will begeoreferenced. At the end of this processing step, we obtained

Table 3 Properties of acquired laser scans

LMS-Z620 VZ-400

No. of scan stations 7 3

Average meas. per scan 3,400,000 15,300,000

Max. range 900 m 350 m

Average scan resolution 0.5 mrad 0.5 mrad

Table 2 Technical specifications of the Riegl VZ-400

Field of view 360° (H)×100° (V)

Max. range Refl. >20 % up to 280 ma

Refl. >80 % up to 600 ma

Beam divergence 0.3 mrad

Measurement rate 42 kHz at long range mode

122 kHz at high speed mode

Max number of targets per pulse Practically unlimited

Wavelength Near infrared

Accuracy 5 mm

Repeatability 3 mm

aDepends on target reflectivity—the more reflective the target the longerthe range

Fig. 3 Registration using a target map of residuals (σ=3 cm)

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a residual registration error of about 3 cm (1σ) between theZ620 and VZ-400 selected scans. This value is higher than theaccuracy claimed by Riegl for both laser scanners (10 mm forthe Z620 and 5 mm for the VZ-400), however it should benoted that the scans were aligned prior to filtering out thevegetation, whose presence may have therefore affected thefitting of planes and the search for correct correspondencesbetween the scans during theMSA procedure. The registrationerror is an order of magnitude greater than the error from theDGPS, therefore we can estimated that the registration errorcorresponds to the final georeferencing error.

Waveform processing in TLS

Conventional terrestrial laser scanners based on the time-of-flight (TOF) measurement principle are characterized asanalogue discrete return systems. For each emitted pulse,target detection and time-of-arrival (TOA) estimation of thereturned pulse is performed in real time through onlinemethods like the constant fraction discriminator (Toth andBrzezinska 2007). The amplitude of the target signal detect-ed by the receiver affects the resulting value of the TOA. Inpresence of multiple targets along the pulse path, analogueestimators can yield significant range errors for the secondand further targets or completely fail to detect them. Thedegree of separation of successive targets also depends onthe temporal separation between consecutive target echoeswith respect to the emitted pulse width and receiver’s band-width (Ullrich and Pfennigbauer 2011). In contrast to adiscrete return systems, in an echo-digitizing systemreturned signals are sampled at high rate and converted ina digital form prior to target detection. All subsequent pro-cessing steps are executed in the digital domain on-line or inpost-processing. The latter approach is typically adopted inairborne LiDAR systems where sample data are stored inspecific high capacity data recorders (Ullrich and Reichert2005). Applying the full-waveform analysis (FWA) to thesedata enables to acquire additional information with respectto conventional discrete return laser systems.

Beside range measurements, resulting from echo detectionand estimation of related TOA, backscattering properties ofthe targets can be retrieved as well, such as the amplitude ofecho signal, which provides an estimate of target’s lasercross-section, and the echo width, that represents a measureof the backscatter profile of the target along the laser beam.As mentioned in (Ullrich and Pfennigbauer 2011), the dif-ferent approaches proposed so far to extract the target back-scattering properties from digitized returned signals can begrouped into two main classes: deconvolution based methods(Roncat et al. 2011) and procedures based on the modellingof digitized waveform with basic functions (Wagner et al.2006; Roncat et al. 2008). An example of the latter approach

used for FWA is represented by Gaussian decomposition.This method relies on the assumption that the system re-sponse can be modelled with a Gaussian function and that allthe contributions of the backscattering targets are also Gauss-ian. Echo detection is therefore performed by finding Gauss-ian pulses in the returned waveform. Such approach has beenimplemented in RiANALYZE, the Riegl software dedicatedto the FWA of echo-digitizing systems.

A different approach to FWA has been implemented in thenew line of terrestrial laser scanners by the Riegl company(VZ-series, Riegl 2012). Airborne Laser Scanning (ALS)system store the full return waveform in a 8-bit or 12-bitdigitized representation during the flight for subsequentpost-processing. Post-processing computational power forreal-time processing and the need to immediately analyzethe received signals has led Riegl to implement an onlinewaveform processing for the VZ-line products. Basically,upon echo pulse reception a highly accurate estimate of itsamplitude and TOA is performed in real-time. Throughhardware-oriented implementation of the processing algo-rithm, a VZ-series laser scanner is able to perform about 1.5million range and amplitude measurements per second. Asdenoted in Table 2, given a laser pulse repetition rate of100 kHz (42,000 measurements per second in long rangemode) and 300 kHz (125,000 measurements per second inhigh speed mode), the Riegl VZ-400 laser scanner can record10 or 5 targets per laser shot, respectively (Doneus et al.2009). Similarly to ALS-based echo-digitizing systems, theRiegl’s VZ-series instruments provide some additional andvery interesting features with respect to the conventionalanalogue discrete return-based terrestrial laser scanners, asbriefly described in the following subsections.

Multi-target capability

The adoption of online waveform processing in the VZ-400laser scanner allows to recordmultiple echoes for each emittedlaser pulse along with their characteristic width and amplitude.However, as previously mentioned, the capability to correctlydiscriminate two consecutive echoes is determined by thelaser’s pulse width and the receiver bandwidth. For the VZ-400, a minimum distance of 0.8 m between two consecutivetargets is necessary for recording them as two distinct echoes(Riegl 2009). Echoes separated by shorter distances betweenscatterers within the same laser shot cannot be distinguishedas the corresponding echo signals because they overlap.Figure 4 depicts different return segments of a return echo,showing in red, on the left image overlap of peaks, and in theright image two distinct peaks. Consequently, the measuredrange will be estimated somewhere in between the targets,thus resulting in an erroneous point.

Since the processing of recorded waveform is performed inreal-time and given the limited computational power available

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on TLS systems, the Gaussian decomposition method can-not be applied. This limits the multi-target capability of VZ-series laser sensors and prevents to retrieve the width ofdetected echoes as additional information. However, theonline waveform processing allows to reduce the problemof detection of nearby targets by providing informationabout the “pulse shape figure”. This parameter represents ameasure of the deviation of the actual target’s pulse shapefrom the expected (and undistorted) pulse shape for eachindividual echo. In this way, in cases where targets are closerthan the discrimination limit of 0.8 m, the pulse shape figureallows to determine whether the return echo originates froma single target or from at least two nearby targets(Pfennigbauer et al. 2009).

Calibrated amplitude reading

Beside expressed information, the VZ-line additionally de-livers a calibrated amplitude information for each target echo,which can be used to improve the object classification. Theamplitude reading is defined as the ratio of the actual detectedoptical amplitude of the echo pulse versus detection threshold.The ratio is expressed in decibels (dB) as in the followingequation:

AdB ¼ 10⋅logPecho

Pthr

� �ð1Þ

where

AdB calibrated amplitude [dB]Pecho echo signal power [W]Pthr power detection limit [W]

Calibrated reflectance reading

Laser radar cross-section is somewhat present in the VZ-400by the reflectivity value of each target echo. The reflectivityvalue gives the ratio of the actual optical amplitude versus theoptical amplitude of a diffuse white target located at the samerange (it is furtherly assumed that the white target is largerthan the laser footprint, 100% reflecting, flat, and its surface isorthogonal to the laser beam Riegl 2012). The value is againgiven in dB as in the following equation:

ρrel RTð Þ ¼ AdB RTð Þ−AdB;White RTð Þ ð2Þwhere

RT target T range [m]ρ rel(RT) calibrated reflectance of target T at range RT

AdB,T calibrated amplitude of T at range RT [dB]AdB,White = amplitude of white target T at range RT [dB]

Reflectivity values above 0 dB indicate that the target is(partially) retro-reflecting. Assigning a nominal reference val-ue as a function of range, objects with the same reflectivitywill have the record with the same brightness regardless of thedistance to the scanner. This applies to first or unique returnechoes; for the succeeding echoes the response is a combina-tion of the respective laser radar cross section and of theattenuation of the laser pulse due to the preceding targets(Pfennigbauer et al. 2009). Since this target-induced attenua-tion cannot be retrieved from the amplitude of thebackscattered pulse, the calibrated reflectance readings arerepresentative of the backscattering properties of just thesingle or first targets acquired by a multi-echo digitizingsystem. An example of calibrated amplitude and calibrated

Fig. 4 Two examples of multiple return echoes: green is the first return segment and red is the last (from Optech IWD1 8 bit waveform digitizer)—thecoordinates of the peaks will provide reflectance and the coordinate of the point in 3D space using the time of flight measure

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reflectance is shown in Fig. 5, while a small portion of a VZ-400 scan coloured according to the recorded multiple echoesis displayed in Fig. 6.

Vegetation filtering

The objective of the step regarding vegetation filtering is toremove as much as possible the above-ground elements(vegetation) in order to separate ground from non-groundpoints. The area delineated and collected with the RieglLMS-Z620 and the VZ-400 laser scanners for investigationfrom the Brustolè study site was extracted as a geo-referenced point-cloud and the filter method was applied toseparate vegetation points from the rest. The points whichwere not recognized as vegetation by the filter were consid-ered ground points, thus a terrain surface was modelledusing them. The resulting DTMs from the two scannerswere then compared to assess the improvements of amulti-target TLS system. The two datasets from the twoTLS surveys went through slightly different proceduresdue to the necessity to exploit the enhanced characteristicsof the VZ-400.

Fig. 5 Front view of the test arearendered according to thecalibrated amplitude (a) and thecalibrated reflectance (b)

Fig. 6 Side view of a small portion of the landslide surveyed with theRiegl VZ-400 laser scanner. Green colour denotes unique targets, redfirst targets, light blue intermediate targets and blue last targets

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Filtering the VZ-400 dataset

The VZ-400 scan was pre-filtered by exploiting the multi-target capability and the “calibrated relative reflectance” read-ings. In the first step of the filter procedure, unique and last-of-many targets were extracted from the original point cloudselecting only points whose echo ordinal return number wasequal the total number of return echoes reflected from theoutgoing laser pulse. Intermediate echoes were discarded asthey most likely originated by non-ground points. The nextstep in the filter procedure was applied to the candidate pointsremaining after the first step, and it is based on the calibratedreflectance information. The use of the calibrated reflectanceallowed to significantly reduce the number of points, but somevegetation elements, like tree trunks, could not be correctlyremoved because their reflectance values are similar to thoseof ground points. An example of the results of this kind offiltering is shown in Fig. 7, where red points represent uniquetargets eliminated by applying a reflectance threshold of−6 dB. A lower threshold (−8 dB) was set for the group oflast-of-many echoes because the reflectance values of vegeta-tion elements resulted to be more similar to that of groundpoints. This is due to the attenuation effect that precedingtargets produce on the emitted laser pulse, as was mentionedin the previous section, so that reflectance values recorded forthe last echoes are not range independent and do not actuallyrepresent the backscattering properties of corresponding tar-gets. The reflectance thresholds were determined empiricallyusing the distribution frequency plot of reflectance valuesprovided by RiscanPro software and by checking the reflec-tance value of some points (clearly recognized as vegetationand ground) manually selected in the 3D view of the test area.

The first two steps described previously were applied in thefilter procedure of VZ-400 laser data; the next steps arecommon to both datasets derived by the two TLS instruments.The next procedure is to apply two different spatial filters tothe point clouds in order to eliminate as much vegetation aspossible: an iterative filter originally developed for the

filtering of ALS data, and a custom morphological filter,developed by the authors for the mapping and quantificationof vegetation in forested areas (Pirotti et al. 2013).

In the iterative filter, the original point cloud is projected ona reference plane and then the cells in a regular grid are filledwith values by selecting the point with minimum laser eleva-tion (Z axis orthogonal to the plane) in each cell. The originalpoint cloud is then compared with the DTM grid from theprevious step and only the points closer than a distance thresh-old are preserved. This process is repeated iteratively byreducing at each step the size of both the grid cell and thethreshold, until the final DTM raster grid is produced, theo-retically without vegetation or other above-ground elements.

In the morphological filter, three main groups of parame-ters are derived from a dataset acquired with an echo-digitizing system: amplitude, ordinal echo return numberand spatial coordinates of measured points. The first twofeatures are used in the first two steps of the algorithm inorder to extract candidate ground points from the originalpoint cloud as was described at the beginning of this section.A threshold is applied to the amplitude data recorded with thelaser measurements. Such threshold is determined by the firstvalue of the last quartile of the cumulative distribution func-tion of the amplitude values. The second step uses the lastfeature, the 3D coordinates of the points. A custom morpho-logical filter (Haralick and Shapiro 1992), composed by anerosion (E) operator followed by a dilation (D) operator, in theso-called morphological “opening” operation which is appliedto maximum and minimum laser elevations falling inside aregular grid as in the equation below:

En ¼ min Znð Þxn;ynð Þ∈C

Dn ¼ max Znð Þxn;ynð Þ∈C ð3Þ

where Zn is the height of the points inside the window of sizeC at the n th iteration; the size decreases by one quarter area-wise (half of width and height of window) at each successiveiteration. These type of operations have been applied to air-borne altimetry data (Vosselman 2000) and are not new in thescientific community, but no applications have been done onTLS datasets.

By iteratively decreasing the cell size C of the grid, a set ofDTMs are obtained until the vegetation is almost completelyremoved. The conceptual workflow of this method is shownin Fig. 8.

The application of the procedure including the iterative andof the morphological filters for the vegetation removal showsresults presented in Table 4. The final filtered point-cloudsderived from the Riegl LMS-Z620 and VZ-400 laser scannersare shown in Fig. 9, respectively at the top for the former andthe bottom for the latter. It can be seen from both table andimages that the VZ-400 covers a significant amount of area.Fig. 7 Example of vegetation filtering based on the calibrated reflectance

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Discussion

This study case is a practical comparison of a method forfiltering ground from above-ground elements (vegetation) inpoint-clouds obtained from a classic discrete-return TLS sen-sor and from a TLS sensor with online waveform processing.Results show that the added-value in the latter sensor consistson the information extracted from the online processing of thereturn waveform, specifically the width and amplitude corre-sponding to the peak detected in the analysis. From a practicalpoint of view the characteristics which are available to theend-user are the ordinal return number and the calibratedreflectance value. These features add two criteria which arenot available in discrete return TLS sensors and which areuseful in determining which points can be kept as candidateground points. This first step, i.e. the removal of non-groundpoints using the above-mentioned criteria, has the advantagesof decreasing computational time necessary in the following

steps and of removing points which might mistakenly be usedto assign a height value to a cell in the final DTM product.This error is one of the factors that can decrease the quality ofthe interpolation along with morphometric characteristics(Godone and Garnero 2013).

There are two important considerations to be done lookingat Table 4. The ratio between the number of points from thetwo different sensors is 4.9 (12,200/2,500) before filtering and7.6 (3,020/400) after filtering. This is to be interpreted as ahigher percentage of ground points detected using the VZ-400scanner. This is reasonable considering that the potential fordetermining a ground hit when vegetation partly occludes thelaser beam is a prerogative of the VZ-400 scanner, whereasunder the same circumstances the LMS-Z sensor only returnsthe echo caused by the first target, the vegetation. In the caseof the LMS-Z sensor the filter determines 16.6 % (415 from 2,500) of the original points as ground points, whereas the VZ-400 determines 24.9 % (3,035 from 12,200). This can implythat the user can estimate a 50 % increase in the potential ofacquiring ground hits using sensors with multiple returns.Most important, from looking at Fig. 9, the ground returnsfrom the VZ-400 sensor cover areas where the LMS-Zscanner’s ground points are missing, causing empty patcheswhere ground height will be estimated by interpolation when

Fig. 8 Workflow of the morphological filter applied to the VZ-400. PRNdenotes the point return number, while NoR the total number of returnsfrom the outgoing pulse (Pirotti et al. 2013)

Table 4 Results from filtering LMS-Z260 and VZ-400 datasets

LMS-Z620 VZ-400

No. of initial points (×103) 2,500 12,200

No. of points after multi targetfiltering (×103)

N/A 10,750

No. of points after filtering basedon calibrated reflectance (×103)

N/A 6,230

No. of points left after iterativefiltering (×103)

400 3,020

No. of points left after morphologicalfilter (×103)

415 3,035

Fig. 9 Point-clouds resulting after the removal of the vegetation throughthe morphological filter. LMS-Z620 dataset (top) and VZ-400 dataset(bottom)

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producing the DTM. More information on the differences canbe determined by looking at Fig. 10, where a triangulatedirregular network (TIN) of ground points was used to extractDTMs for each dataset. It is clear by visual interpretation thatthe VZ-400 allowed to obtain more detail and more coverage.The map in the bottom reports differences between the scans,and highlights spots (in green colour) where the VZ-400sensor allowed an improvement in ground detection. Someinteresting considerations can be done here by seeing that insome spots differences can reach high values. It must beremembered that these can be caused by a single pixel beingclassified as “ground” by the procedure, resulting in a falsepositive. This can happen in both datasets, but it can be arguedthat false positives can be more common in the single-echoTLS due to most points belonging to canopy. In Fig. 10c, thered spot in the middle of the area shows that in that specificpart the VZ-400 gave a pixel with higher Z value. This is theonly spot with a significantly higher Z value in the VZ-400and it can be caused by the laser missing the ground due toscan geometry whereas the LMS-Z managed to go thru to hitthe surface. Being the difference so much it might be the casethat an outlier was considered as ground as not differentenough to be discarded by the filter. In general though most

pixels were lower in the VZ-400 showing an increase inpenetration and ground detection rate.

It is important, when extracting DTMs from TLS data inparticular, to take into consideration the sampling step of thelaser scanner. The geometry of TLS scans causes the pointdensity to decrease with distance from the sensor. At certaindistances the footprint size of the laser beam also becomesimportant when related to accuracy of the point position;theoretical investigations (Lichti and Jamtsho 2006) haveshown interesting detail about the sampling step/spotdiameter/real spatial resolution connection. Sensor calibrationplays also an important part, especially considering the effectof vertical angle on the collimation axis error (Lichti andFranke 2005; Lichti and Licht 2006). In this case, scangeometry’s vertical angle is limited to ±30° with an estimatedmaximum error which is one order of magnitude less than thescan resolution. In this article the topic of accuracy is notdiscussed in depth, but is nevertheless mentioned here forthe purpose of comprehensiveness.

The final DTM produced by using the VZ-400 processeddataset covers more homogenously the area of interest (seeFig. 9). This is not to be underestimated as TLS sensors with asingle discrete return suffer from the presence of obstruction

Fig. 10 Shaded DTMs withcolour scale obtained from scanwith VZ-400 (a), LMS-Z620 (b),and differences between theDTMs (c)—the areas in black areempty cells in (b) which has datain (a)

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elements and have no capability of penetrating vegetation. Inthis case, the sensor can record multiple returns thus includingreturns from echoes originating after the first. This results in ahigher probability of returning a ground hit and its respectivecoordinate.

Conclusions

Results show that in both datasets the application of theiterative and morphological filter performs quite well foreliminating vegetation in the study area. The morphologicalcomplexity of the ground surface limits the performance of theprocedure, thus two steps are still fully manual: the choice ofthe calibrated reflectance threshold, and a direct manualediting on the final product to detect and remove cells wherethe value incorrectly represents non-ground elements. Thecomparison between the number of the laser returns left inthe final DTMs shows that the VZ-400 provided a one order ofmagnitude denser point cloud in respect to the LMS-Z620.This demonstrates that a TLS with multi-target capability canpotentially provide a more detailed DTM even in presence ofvery dense vegetation, as in the case of the Brustolè landslidearea. Processing the TLS data to extract the DTM from eachsingle scan and successively merge the DTMs can be a validalternative to merging the scans first. As was mentioned thenumber of points acquired in a typical TLS session is veryhigh, therefore processing several scan stations—co-registra-tion and georeferencing—might be difficult due to computermemory limitations.

Filter criteria based on the calibrated reflectance revealed tobe quite effective to reduce the number of off-ground points inthe pre-filtering stage, however the selection of the moresuited threshold values was quite difficult as it was based onempirical methods. This applies overall in the case of the lastechoes where the attenuation effect of preceding targets makesthe calibrated reflectance values of that echoes still rangedependent, so that ground and off-ground points tend toassume more similar reflectance values. The added featuresprovided by waveform analysis from TLS surveys are a validsource of information for filtering the point cloud. A lot hasbeen done for improving the exploitation of such informationfor filtering/classification procedures and thus ameliorate end-products. Still the analysis of waveform-derived informationis at an early stage of investigation, and new results are likelyto further enrich its convenience and efficiency.

Acknowledgments Authors wish to thank prof. Gabriele Bitelli and hisresearch team of the Department of Civil, Environmental and MaterialsEngineering (DICAM) of the University of Bologna (Italy) for the pro-vision of the Riegl VZ-400 laser scanner and the technical support givenduring the survey.

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