ALTERNATIVE METHODOLOGIES FOR THE ESTIMATION OF LOCAL POINT DENSITY INDEX: MOVING TOWARDS ADAPTIVE LIDAR DATA PROCESSING

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1 Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XXXIX-B3, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa ALTERNATIVE METHODOLOGIES FOR THE ESTIMATION OF LOCAL NT DENSITY INDEX: MOVING TOWARDS ADAPTIVE LIDAR DATA PROCESSING Z. Lar, A. Habb Dept. of Geomatc Engneerng, Unversty of Calgary, 2500 Unversty Drve N.W., Calgary, AB, T2N1N4, Canada (zlar, Commsson III, WG II/2 KEY WORDS: LIDAR, Pont cloud, Processng, Estmaton, Qualty, Analyss ABSTRACT: Over the past few years, LDAR systems have been establshed as a leadng technology for the acquston of hgh densty pont clouds over physcal surfaces. These pont clouds wll be processed for the extracton of geo-spatal nformaton. Local pont densty s one of the most mportant propertes of the pont cloud that hghly affects the performance of data processng technques and the qualty of extracted nformaton from these data. Therefore, t s necessary to defne a standard methodology for the estmaton of local pont densty ndces to be consdered for the precse processng of LDAR data. Current defntons of local pont densty ndces, whch only consder the 2D neghbourhood of ndvdual ponts, are not approprate for 3D LDAR data and cannot be appled for laser scans from dfferent platforms. In order to resolve the drawbacks of these methods, ths paper proposes several approaches for the estmaton of the local pont densty ndex whch take the 3D relatonshp among the ponts and the physcal propertes of the surfaces they belong to nto account. In the smplest approach, an approxmate value of the local pont densty for each pont s defned whle consderng the 3D relatonshp among the ponts. In the other approaches, the local pont densty s estmated by consderng the 3D neghbourhood of the pont n queston and the physcal propertes of the surface whch encloses ths pont. The physcal propertes of the surfaces enclosng the LDAR ponts are assessed through egen-value analyss of the 3D neghbourhood of ndvdual ponts and adaptve cylnder methods. Ths paper wll dscuss these approaches and hghlght ther mpact on varous LDAR data processng actvtes (.e., neghbourhood defnton, regon growng, segmentaton, boundary detecton, and classfcaton). Expermental results from arborne and terrestral LDAR data verfy the effcacy of consderng local pont densty varaton for precse LDAR data processng. 1. INTRODUCTION In recent years, Lght Detecton and Rangng (LDAR) systems are beng extensvely utlzed for rapd collecton of hgh densty 3D pont clouds. These pont clouds provde accurate 3D data for dfferent applcatons such as terran mappng (Elmqvst et al., 2001), transportaton plannng (Uddn and Al- Turk, 2001), 3D cty modelng (Km et al., 2008), hertage documentaton (Patas et al., 2008), and forest parameter estmaton (Danln et al., 2004). The collected data should be processed to extract useful nformaton for the aforementoned applcatons. The nternal propertes of LDAR pont cloud and the performance of processng technques hghly affect the valdty of extracted nformaton. Pont densty s an mportant characterstc of LDAR data that should be consdered durng the varous processng actvtes (e.g., neghbourhood defnton, classfcaton, segmentaton, feature extracton, and object recognton). The majorty of exstng data processng technques assumes that the LIDAR pont cloud has a unform pont densty. However, the collected data mght show varatons n the pont densty due to rregular movements of the acquston platform, varatons n the scatterng propertes of the mapped surface, number of overlappng strps, and ncorporaton of terrestral laser scannng systems (Vosselman and Mass, 2010). Therefore, data processng technques should consder possble varatons n the pont densty wthn the datasets n queston. Ths ablty wll assure the flexblty of these technques n dealng wth datasets captured by dfferent platforms and/or from dfferent sources. The effectve consderaton of these varatons s dependent on provdng standard approaches for the estmaton of local pont densty ndces. So far, only a few applcable methods have been presented for the determnaton of local pont densty ndces. The most commonly used method for the estmaton of local pont densty s the box-countng method proposed by County (2003). In ths method, the LDAR ponts are frstly projected onto a 2D space. Then, the defned 2D space s rasterzed usng a grd wth a predefned cell sze. The local pont densty ndex for the ponts wthn each cell s then determned as the total number of the projected ponts n that cell normalzed by the cell area. The man dsadvantage of ths method s that the estmated local pont densty values are dependent on the sze of the cells and ther placement wthn the projected data. Snce there s no standard for the determnaton of the approprate cell sze and ts placement wthn a LDAR data, dfferent densty values may be estmated for the same ponts n a LDAR dataset (Raber et al., 2007). In order to estmate unque local pont values for ndvdual LDAR ponts, Shh and Huang (2006) proposed a TIN-based method. In ths method, the pont cloud s frstly trangulated usng an advanced Delaunay trangulaton technque (Isenburg, 2006) to produce a TIN. Then, a Vorono Dagram s constructed utlzng the generated TIN structure. The local pont densty ndex for each pont s then estmated by nversng the area of the Vorono polygon assgned to that pont. The presented approaches for the local pont densty estmaton only consder the 2D dstrbuton of the pont cloud. Therefore, they are only vald when dealng wth pont cloud acqured by an 127

2 Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XXXIX-B3, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa arborne system over relatvely flat horzontal terran and cannot be appled for terrestral laser scans. The shortcomngs of the aforementoned approaches mandate the development of alternatve methods for the local pont densty estmaton. In ths paper, new approaches are proposed for the estmaton of local pont densty ndces. These approaches am to derve unque/accurate pont densty values for ndvdual LDAR ponts whle consderng the 3D relatonshps among them and physcal propertes of the surfaces they belong to. Furthermore, the mplcaton of consderng the varyng pont densty n dfferent LDAR data processng actvtes wll be hghlghted n ths paper. The paper starts wth the ntroducton of the proposed approaches for local pont densty estmaton. In the followng secton, the mpact of consderng the local pont densty ndces on some of LDAR data processng actvtes s ponted out and dscussed. In the next secton, the performance of the proposed methods for local pont densty estmaton and the mpact of consderng these local ndces on LDAR data processng results are evaluated through expermental results usng arborne and terrestral LDAR datasets. Fnally, concludng remarks and recommendatons for future research work wll be presented. 2. METHODOLOGY Ths secton ntroduces alternatve methodologes for the estmaton of local pont densty ndces. These methods try to overcome the shortcomngs of prevous approaches by consderng the 3D relatonshps among the ponts and the physcal propertes of the surfaces enclosng the ndvdual ponts. In the followng subsectons, the detaled explanaton of these approaches s presented and ther advantages and dsadvantages are ponted out. 2.1 Approxmate Method In ths method, the local pont densty ndex for each pont s computed whle only consderng the dstrbuton of the ponts wthn ts sphercal neghbourhood. Ths neghbourhood s defned to nclude n-neghbours of the pont n queston ( Fgure 1), where n s a pre-specfed number of neghbourng ponts. Fgure 1. 3D neghbourhood of the pont n queston () For a gven LDAR pont, the local pont densty (LPD) s estmated as follows: n + 1 LPD = (1) π rn 2 r n Where πr n 2 s the area of the crcle centred at the pont n queston wth a radus (r n ) that s equvalent to the dstance from ths pont to ts n th -nearest neghbour. Ths approach provdes a unque estmate of the local pont densty for all the ponts n a LIDAR dataset n a fast and smple manner. Therefore, ths approach can be effcently utlzed for n-flght qualty assessment of the collected LDAR pont cloud. However, t suffers from the followng shortcomngs: It does not consder the physcal propertes of the surface whch the pont n queston and ts neghbourng ponts belong to. It assumes a unform dstrbuton of the ponts n the 3D space defned by the pont n queston and ts neghbourng ponts. 2.2 Egen-value Analyss of the Dsperson of Neghbourng Ponts In order to resolve the frst drawback of prevous method, ths approach estmates the local pont densty only when the pont n queston and ts neghbourng ponts defne a planar regon. In ths method, a sphercal neghbourhood of the pont n queston s ntally defned. Ths neghbourhood encloses the n- nearest neghbours of the pont n queston, where n s the number of ponts needed for relable plane defnton whle consderng the possblty of havng some outlers. The planarty of the establshed 3D neghbourhood s nvestgated usng the egen-value analyss of the dsperson matrx of the (n+1) ponts wthn the sphercal neghbourhood of the pont n queston relatve to ther centrod (C) (Fgure 2). C Fgure 2: Dsperson of the ponts wthn the establshed 3D neghbourhood relatve to ther centrod Ths dsperson matrx (C 1 ) s computed as follows (Pauly et al., 2002): 1 n+ 1 r r r r T C1 = ( )( ) = 1 centrod centrod n + 1 r (2) T where = X Y Z [ ] r 1 n+ 1r, and centrod = = 1 n + 1 The egen-value decomposton of the dsperson matrx (C 1 ) results n three egen values (λ 1, λ 2, λ 3 ). For planar neghbourhoods, one of the egen values wll be qute small when compared to the other two egen values. Ths egen value corresponds to the egen vector whch s perpendcular to the plane passng through those ponts. Once the planarty of the establshed neghbourhood s confrmed, the local pont densty s estmated n the same way as the approxmate approach. Ths approach overcomes the frst drawback of the approxmate method by consderng the planarty of the surface enclosng the pont n queston. However, t stll assumes a unform dstrbuton of the ponts wthn the establshed 3D neghbourhood. Ths approach also has the followng shortcomngs: It does not check whether the pont n queston belongs to the planar surface passng through the neghbourng ponts or not. When the majorty of the ponts n the establshed 3D neghbourhood are coplanar, non-coplanar ponts are consdered n the estmaton of the local pont densty. To resolve these problems, the planarty of the establshed 3D neghbourhood s checked usng the egen-value analyss of the dsperson matrx of the neghbourng ponts wthn the 128

3 Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XXXIX-B3, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa establshed 3D neghbourhood relatve to the pont n queston () (Fgure 3). Fgure 3: Dsperson of the ponts wthn the establshed 3D neghbourhood relatve to the pont n queston In ths case, the dsperson matrx (C 2 ) s derved as follows: 1 n r r r r T C2 = n = ( )( ) 1 r T (3) where = [ X Y Z ] r, and = X Y Z [ ] T Based on egen-value analyss of C 2, the establshed 3D neghbourhood s deemed planar f and only f the pont n queston belongs to the local plane through ths neghbourhood. Once the planarty of establshed neghbourhood s checked, the local pont densty ndex s estmated n the same way as the approxmate method. Despte consderng f the pont queston belongs to planar neghbourhood or not, ths approach s stll subjected to the ncluson of non-coplanar ponts wthn establshed 3D neghbourhood for local pont densty estmaton. 2.3 Adaptve Cylnder Method The adaptve cylnder approach s proposed to overcome the drawback of the prevous method - ncluson of non-coplanar ponts wthn planar neghbourhood - for local pont densty estmaton. In ths method the planarty of the establshed sphercal neghbourhoods s nvestgated by defnng a cylnder whose axs orentaton s adaptvely changng to be algned along the normal to the plane through the majorty of the ponts n the defned neghbourhood (Fgure 4). Ths axs s derved usng an teratve plane fttng procedure where the ponts are assgned weghts that are nversely proportonal to ther normal dstances from the derved plane n the prevous teraton.the cylnder dameter s equvalent to the dstance between the pont n queston and ts n th -nearest neghbourng pont wthn the defned neghbourhood. The heght of the defned cylnder s determned based on the expected nose level n the pont cloud. Fgure 4: Adaptve cylnder neghbourhood In ths approach, the local pont densty ndex s estmated only when the majorty of the ponts wthn the establshed sphercal neghbourhood, together wth the pont n queston, are ncluded n the defned cylnder. The local pont densty ndex s then computed as follows: k LPD = n whch k n (4) 2 πr n Where k s the number of the ponts wthn the defned cylnder and r n s the dstance between the pont n queston and ts n th - nearest neghbourng pont n the sphercal neghbourhood. Ths method estmates the local pont densty only by utlzng the ponts nsde the adaptve cylnder. Ths resolves the drawback of the egen-value analyss methods by removng the ponts whch do not belong to the planar neghbourhood. The only shortcomng of ths method s assumng a unform pont dstrbuton wthn the adaptve cylnder. 3. THE IMPACT OF CONSIDERING LOCAL NT DENSITY INDICES ON LIDAR DATA PROCESSING As mentoned earler, the consderaton of local pont densty ndces wll mprove the LDAR data processng results to a great extent. In ths secton, some of the processng actvtes whch are hghly affected by local pont densty varatons are hghlghted and the mpact of ncorporatng estmated local densty values n these actvtes are dscussed. 3.1 Neghbourhood Defnton Neghbourhood defnton s the prmary step of LDAR data processng. Ths defnton s a rule that determnes the neghbours of each pont, and as a result has a great mpact on the relablty of dfferent processng actvtes results. Dfferent neghbourhood defntons are presently beng used for LDAR data. However, none of them consder the local varatons n the LDAR pont densty. Ths nconsderaton leads to the excluson of requred neghbourng ponts and ncluson of the ponts that should not be consdered for the dervaton of processng parameters (e.g, segmentaton attrbutes). In order to defne meanngful neghbourhoods for ndvdual LDAR ponts, whle consderng the characterstcs of ther assocated surfaces, the computed local pont densty ndces should be consdered. In ths case, when the local pont densty s low, the sze of the defned neghbourhood wll be ncreased to nclude the needed number of ponts for the dervaton of processng parameters. 3.2 Regon Growng Regon growng s recognzed as one of the spatal-doman LDAR data segmentaton approaches. In ths approach, the neghbourng LDAR ponts that fulfl a homogenety crteron (e.g., planarty or smoothness of the surface) wll be segmented n one group (Besl and Jan, 1988). The conventonal crteron for the determnaton of neghbourng ponts, n ths process, s a fxed 3D dstance. However, when the local pont densty s not unform wthn a LDAR dataset, consderng a fxed neghbourhood radus may result n naccurate segmentaton results. In order to defne adaptve neghbourhoods of the seed ponts for regon growng, the local pont densty ndex n the seed pont locaton should be ncorporated n the defnton of the neghbourhood radus. 3.3 Dervaton of Attrbutes for Parameter-Doman Segmentaton Approaches The performance of the parameter-doman segmentaton methods depends on the computed attrbutes for ndvdual laser ponts. In most of these approaches, the segmentaton attrbutes are derved based on the parameters of the ftted plane through the neghbourng ponts wthn the defned neghbourhood for each pont. The qualty of plane fttng process s ensured by provdng the needed number of ponts for the plane defnton. Therefore, the sze of establshed neghbourhood should be made flexble wth 129

4 Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XXXIX-B3, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa local pont densty varatons to nclude the requred number of ponts for relable plane defnton. 3.4 Boundary Detecton The major drawback of parameter-doman segmentaton technques s that the spatal connectvty of ponts belongng to each segment s not consdered. Therefore, the ponts belongng to coplanar but spatally dsconnected planes wll be segmented nto the same group. To resolve such ambguty, a neghborhood analyss s conducted through boundary detecton of the clustered ponts. The process of searchng for each boundary pont s carred out n the local neghbourhood of the prevous boundary pont. In order to defne adaptve neghbourhoods for sequentally fndng the boundary ponts, the estmated local pont densty ndces should be taken nto consderaton. (c) Non- (d) Pnts/m Pnts/m Terran/Off-terran Classfcaton In order to classfy the clusters of LDAR ponts nto those belongng to terran or off-terran objects, the dscontnuty measures between adjacent clusters should be consdered. The adjacency relatonshp between these clusters s defned by analyzng the neghbourhoods of each cluster s boundary ponts. For all the ponts n the boundary of each cluster, there exst neghbourhoods whch nclude the ponts belongng to ts adjacent clusters. In order to defne adaptve neghbourhoods whch nclude the ponts belongng to adjacent segments, the local pont densty ndex at each pont s locaton should be consdered. 4. EXPERIMENTAL RESULTS In ths secton, the performance of the newly developed methods for the estmaton of the local pont densty ndces and the mpact of consderng them on the qualty of LDAR data segmentaton results wll be nvestgated by conductng experments usng arborne and terrestral LDAR datasets. 4.1 Arborne LDAR Data The utlzed arborne LDAR dataset for ths experment (Fgure 7.a) has been collected over an urban area n Swtzerland wth the Scan2Map mappng system. Ths dataset exhbts sgnfcant local pont densty varatons (estmated by the approxmate method) as shown n Fgure 7.b. The results of the planarty check for the ndvdual ponts usng the egenvalue analyss and adaptve cylnder methods are presented n fgures 7.c and 7.e, respectvely. Once the planarty of ndvdual ponts was checked and local pont densty ndces were calculated, the pont densty maps for the ponts belongng to planar surfaces are generated usng estmated local pont densty ndces (fgures 7.d and 7.f). Non Pnts/m 2 (e) (f) Fgure 7. Arborne LDAR dataset: orgnal LDAR data, generated pont densty map usng the approxmate method, (c) planarty check result usng the egen-value analyss relatve to the pont n queston, (d) generated pont densty map based on egen-value analyss relatve to the pont n queston, (e) planarty check result usng the adaptve cylnder method, and (f) generated pont densty map based on adaptve cylnder method Pnts/m 2 To verfy the mportance of the processng of LDAR data whle consderng the estmated local pont densty ndces, the provded arborne datasets s processed usng an adaptve segmentaton approach (Lar et al., 2011). The segmentaton process s carred out wth and wthout consderng local pont densty varatons. Fgure 8.a shows the result of the arborne LDAR data segmentaton wthout consderng local pont densty varatons whle Fgure 8.b shows the result of the segmentaton of the same data consderng the local pont densty varatons. Qualtatve evaluaton of the derved segmentaton results through vsual nspecton of Fgures 8.a and 8.b shows that consderng the local pont densty ndces avods some problems n the segmentaton results, the most vsble one s the over-segmentaton problem as hghlghted wthn the red rectangles. Fgure 8. Arborne LDAR dataset segmentaton results: wthout consderng local pont densty varatons and consderng local pont densty varatons Pnts/m Terrestral LDAR Data 0.12 Pnts/m 2 The terrestral LDAR dataset (Fgure 9.a) has been obtaned from a buldng façade n the Unversty of Calgary campus 130

5 Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XXXIX-B3, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa usng Trmble GS200 3D laser scanner. The local pont densty map of ths dataset, estmated by the approxmate method, s shown n Fgure 9.b. The results of the planarty check for the ndvdual ponts usng the egen-value analyss and adaptve cylnder methods are presented n Fgures 9.c and 9.e, respectvely. The pont densty maps for the ponts belongng to planar surfaces are then generated usng estmated local pont densty ndces (Fgures 9.d and 9.f). (e) Non Pnts/m 2 (c) Pnts/m Pnts/m 2 Non- (f) 0.04 Pnts/m 2 Fgure 9. Terrestral LDAR dataset: orgnal LDAR data, generated pont densty map usng the approxmate method, (c) planarty check result usng the egen-value analyss relatve to the pont n queston, (d) generated pont densty map based on egen-value analyss relatve to the pont n queston, (e) planarty check result usng the adaptve cylnder method, and (f) generated pont densty map based on adaptve cylnder method To assess the mpact of consderng the estmated local pont densty ndces on the qualty of terrestral LDAR data segmentaton results, ths dataset s segmented usng the cted segmentaton approach. The segmentaton process s performed wth and wthout consderng local pont densty varatons. Fgure 10.a shows the result of the terrestral LDAR data segmentaton wthout consderng local pont densty varatons whle Fgure 10.b shows the result of the segmentaton of the same data whle consderng the local pont densty varatons. The comparson of the derved segmentaton results wth and wthout consderng the estmated local pont ndces demonstrates that consderng the local pont densty ndces avods the over-segmentaton problems n the segmentaton results Pnts/m 2 (d) 0.11 Pnts/m 2 131

6 Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XXXIX-B3, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa also lke to thank Dr. Jan Skaloud, EPFL (École Polytechnque Fédérale de Lausanne), Swtzerland for provdng the arborne LDAR datasets. Fgure 10. Terrestral LDAR dataset segmentaton results: wthout consderng local pont densty varatons and consderng local pont densty varatons 5. CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH WORK In ths paper, alternatve methodologes have been presented for the estmaton of local pont densty ndces. These methods try to overcome the shortcomngs of avalable methods by consderng the 3D relatonshps among LDAR ponts and physcal propertes of the enclosng surfaces. In the smplest approach, the local pont densty ndex s estmated whle consderng a pre-defned number of neghbourng ponts to the pont n queston n 3D space normalzed by by the area of the crcle that comprses these neghbourng ponts. Although ths approach s smple and computatonally effcent, t does not take the physcal propertes of the surfaces enclosng the ndvdual ponts nto account. In the other approaches, the planarty of the surfaces enclosng the LDAR ponts s frstly checked through egen-value analyss or adaptve cylnder defnton. Then, the local pont densty ndces are estmated for the ponts belongng to planar surfaces. The man advantage of the egen-value analyss over the adaptve cylnder defnton s the effcent computaton. However, ths approach s not able to flter out the neghbourng ponts whch do not belong to the planar neghbourhood of the pont n queston pror to the estmaton of the local pont densty ndex. In spte of ts computatonal burden, the adaptve cylnder defnton technque can provde more accurate pont densty estmatons by flterng out the outlers that do not belong to the planar neghbourhoods before the computaton of the local pont densty ndces. The other advantage of the ths method s drectly provdng the segmentaton attrbutes through the parameters of the best fttng plane through the ponts wthn the defned adaptve cylnder. In order to demonstrate the mpact of consderng the estmated local pont denstes on the qualty of LDAR data processng, dfferent cases have been dscussed n whch ncorporatng these ndces leads to major mprovements n the derved results. Future research work wll focus on the quanttatve evaluaton of LDAR data processng outcome (.e., boundary detecton, segmentaton and classfcaton) whle consderng the local pont densty varatons and comparatve analyss of these results wth the results from other processng technques. ACKNOWLEDGEMENTS REFERENCES Besl P. J. and Jan, R. C., Segmentaton through Varable-order Surface Fttng, IEEE Transactons on Pattern Analyss and Machne Intellgence, 10(2), pp County, K., LDAR dgtal ground model pont densty, tal_ground_model_pont_densty.html/, KGIS Center, Seattle, WA. Danln, I. M. and Medvedev, E. M., Forest nventory and bomass assessment by the use of arborne laser scannng method, example from Sbera, Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, XXXVI (8/W2), pp Elmqvst, M., Jungert, E., Persson, A., and Soderman, U., Terran modelng and analyss usng laser scanner data. Internatonal Archves of Photogrammetry and Remote Sensng, XXXIV-3/W4, pp Isenburg, M., Lu, Y., Shewchuk, J., and Snoeynk, J., Streamng Computaton of Delaunay Trangulatons, Proceedngs of ACM SIGGRAPH 06, New York, USA, pp Km, C., Habb, A., and Chang, Y., Automatc generaton of dgtal buldng models for complex structures from LDAR data, The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, XXVII (B4), pp Lar, Z., Habb A., and Kwak E., An Adaptve Approach for Segmentaton of 3D Laser Pont Cloud, Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, XXXVII-5/W12, Calgary, Canada. Patas, P., Grussenmeyer, P., Hanke, K., Applcatons n cultural hertage documentaton, Advances n Photogrammetry, Remote Sensng and Spatal Informaton Scences, ISPRS Congress Book, 7, pp Raber, G. T., Jensen, J. R., Hodgson, M. E., Tulls, J. A., Davs B. A., and Berglund, J., Impact of Ldar Nomnal Postspacng on DEM Accuracy and Flood Zone Delneaton, Photogrammetrc Engneerng & Remote Sensng, 73(7), Shh, P.T, and C. M. Huang, Arborne Ldar Pont Cloud Densty Indces, Amercan Geophyscal Unon, Fall Meetng 2006, abstract # G53C Uddn, W. and Al-Turk, E., Arborne LIDAR dgtal terran mappng for transportaton nfrastructure asset management, n Proceedngs of Ffth Internatonal Conference on Managng Pavements, Seattle, Washngton. Vosselman, G. and Maas, H. G., Arborne and Terrestral Laser Scannng. Whttles Publshng, Scotland, UK, 320 p. Ths work was supported by the Canadan GEOmatcs for Informed DEcsons (GEOIDE) Network of Centres of Excellence (NCE) (Project: PIV-SII72), the Natural Scences and Engneerng Research Councl of Canada (Dscovery and Strategc Project Grants), and TECTERRA. The authors would 132