How to Analyze Dstrbuted Streams and Cutting Edge forests in Europe

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1 Automated quantfcaton of dstrbuted landslde movement usng crcular tree trunks extracted from terrestral laser scan data Conner, J. C., & Olsen, M. J. (2014). Automated quantfcaton of dstrbuted landslde movement usng crcular tree trunks extracted from terrestral laser scan data. Computers & Geoscences, 67, do: /j.cageo /j.cageo Elsever Verson of Record

2 Computers & Geoscences 67 (2014) Contents lsts avalable at ScenceDrect Computers & Geoscences journal homepage: Automated quantfcaton of dstrbuted landslde movement usng crcular tree trunks extracted from terrestral laser scan data Jeremy C. Conner a, Mchael J. Olsen b,n a Department of Geography and Envronmental Engneerng, Unted States Mltary Academy, 745 Brewerton Road, Rm 6003, West Pont, NY 10996, Unted States b School of Cvl and Constructon Engneerng, Oregon State Unversty, 220 Owen Hall, Corvalls, OR 97331, Unted States artcle nfo Artcle hstory: Receved 10 December 2013 Receved n revsed form 11 February 2014 Accepted 18 February 2014 Avalable onlne 3 March 2014 Keywords: Laser scannng LIDAR Landslde Coastal Eroson Topography Deformaton modelng abstract Ths manuscrpt presents a novel algorthm to automatcally detect landslde movement n a forested area usng dsplacements of tree trunks dstrbuted across the landslde surveyed repeatedly usng terrestral laser scannng (TLS). Common landslde montorng technques nclude: nclnometers, global poston system (GPS), and nterferometrc synthetc aperture radar (InSAR). Whle these technques provde valuable data for montorng landsldes, they can be dffcult to apply wth adequate spatal or temporal resoluton needed to understand complex landsldes, specfcally n forested envronments. Comparson of the center coordnates (determned va least-squares ft of the TLS data) of a cross secton of the tree trunk between consecutve surveys enable quantfcaton of landslde movement rates, whch can be used to analyze patterns of landslde dsplacement. The capabltes of ths new methodology were tested through a case-study analyzng the Johnson Creek Landslde, a complex, quck movng coastal landslde, whch has proven dffcult to montor usng other technques. A parametrc analyss of fttng thresholds was also conducted to determne the relablty of tree trunk dsplacements calculated and the number of features that were extracted. The optmal parameters n selectng trees for movement analyss were found to be less than 1.5 cm for the RMS resduals of the crcle ft and less than 1.0 cm for the dfference n the calculated tree rad between epochs. & 2014 Elsever Ltd. All rghts reserved. 1. Introducton Landsldes are persstent natural hazards that result from downward sldng of an earth mass. Landsldes can be trggered by a varety of factors such as precptaton, groundwater fluctuatons, sesmc actvty, eroson, and human actvtes whch destablze the slope through geometrc or loadng changes (Fernandez Merodo et al., 2004). They have both drect and ndrect effects on people and the envronment. Large landsldes can destroy or damage everythng n ther path when a falure occurs (.e., people, roads, houses). Further, the mmedate mpact of a landslde s often overshadowed by the aftermath. Landsldes often damage or block roadways that are necessary to lnk remote populaton centers, causng hardshp for everyone affected Landslde montorng Landslde movement s often determned be a wde varety of montorng technques, ncludng nclnometers, Global Postonng n Correspondng author. E-mal addresses: jeremy.conner1@us.army.ml (J.C. Conner), mchael.olsen@oregonstate.edu (M.J. Olsen). System (GPS), Interferometrc Synthetc Aperture Radar (InSAR), and Lght Detecton and Rangng (LIDAR). These technques each have postve and negatve attrbutons for landslde montorng dependent on landslde characterstcs. Ths secton wll dscuss these technques, wth the excepton of LIDAR, whch wll be addressed n more detal n the followng secton. Weczorek and Snyder (2009) descrbe usng nclnometers to montor movement at dscrete locatons on a landslde by placng a hollow metal tube nsde a drlled hole and perodcally montorng the varaton of the orgnal nclnaton of the tube. Although nclnometers provde subsurface detal of landsldes, whch other technques do not capture, they have poor spatal resoluton. They can also break when a landslde moves too quckly. Permanently mounted GPS unts have been used to montor surfcal landslde movement by analyzng postonal changes of the GPS unts compared to stable unts n a GPS network. Wang (2011) showed landslde movement could be determned by GPS wthn 2 mm horzontally and 6 mm vertcally wth four hour observatons and an open vew of the sky. However, the cost of permanently mountng the survey-grade GPS unts necessary for ths detecton s too hgh to mount more than a few recevers on a sngle slde, lmtng spatal resoluton and the number of landsldes that can be montored. Often ths s only sutable for landsldes near hgh populaton areas & 2014 Elsever Ltd. All rghts reserved.

3 32 J.C. Conner, M.J. Olsen / Computers & Geoscences 67 (2014) Interferometrc Synthetc Aperture Radar (InSAR) determnes dsplacement from the phase change between radar mages. Rosen et al. (2000) descrbes how vegetaton canopes affect InSAR magng by reportng a heght of somewhere between the ground and the top of the canopy and reduces correlaton between mages because of volumetrc scatterng. Hence, t can be very dffcult to apply for landslde montorng n heavly forested envronments. InSAR provdes ncreased spatal coverage, but s lmted n temporal coverage by repeat passes LIDAR background LIDAR s an actve optcal remote sensng technology that measures the dstances and angles to objects from a scanner to create complete 3D models of XYZ coordnates, termed pont clouds. A laser pulse s emtted from the scanner, reflects off a target, and returns to the scanner, provdng the two way travel tme used to determne the dstance from the scanner for each target. Scanners are a lne of sght technology: f the complete laser pulse reflects from an object, no ponts are detected behnd the object, creatng an occluson (shadow). When only part of the laser pulse reflects back from a small object, the remanng lght contnues and multple XYZ coordnates (returns) can be obtaned from one laser pulse (Renslow, 2012). Multple returns enable mproved penetraton of vegetaton canopy compared to many other technques. LIDAR has proven to be an effectve tool for landslde analyss rangng from detecton and characterzaton of mass movement and montorng at the regonal scale wth arborne laser scannng (ALS) to terrestral laser scannng (TLS) provdng ste specfc detals at mproved resolutons (cm level) (Jaboyedoff et al., 2012). TLS has been successfully mplemented for geologcal characterzaton and assessment of landsldes (Collns and Sttar, 2008; Collns and Stock, 2012; Dunnng et al., 2010). Laserscannershavealsoproventobeaneffectvewaytomontor coastal eroson and clff collapses (Olsen et al., 2009; Rosser et al., 2005; Young et al., 2009). Young et al. (2010) performed a comparson between TLS and ALS for sea clff eroson analyss and concluded that ALS has superor coverage capturng the clff-top and crest, whch s useful for detectng large or deep seated landsldes but may not detect surfcal landsldes, eroson hotspots, or detaled change thatcanbepckedupbyatls.montorngareasofncreasederoson and detaled change s necessary to understand the landsldes occurrng along the Oregon coast. Several agences have started usng moble (vehcle-based) laser scannng (MLS) n aspects such as asset nventory (Olsen et al., 2013a). For example, Lehtomak et al. (2011) presents the applcaton of usng segmentaton to extract poles and tree trunks from urban areas usng MLS. Segmentaton was used due to a lower pont densty than TLS and nose, whch allows for only part of the cylndrcal targets to appear. Although some trees were detected that were unformly spaced along the road, the tree canopes were problematc. Hence, ths approach s not suted for a heavy forest envronment Study area The northern Oregon coastlne extends south from the mouth of the Columba Rver to Florence. Landsldes are a persstent problem along the Oregon coast due to weak sols and hgh concentraton of erosonal processes, resultng n slope falures and coastal eroson. Eroson on the toe (base) of the sea clff causes destablzaton and can result n the formaton of notches or sea caves. North and Byrne (1965) determned that land sldng s actve along 130 of the 240 km of coastlne. Further, addtonal landsldes can be trggered by sesmc sources, such as the Cascada Subducton Zone, whch extends under the coast range where the North Amercan tectonc plate s overrdng the Juan de Fuca plate km west of the coast (Mtchell et al., 1994). Wthn Lncoln County, Oregon there are several translatonal landsldes movng through Tertary (6 63 mllon years old) sedmentary rocks wth coastal bluffs m hgh (Prest et al., 2011). Prest and Allan (2004) descrbe these landsldes from the Mocene age as thck to thn-bedded, very fne to medum graned, mcaceous and carbonaceous arkosc sandstone and massve slty sandstone and are common wth sngle block falures exceedng 100 m n wdth. One of the most wdely researched landsldes along the Oregon coast s the Johnson Creek Landslde (JCL), located about 2 km south of Otter Rock and 11 km north of Newport along Hghway 101 (Prest et al., 2006; Schulz and Ells, 2007; Prest et al., 2011; Schulz et al., 2012). JCL s a translatonal, seaward-dppng landslde dsplacng through a coastal bluff consstng of Mocene sltstone and sandstone overlan by Quaternary marne terrace deposts (Prest et al., 2011). Thesenvestgatonshavefoundthatthebasalsldeplanegenerally runs parallel to the dp of the Mocene rocks; however, the slde plane slopes nward toe block whch tlts backwards. The prmary extents of the landslde measures approxmately 360 m wde, 200 m long (Schulz and Ells, 2007). Pror subsurface exploraton of JCL has characterzed and montored the landslde movement (Landslde Technology, 2004; Prest et al., 2008; Schulz and Ells, 2007). However, these efforts have been met wth dffculty. Eroson pns were ntally ntended to montor eroson, but too many pns were lost over the frst wnter season, preventng accurate determnaton of the total amount of eroson. Inclnometer casngs were nstalled to measure landslde movement, but movement was so excessve that t prevented the nclnometer survey; therefore, manual extensometers of wre rope were nstalled to obtan measurements of movement. Landslde Technology (2004) and Prest et al. (2008) performed slope stablty analyses to evaluate the nfluence of groundwater condtons, geotechncal parameters and toe eroson on the amount of landslde movement. The slope stablty analyses determned the landslde s least stable n the southern porton of the landslde and ncreases n stablty movng northward. Prest et al. (2008), Olsen et al. (2012), and Olsen (n Press) used TLS to model the eroson of the bluff face as well as quantfy landslde movement. Olsen et al. (2012) estmate landslde movement by manual extracton and comparson of features (houses, trees and starwells) along the crest of the coastal bluff face of the landslde, concludng that areas of ncreased landslde movement also experence more eroson. Hence, for mproved understandng and representaton of the landslde movement, dsplacement needs to be montored throughout the entre landslde area because of the varable movement Purpose The am of ths research s to develop an automated algorthm that determnes landslde movement along an erodng coast n a forested area usng dense, tme-seres data acqured usng terrestral laser scannng. Specfcally, key objectves were to: Develop a consstent, systematc, montorng technque usng exstng (natural), durable features snce artfcal nstrumentaton s often destroyed from landslde movement. Map dsplacement across the slde so that one can dentfy dstnct landslde blocks and understand the complextes of non-unform landslde movement. Dstngush between eroson and landslde movement components of change observed between repeat surveys. Evaluate the senstvty of methodology to nput parameters.

4 J.C. Conner, M.J. Olsen / Computers & Geoscences 67 (2014) Methodology To demonstrate the feasblty of the proposed methodology, the JCL, a hghly actve landslde, was selected as a test ste. Trees are rgd natural features that can wthstand a sgnfcant amount of movement. Cross sectons of tree trunks are also nearly crcular n shape, enablng detecton and extracton from a pont cloud. Trackng the changes n the center coordnates of the crcle over tme provdes a dsplacement vector. Multple trees can then be detected across the ste to map the entre landslde and examne varablty n movement. An overvew workflow for extractng dsplacements s outlned n Fg Feld collecton Fg. 2 shows the locatons from whch scans were obtaned and methods used to obtan the coordnates for each survey. Control ponts were setup usng the Oregon Real-tme GPS Network (ORGN), provdng RTK GPS. For the RTK-GPS determned postons, three, one-mnute readngs per setup were collected, checked for consstency, and averaged. Scans conducted on the beach were completed wth the GPS mounted on top of the scanner. On top of the sea clff, vegetaton and tree cover prevented GPS acquston from all scan postons, requrng the use of a total staton to te nto the control ponts. A Regl VZ-400 scanner (nomnal measurement accuracy of 5 mm, 1 standard devaton, s) was used for all scans; a fve mnute 3601 scan wth angular resoluton of was collected from each scan poston. Each scan collected an average of 27.5 mllon ponts per scan resultng n approxmately 700 mllon ponts per survey. Total staton, a total postonng system that collect dstances and angles, measurements were used to establsh coordnates for 1.Perform Surveys (Feld Collecton) Terrestral laser scannng Total staton & GPS control network 2.Geo-reference Scans (Olsen et al. 2011a) Translaton XYZ GPS coordnates Rotaton XY-Inclnaton sensor readngs Rotaton Z manually estmated wthn 1 o Automated least-squares refnement 3.If needed, dvde the dataset nto tles for workablty 4. Generate Dgtal Terran Model (DTMs) for each tle and merge 5. Extract slce of pont cloud above DTM 6.Fnd trees n datasets Least-Squares Ft Crcles to Ponts on Indvdual Trees 7. Compare tree locatons between surveys to extract movement Fg. 1. Workflow dagram for tree movement detecton. Steps done automatcally are shown wth small dashes and sem-automatc steps are shown wth large dashes. Fg. 2. Test ste layout showng GPS, total staton, and scanner orgns. ( , ). Base map photographs provded by ESRI. scan orgn markers placed across the ste on top of the bluff. A Leca TPS 1200þ seres total staton was set up at two locatons to determne coordnates of each of the 15 scan postons (reference ponts) on top of the sea clff. The total staton data collected was ntally stored n a local coordnate system. The nstrument heght was measured and recorded three ndependent tmes varyng by less than 0.1 cm, the average of the three readngs was used as nstrument heght. The scanner was setup on a trpod over each reference pont. The nstrument heght was measured and recorded three ndependent tmes varyng by less than 0.1 cm wth the average recorded as the nstrument heght. GPS control coordnates were obtaned from three locatons wth relatvely clear vews of the sky near the boundary of the landslde area. These three postons were also used as scan postons, as well as to constran the overall network, as shown n Fg. 2. Geomorphologcal data were acqured along the beach to montor eroson magntudes and patterns on the bluff face. The scanner was mounted to a wagon to speed transport between scan postons. The scans were conducted about 40 m from the clff and spaced 50 m from one another. Olsen et al. (2009) descrbes these as the optmal zone for TLS of a dynamc coastal settng smlar to JCL area Geo-reference scans In order to compare changes between surveys the data requres geo-referencng nto a common coordnate system. Frst, the three RTK GPS control ponts located on top of the landslde n an area wth a clear vew of the sky were used to transform the total staton ponts nto a global coordnate system. The GPS data for the Aprl 2012 survey, vared by less than 1.5 cm for horzontal observatons and vertcal observatons vared up to 5 cm. For the November 2012 survey, horzontal observatons vared by less than 2 cm and vertcal observatons vared up to 5 cm. For both surveys, the observatons from each control pont were averaged for the

5 34 J.C. Conner, M.J. Olsen / Computers & Geoscences 67 (2014) fnal GPS locaton. The total staton network was adjusted to the GPS ponts through a least-squares ft. For the Aprl 2012 survey, horzontal resduals were less than 0.6 cm and vertcal resduals were less than 5 cm. For the November 2012 survey, resduals were less than 2.5 cm (horzontal) and 5 cm (vertcal). The adjusted ponts are the scan locatons at ground level. In order to offset the scanner heght, the measured nstrument heght s added to the adjusted ponts, resultng n coordnates for the scanner orgns. PontReg, an automated program, nputs the scanner orgn coordnates (X, Y, and Z) obtaned usng the prevously descrbed technques, the roll and ptch from the nclnaton sensors of the scanner for levelng nformaton and an ntal yaw estmate (obtaned by manually rotatng the scans about the Z-axs to wthn a few degrees) to geo-reference all the scans (Olsen et al., 2009, 2011a). The algorthm performs a least-squares adjustment to determne the optmal yaw angles of all scans. The yaw angle for each scan poston s determned by comparson each scan wth multple neghborng scans, whch mnmzes error propagaton throughout the scans Create tles Once the scans are geo-referenced, the Tle Creator dvdes the scan data nto tles f there are too many ponts n the entre dataset to create a DTM of the whole test ste at one tme due to memory constrants. It frst calculates the extents of the area to flter by usng the coordnates for each scan orgn, buffered by a predetermned scanner range dstance, r f, to whch the scans wll be fltered. For the JCL ste, the fltered area was evenly dvded nto segments along the Y drecton nto tles of equal area. Each data pont s read n one by one and wrtten out to the fle assocated wth the specfc tle. If a pont s outsde the fltered area t s no longer used. For ths case study, three tles were used wth a scanner range dstance of 100 m Create dgtal terran model A dgtal terran model (DTM) s then created for the data wthn each tle. The terran model was created by usng a statstcal flterng algorthm called Bn N Grd. Olsen (2011b) provdes detals about how the algorthm works and ts capabltes to flter out vegetaton to obtan the ground surface. By grddng the data n large cells of 2 m by 2m,andfndng the mnmum elevaton n each grd to represent the ground surface, most of the landslde area s represented as the ground surface wthout vegetaton. (Note that ths flterng process works very well for arborne laser scans, but can have problems for small cell szes(o10 cm) usng terrestral laser scans n dense forests due to the varablty n pont densty and look angle.) The surface models created for each tle are then merged together to form a contnuous DTM of the entre landslde area. A hghly detaled DTM s not requred for the horzontal dsplacement evaluated n ths study, but could provde vertcal dsplacement nformaton for the landslde Extract slce above surface The Slce extracts a thn slce of pont cloud data at a specfc heght, h, above the DTM (Fg. 3). The purpose of extractng a slce of data s to solate ndvdual trees and mnmze the nose (ponts not actually on the tree trunks). Hence, the slce deally should be taken above shrubs and other short vegetaton on the ground, but also below the canopy. However, to mnmze rotatonal effects and wnd-nduced dsplacements, the slce needs to be as close to the ground as possble. The ponts are fltered to satsfy the followng crtera. h Δhoðh ¼ z z DTM ÞohþΔh ð1:1þ Fg. 3. Example tree n the dataset llustratng the slce taken wthn the dashed rectangle. where h¼heght above DTM¼2.5 m for ths study, Δh¼0.5 slce thckness¼0.05 m for ths study, h ¼heght of pont, z ¼elevaton of pont, and z DTM ¼elevaton on DTM at pont Ft crcles to trees The Trees functon searches through the Slce data to locate and ft crcles to trees. It frst reads n the Slce data and determnes the extents of the dataset, whch s then used to generate a hash table (organzatonal structure to enable effcent searchng) based each pont's locaton wthn a grd. Intally, the grd s set up usng the mnmum and maxmum values of the X and Y coordnates as the extents of the grd. The cell sze, Δ, wthn the grd s equal to the typcal tree radus, r typ. After the grd s establshed, each pont from the dataset s ndexed nto the approprate grd cell. Trees systematcally searches through the data n sets of nne grd cells, a center cell and all of ts closest neghbors (Fg. 4). The algorthm starts n the lower left corner of the grd and moves through each row n the column. Upon reachng the top, t proceeds wth the column to the rght. Wthn each set of nne grd cells the numbers of ponts are determned. When there are more than N mn ponts (70 for ths dataset), 2D crcles (gnorng Z values because the slce s thn) are created through the ponts usng a least-squares ft to all the data ponts. The ntal crcle s determned n a local coordnate system (u, v) by subtractng the averages, _x and _y, from x and y for each pont. The center of crcle (u c, v c ) s determned by mnmzng the summaton of resduals, S, n Eq. (1.2) (Bullock, 2006). The resduals are determned by takng partal dervatves wth respect to S and settng t equal to zero, resultng n the best ft crcle usng Eq. (1.3). Then, n the orgnal coordnate system the center of the crcle (X c, Y c )¼(u c, v c )þ(_x, _y). S ¼ ððu u c Þ 2 þðv v c Þ 2 r 2 Þ 2 S uu S uv where: S uu ¼! S uv uc S vv v c! ¼ u 2 ; S uv ¼ 0:5ðS! uuu þs uvv Þ 0:5ðS vvv þs vuu Þ u v ; S vv ¼ v 2 ð1:2þ ð1:3þ

6 J.C. Conner, M.J. Olsen / Computers & Geoscences 67 (2014) landslde translatonal dsplacement f crcles are not extracted close to the ground. X 3. Results and dscusson 3.1. Landslde movement across slde Fg. 4. Example grd of dataset wth the red (dark shaded) grd cell s the center grd and the orange (shaded) grd cells are the closest neghbors. (For nterpretaton of the references to color n ths fgure legend, the reader s referred to the web verson of ths artcle.) S uuu ¼ u 3 ; S vvv ¼ v 3 ; S uvv ¼ u v v ; S vuu ¼ v u u The X and Y coordnates for the center of the crcle (X c, Y c ) and the radus, r, are then calculated as the parameters of the best ft crcle. The root mean square error (RMS) of the nput ponts to the crcle ft can be calculated. Each pont s examned ndvdually and f the pont s not wthn r72 RMS, t s marked as an outler for the next teraton. The fttng process s then reterated untl the RMS of the ft s deemed acceptable by a user defned threshold (1.5 cm recommended for ths study) and outlers are removed. The fnal crcle s then only used f (X c, Y c ) are located wthn the center grd cell to avod redundancy and ensure an optmal ft Compare tree locatons between surveys Followng fttng crcles to the tree trunk cross-secton, the estmated dsplacement of the trees s measured by comparng the center of the crcles between two surveys. The tree locatons and rad for all the trees n both surveys are read by the movement program. Movement looks at each tree from the frst survey and fnds the closest tree to t from the second survey, wthn the same grd cell or ts nearest neghbor grd cell. The rad of the two trees are compared (Δr) to ensure the two trees are reasonably the same tree, 71 5 cm, dependng on user preference. After t s determned the two trees are lkely the same tree, the horzontal dsplacement, δ, s determned wth drectonal components: δ X and δ y, and total horzontal dsplacement, δ XY.Afle contanng the X c, Yc, Z c,r, N mn (the number of ponts used for the crcle ft), and the RMS ft for each crcle ft as well as δ X, δ y, and δ XY s wrtten. The algorthm was appled to JCL to test ts effectveness n determnng landslde movement, ncludng dstngushng varable movement across the slde. To estmate the amount of landslde movement takng place, coordnates for the center of the trees were compared between consecutve surveys. Negatve δx values ndcate movement to the west and postve values ndcate movement east. Postve δy values ndcate movement north and negatve values ndcate movement south. Fg. 5 shows the landslde movement between Aprl 2012 and May 2013 surveys. The majorty of the trees show movement between 6 and 14 cm. The area wth the greatest amount of movement s located on the western sde of the landslde near the bluff face. Trees were defned wth expected results when: 0.14oδx (m)o0, 0.1oδy (m)o0, and 0.15rδxy (m). For the 39 trees detected at h¼2.5 m, RMSr1.5 cm and Δrr1 cm, 80% of the trees have expected results. These trees provde the capablty to montor more locatons than are typcally montored through tradtonal methods for ths ste, enablng quantfcaton across more of the landslde area. For example, Fg. 5 shows a block next to the sea clff that s advancng at a faster rate than the rest of the landslde Eroson on bluff face Fgs. 6 and 7 show the results of a change analyss of a trangulated surface model (created usng the surfacng method descrbed n Olsen et al., 2013b) of the bluff face for November 2012 and May 2013 surfaces respectvely, wth dfferences up to 50 cm. For ths analyss, the Aprl 2012 model was used as the base survey when compared to the November 2012 and May 2013 surveys. The negatve numbers are 2.8. Assumptons and lmtatons Whle ths algorthm can detect the movement of the tree trunks, there are several assumptons and lmtatons to consder: the tree trunks can be modeled as crcles wth mnmal error (RMSo1.5 cm). In many cases, trees do not grow completely vertcal, so f a large cross-secton s used, an ellpse may be requred; the tree's best ft crcle occurs when ts coordnates are located n the center grd, rather than a neghbor grd; trees are spaced, s, farther apart than at least 1.5 tmes the typcal tree dameter, d. In other words, trees are not too close together; the dsplacement, δ, s less than cell sze, Δ. Ths requres good temporal resoluton to trace the paths of trees; wth sgnfcant tme, movement may exceed the cell sze, Δ; and the landslde s only montored at the surface; no subsurface nformaton s obtaned. Further, f there s a sgnfcant rotaton component to the landslde, the technque may overestmate Fg. 5. Landslde total dsplacement, δ xy. Base map photographs provded by ESRI.

7 36 J.C. Conner, M.J. Olsen / Computers & Geoscences 67 (2014) Fg. 6. Change analyss between laser scan surveys showng advance (landslde movement, accreton) and retreat (eroson) of the clff face between Aprl 2012 and November (See Fg. 2 for locaton of each sub-secton). Fg. 7. Change analyss between laser scan surveys showng advance (landslde movement, accreton n blue) and retreat (eroson n orange) of the clff face between Aprl 2012 and May (See Fg. 2 for locaton of each sub-secton). shown n orange and represent a retreat from the ocean due to eroson exceedng landslde movement. Postve numbers are shown n blue and represent advancement toward the ocean (landslde movement and accreton exceeds eroson) and green represents values exceedng the 50 cm threshold. In general, the upper slopes of the bluff face have retreated from subaeral mechansms (e.g. precptaton). The

8 J.C. Conner, M.J. Olsen / Computers & Geoscences 67 (2014) Table 3-1 Compared dsplacement of total staton and GPS network to closest tree. Conventonal survey Closest tree Absolute dfference Dstance from tree (m) Pont δx (m) δy (m) δxy (m) δx (m) δy (m) δxy (m) δx (m) δy (m) δxy (m) Max Mn Average Std dev advancement at the base of the clff s buldup of talus deposts from the eroded materal above Valdaton Surveys conducted n Aprl 2012 and November 2012 were used for all valdaton. For the surveys conducted at JCL, the uncertanty of the tree movement determned by ths new technque was evaluated by comparng the movement at 10 ponts usng tradtonal surveyng technques, based on the same RTK control, to the movement of the closest vald tree (Table 3-1). Fve addtonal ponts were ntally set, but destroyed between surveys (from road re-surfacng), preventng comparson. The results show that the average dfferences n the amount of movement calculated for δ X, δ y, and δ XY are 1.7, 1.8 and 1.4 cm wth a standard devaton of 71.5, 1.3, and 1.2 cm respectvely. These fall wthn the antcpated error budget of the total staton network ted to RTK GPS control ponts. The largest error s n the GPS control ponts, followed by the total staton network. The next largest source of error s n the process of fttng crcles to a cross-secton of data to represent tree trunks Parametrc analyss Selectng the approprate heght above the DTM, h, to take a slce of data, t, and an acceptable RMS value for the crcle ft sa balance between parng enough trees and rejectng false tree parngs and poor crcle fts. The heght above the DTM for the slce s hghly dependent on the type and varablty of vegetaton n the forested area and canopy of the trees. Fg. 8 shows the relatonshp between the number of trees detected, M, crcle ft RMS for the trees, and the relablty of the trees for varous Δr curves and wth dfferent h values. Relablty was determned by dvdng the number of trees wth expected results by the total number of trees. As the acceptable RMS value ncreases, more trees are pared together, but the relablty of the trees decrease. When a slce of data s taken wthn the vegetaton (h¼2.0 m) fewer trees are pared at low RMS values. As the RMS value ncreases, the relablty of the trees rapdly decreases. Wth hgh h values (6.0 m), when the slce s n the canopy of the trees, the relablty of the trees s about 5% lower than a slce taken between the vegetaton and the canopy (h¼2.5 m or 4 m). For all cases above the vegetaton the number of trees dentfed ncreases sgnfcantly when usng a RMS threshold value between 0.5 and 1.0 cm. After a RMS value of 1.5 cm, the number of trees starts to level off and few addtonal trees are found. The Δr parameter produces the largest ncrease n the number of trees when ncreased from 0.5 cm to 1.0 cm, wth the lowest mpact on relablty. Optmal use, provdng the most trees wth the hghest level of relablty, s where both curves start to level off, whch s at a RMS value of 1.5 cm and a Δr value of 1.0 cm. Usng the optmal parameters provdes more montorng locatons (relable trees) than typcally obtaned usng current methods for all h values Tme analyses The tme ths methodology takes to determne movement for a landslde wll vary based on the number of trees and the sze of the datasets. However, for ths case study ( 700 mllon data ponts per survey), usng an Intel(R) Xeon(R) quad-core CPU wth a 64-bt operatng system, 24 GB RAM and sold state hard drves, t took about 20 mn to tle the data. Generatng the DTM for the test ste took about 15 mn and extractng a slce from both datasets took about 15 mn. Fndng trees took less than 10 mn and comparng the locaton of the trees took less than a mnute. Overall, after the data was processed ths methodology shows landslde dsplacement results n about one hour, whch s mostly computer processng tme for the algorthm rather than user nput. 4. Concluson By usng natural features such as trees to montor landslde movement, TLS surveys can effcently model landslde dsplacements across the entre landslde area. The method detaled n ths paper demonstrates TLS surveys of natural features n a forested envronment can quantfy landslde dsplacement, consstent wth tradtonal survey devces. In partcular, the methodology enables dsplacements to be accurately and relably detected n areas of heavy vegetaton, whch can be dffcult, f not mpossble, to obtan wth other technques. The JCL shows dsplacements of 6 14 cm throughout most of the landslde area, whch are very smlar results to the dsplacement determned by a conventonal total staton. Further research should be conducted to analyze the mechancs of JCL to evaluate the separate blocks observed n Fg. 5. Now that a baselne for the entre JCL has been establshed, future TLS can be used to evaluate the seasonal effects and longer term movement rates of ths landslde. From ths case study, t was noted that t s also mportant to collect data outsde the extents of the test ste to valdate the methodology and determne the mpacts the landslde movement has on surroundng landmass.

9 38 J.C. Conner, M.J. Olsen / Computers & Geoscences 67 (2014) Number of trees detected 200 h = 2.0 m Relablty (%) 100% h = 2.0 m 95% 90% 85% 80% 75% 70% 65% 60% 55% 0 50% Number of trees detected 100 h = 2.5 m Relablty (%) 100% h = 2.5 m 95% 90% 85% 80% 20 75% % Number of trees detected h = 4.0 m Relablty (%) 100% 95% 90% 85% 80% 75% h = 4.0 m 0 70% Number of trees detected h = 6.0 m Relablty (%) 100% 95% 90% 85% 80% 75% h = 6.0 m % Fg. 8. (a) Number of trees detected at 2.0 m above DTM based on crcle ft RMS for varous Δr curves. (b) Relablty of trees detected at 2.0 m above DTM based on crcle ft RMS for varous Δr curves. (c) Number of trees detected at 2.5 m above DTM based on crcle ft RMS for varous Δr curves. (d) Relablty of trees detected at 2.5 m above DTM based on crcle ft RMS for varous Δr curves. (e) Number of trees detected at 4.0 m above DTM based on crcle ft RMS for varous Δr curves. (f) Relablty of trees detected at 4.0 m above DTM based on crcle ft RMS for varous Δr curves. (g) Number of trees detected at 6.0 m above DTM based on crcle ft RMS for varous Δr curves. (h) Relablty of trees detected at 6.0 m above DTM based on crcle ft RMS for varous Δr curves.

10 J.C. Conner, M.J. Olsen / Computers & Geoscences 67 (2014) It has not only been shown that landslde movement can be detected usng tree trunks, but that the qualty of the modeled dsplacement depends on the RMS of the crcle ft and the dfference n the rad of trees between surveys. Understandng how these parameters affect the qualty of the tree parng wll allow researchers to accurately model landslde movement n forested envronments where GPS and other tradtonal devses are unsuccessful or unfeasble. Although the optmal parameters for JCL are a RMS value less than 1.5 cm and rad dfference less than 1.0 cm, ths wll change based on each forested area. Therefore, t s recommended users mplement a smlar analyss to determne ther optmal parameters for ther study area. Whle ths s a short term study, ths research paves the way for addtonal work n understandng long term trends usng the automated, systematc methodology presented heren. Surveys completed wth short tme ntervals wll enable researchers to montor the dsplacement of ndvdual trees over long perods of tme to gan an mproved understandng of the mechancs of an entre landslde. Fnally, the methodology proposed s most approprate for translatonal landsldes rather than rotatonal landsldes or those wth sgnfcant vertcal deformaton. However, extractng two slces at dfferent heghts may assst n determnng f there s a large rotatonal component to the landslde. Acknowledgments The authors would lke to thank Leca Geosystems, Davd Evans & Assocates, and Maptek I-Ste for provdng the equpment and software used for ths research. Addtonally, the Unted States Army, Erc HI and Jance Hoffman and Oregon Department of Transportaton (SPR 740) provded fundng for ths study. Thank you to Jonathan Allan and George Prest for a thorough Johnson Creek Landslde ste vst physcally showng and explanng prevous work done and the geologcal settng. 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