Western Coastal and Marine Geology
Geoengineering Research



Robert Kayen1, Robert T. Pack2, James Bay2, Shigetoshi Sugimoto3, and Hajime Tanaka4

1U.S. Geological Survey, Menlo Park, CA
2Utah State University, Logan, UT
3Kobe University, Kobe, Japan
4Tokyo University , Tokyo, Japan

Reprinted from "Earthquake Spectra"

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Horinouchi Landslide

  • High Resolution-NW View (QuickTime) 158.8 MB

White Rock Landslide at Ojiya

  • High Resolution-NW View (QuickTime) 374.8 MB

Juetsu Railroad portal at Kita-Horinouchi

  • High Resolution-NW View (QuickTime) 190.0 MB


The 23 October 2004 Niigata Ken Chuetsu, Japan, Mw 6.6 Earthquake was the most significant earthquake to affect Japan since the 1995 Kobe earthquake. Landslides and permanent ground deformation caused extensive damage to roads, rail lines and other life-lines, resulting in major economic disruption. The cities and towns most significantly affected by the earthquake were Nagaoka, Ojiya, and the mountainous rural areas of Yamakoshi village and Kawaguchi town. In this paper, ground-based scanning-laser LIDAR technology (LIght Detection And Ranging) is used to create ultra high-resolution three-dimensional digital terrain models of earthquake damage.  This new technology allows for the immediate detailed collection of earthquake detailed port-earthquake failure geometries prior to its modification by post-disaster recovery efforts and natural processes. Two reconnaissance teams traveled with tripod-mounted LIDAR that are successfully documented earthquake-damaged surfaces including ground, structures, and life-lines.

The two similar tripod-mounted LIDAR systems used in this study both have range accuracies of approximately 2.5 cm, and can illuminate targets up to 400-700m away from the sensor depending on the target reflectivity and atmospheric conditions to collect spatial and color data.  During several minutes of LIDAR scanning, several million survey position points are collected and processed into an ultra-high resolution terrain model. This study documents several benefits of LIDAR in the initial phase of an earthquake reconnaissance effort.  First, the detailed failure morphologies of damaged ground and structures can be measured remotely and in a way that is either impractical or impossible by conventional survey means.  Second, the ultra-high resolution of objects allows the exploration and visualization of damage on a computer screen in orientations and scales not previously possible.  This ability allows one to better define failure surfaces, deformation patterns, and morphologies required to understand failure modes.  Finally, LIDAR allows for the archiving of 3-D terrain models so that the engineering community can evaluate analytical and numerical models of deformation against detailed field measurements.  In this paper, we present LIDAR-based damage-visualization from the 2004 Niigata Chuetsu Earthquake (M6.6) reconnaissance effort. High resolution images and movies of LIDAR data can be viewed at http://walrus.wr.usgs.gov/geotech/Niigata/ and the online pages of Earthquake Spectra.


The Mw 6.6 earthquake that struck Niigata Prefecture on the evening of October 23, 2004, was the most significant earthquake to affect Japan since the 1995 Kobe earthquake. Forty people were killed, almost 3,000 were injured, and numerous landslides destroyed entire upland villages. Total damages are estimated by Japanese authorities at US$40 billion, making this the second most costly disaster in Japanese history, after the 1995 Kobe earthquake.  The epicenter was in northwestern Honshu, about 80 km south of Niigata City (population 500,000) beneath the Uonuma Hills east of the Shinano River Lowland (Figure 1).

Landslides that occurred during the earthquake were of many types: earthflows, debris slides, debris flows, earth slumps and lateral spreads.  The landslides also had a variety of effects: some dammed streams, creating new lakes likely to overtop their new embankments at any moment and cause flash floods and mudslides; others buried houses and roads; and some permanent ground deformations damaged roads, rail lines and other lifelines, resulting in major economic disruption. The behavior of the numerous landslides was influenced, in part, by heavy rain associated with Typhoon Tokage. At Nagaoka City, there had been 100 mm (4 inches) on October 20 and 13 mm (.5 inch) on October 21.

Index map of central Niigata Prefecture, with LIDAR scan locations.

Figure 1. Map of affected area in central Niigata Prefecture, Japan, including the tracklines of the air and ground reconnaissance, and the locations of LIDAR scans presented in the paper.


In past studies, LIDAR has proven useful in engineering geologic applications (Pack, 2002) and for the monitoring of ground movements (Kayen et al, 2004; Bawden et al. 2004).  LIDAR systems have been applied to landslides in Europe (Paar et al, 2000; Scheikl et al., 2000; Rowlands et al. 2003), and in the U.S. (Collins and Sitar, 2004).  In this paper the focus of the application of LIDAR is on landslides, deformed ground and damaged structures caused by the Niigata Ken Chuetsu Earthquake. The specific purposes have been to (1) assess the ability of the technology to image ground and structural deformations; (2) develop and archive example three-dimensional imagery of surface deformation caused by landsliding and liquefaction; and (3) establish an ultra-high precision baseline survey for future change detection.

LIDAR technology is a natural extension of laser range finder systems or electronic distance meters (EDMs) used commonly in survey applications.  With this technology, a laser beam scans up and down and back and forth to acquire the precise distance to objects across the scene.  The laser repeatedly shoots out a pulse of light at each rotation point of the scanner.  The pulse hits the object and scatters a portion of the light back.  By timing the round trip of each laser pulse, the range is determined for each scan position and a series of spherical coordinates are recorded.  Knowing the position and orientation of the instrument, a group of x,y,z coordinates (referred to as a “point cloud”) is acquired at a very rapid rate.  As the scanning laser shots bounce off objects at various distances from the scanner, point measurements are collected that define the object’s shape.  In addition, the two systems used in the study were also able to record the natural color of the objects as well as the intensity of returned laser light from each shot. 


Two tripod-mounted LIDAR systems were deployed by two reconnaissance teams.  The first was a USGS-owned system used to collect data between October 30 and November 2, 2004. The second was a system owned by Utah State University (USU) used to collect data between November 17 and November 19, 2004, approximately 2 weeks later.

The USGS system and USU systems are based on the same near-infrared LIDAR transceiver manufactured by Riegl (http://www.rieglusa.com/).  The systems are portable and are designed for the rapid acquisition of high-resolution three-dimensional imagery under outdoor conditions. The maximum distance to targets the laser can sense is up to 700m under the best atmospheric conditions and is also dependent on the reflectivity of the given target.  The minimum target distance is 2 m.  The range accuracy is consistently about 2.5 cm at most ranges.  The laser beam divergence angle is 3 milliradians, meaning that at a range of 10 m, the beam footprint is approximately 3 cm across. At a range of 100 meters, the beam footprint expands to 30 cm across.  Because of the footprint size, the shots are ideally spaced 3 milliradians apart.  The position of the center of the footprint is measured to a precision of 0.17 milliradians by the encoder. The angular position of the laser-pulse leaving the scanner is controlled by precise servo motors within the units.

The USGS and USU systems employ different scanning methods.  The USGS system uses a cylindrical Riegl z210i scanner (Figure 2). With this system, a single scan can sweep up to 336° horizontally and up to 80° vertically.  The scanner makes millions of individual x, y, z position measurements, at a rate of 8,000 points/second.  The time required for scanning the highest density of points per setup (5.6 million targeted points) is 11 minutes.  Point measurements at a coarser density (in the hundreds of thousands of target points) take less than 1 minute.  The USU system uses a pan and tilt scanner head that is somewhat slower than the USGS system (Figure 3).  Scan times are approximately 8 times slower than with the USGS system with some scans taking over an hour to complete.

Both LIDAR systems have an ability to collect color information in addition to the 3D information. The USU system includes a custom developed Texel Camera™ that collects 100 pixels of high-resolution color data within the area of each laser footprint.  This provides an ability to automatically drape a detailed color “texture” onto a three-dimensional (3-D) surface model. In comparison, the USGS records a single pixel of color for each laser footprint and in essence colors each point in the point cloud. The result is a lower resolution color texture than with the USU system.  In both systems, the display of color is virtually instantaneous and requires no hand editing.

Several useful applications of the color and laser intensity channels include the extraction of non-topographic textural information about the target; identification of color-based lithologic changes in the target; and enhancement and identification of geo-referencing reflectors that send back the strongest reflected signals at a given distance and enable convenient merging of multiple data sets (Kayen et al., 2004).

To image a surface, the scanner is transported to the site in a travel-bag or backpack.  The USGS unit weights 13 kg, the USU unit weights 9.5 kg, and the accessory cables, tripod, battery and laptop can easily double the weight of each system. The scanners are placed on a tripod in front of the object of interest and connected to a battery and laptop computer for data storage and visualization. Typically, the scanner is set up upright with the unit rotating horizontally, though to image objects overhead or below, the USGS scanner can be mounted sideways.  For example, the scan of the Juestsu railway tunnel-portal failure at Kita-Horinouchi (discussed later in Figure 6) included sideways scans of the tunnel roof. 

Photo of LIDAR scanner at road embankment failure on Route 252.

Figure 2. The USGS LIDAR unit scanning a road embankment failure on Route 252, west of Horinouchi-Cho.  The system can be easily transported by vehicle or backpack to study sites, and travels as checked baggage.

Figure 3. The USU LIDAR unit scanning a landslide headscarp near Yamakoshi Village.  Note the color Texel Camera mounted on the top of the unit.

The 3-D laser scanners cannot see behind objects, and so the first surface encountered casts a shadow over objects behind it.  For example, in a scan of the boulder-field of the White Rock Landslide at Ojiya (Figure 4), the near-field objects cast shadows over the debris located behind them.  As the grazing-angle of the laser point decreases, proportionally larger shadows are cast on the ground behind the target.  To minimize shadow zones and get full coverage of the target surface, the scanner is moved to other locations surrounding the target zone. Multiple setups limit the number of shadow zones while also increasing the resolution of the target shape and the outermost boundaries of the scanned area.

Figure 4. The landslide at White Rock, Ojiya swept portions of the highway, bluff, and vehicles into the Shinano River. Here, on the south side of the slide, the landslide buried the highway.

A typical scan data set consists of many millions of data points.  Efficient manipulation of that data is performed on computers with the highest currently available processing speed, maximized dynamic RAM memory, and a video card with a 128MB or 256MB memory buffer.  Also, the manipulation of so many points requires specialized surface modeling software.  Most laser manufacturers either distribute or suggest a specialized software program that is coupled with the laser.  The USGS system utilizes a surface modeling software package called I-SiTE Studio (I-Site Pty. Ltd., 2004) and the USU system uses custom USU LIDAR control software and LDViewer™ and LD Modeler™ software by Rappid Mapper Inc. (RMI, 2004).  The software for the two systems collects both the scan point-cloud data and can process multiple scans into geo-referenced surfaces.  After data are acquired, a series of standard processing steps is followed to produce a surface model.  First, the multiple scans are either locally or absolutely georeferenced to one-another.  A least squares “best-fit” match is made between scans, and can be augmented by precise survey measurements made with a total station or differential global positioning satellite (e.g., real time kinematic RTK-GPS, or Omnistar HP-differential GPS).  Filters are then used to eliminate unwanted data.  For example, filters can be used to remove vegetation so as to observe the bare earth. The filtered point-data can then be “segmented” to differentiate discrete surfaces from each other and from complex objects like trees and brush.  Surfaces can be used as working digital terrain models (DTMs) representing ground topography or as surface patches representing parts of various infrastructure objects. Again, different surface modeling schemes can be used to render surfaces from multiple scans.  Multiple processed surfaces can be fused into a new composite surface model.  The surface model can be used to document the condition of the ground and provide a baseline for change detection of volumes, areas, and distances.

The data from the USU LIDAR system includes detailed color information from a custom color Texel Camera that adds a unique capability to the system.  This camera synchronizes to laser shots as the scan progresses and collects approximately 100 pixels of color data for each shot. The color data allows for an accurate definition of object details smaller than the laser beam footprint.  Figure 5 shows a color 3D image of a rockslide side-scarp near Ojiya with a zoomed in view of the image to compare the quality of the texel image and the colored LIDAR shots.  These colored LIDAR shots have been generated by software to simulate the performance of the USGS-owned LIDAR.   Note that the USU LIDAR provides details of the boundary between the upper colluvial versus the sheared bedrock face below.  In comparison, this boundary is less detailed in the colored LIDAR point cloud, and some level of color interpolation is needed to produce a continuous image.

Figure 5. Color 3D image of a rockslide near Ojiya with a zoomed in view of the side-scarp.  The two thumbnail images illustrate the enhanced quality of the texel image when compared to colored lidar point cloud.

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Reconnaissance Team 1

During the week of November 1, 2004, the first team visited sites of damaged roadways, structures, and displaced ground and collected data from approximately thirty individual laser-scan setups with the USGS-owned LIDAR.  The Niigata Chuetsu earthquake was particularly damaging to the roadways and hill-slopes in the mountain country northeast of the Shinano and Uono rivers.  Roadside embankment failures were ubiquitous throughout the mountainous areas.  An example of one such embankment failure on a steep mountainous section of Highway 252, west of Horinouchi Town is presented in Figures 2 and 6.  

Here, a two-way, two-lane road, built on the northern side of a steeply sloped river-incised canyon, failed toward the south into the bottom of a narrow ravine.  Two LIDAR scans were set up on the east and west ends of the failure to minimize the shadow zones.  The four frames of Figure 6 show the processing procedures for producing a surface model: (A) scan the target from multiple perspectives; (B) merge and register the scans; (C) filter out vegetation and render the surface model; and (D) visualize by overlaying the color scan points on the rendered surface, and analyzing the deformations..  With this product, we can make a variety of geometric measurements of the ground failure and have a data set that can be differenced against pre-event topographic survey drawings of the embankment.

An example of structural damage recorded by the LIDAR unit is presented in Figure 7, collected in the damaged portal of the Juetsu railroad tunnel, north of the town of Horinouchi (the north direction is into the tunnel).  The portal is founded on a poorly compacted embankment fill that settled during the earthquake.  The portal pulled away from the tunnel, opening a gap, and settled laterally toward the east (downslope).  Vertical settlement was more pronounced on one side of the portal causing it to undergo a minor rotation.  A photograph of the embankment and tunnel damage is presented in Figure 7A, looking north.  In the interior of the tunnel, displacement of the portal was observed in the walls and ceiling.  An oblique view of the LIDAR point-cloud data can be seen in Figure 7B.  Here, the portal (front section) and tunnel are viewed from above and south of the portal entrance.  The left-lateral offset of the portal relative to the tunnel is clearly visible in the LIDAR model.  In the LIDAR imagery, precise centimeter-scale measurements can be made of the three-dimensional deformation of the structure.  At this tunnel, 36 cm of separation of the portal and 21 cm of left-lateral displacement was measured in the LIDAR imagery.  Displacement of the portal and failure of the gravel embankment also resulted in deformation of the railway tracks, clearly visible in the LIDAR scans.

Figure 6.  Processing procedures for ground-LIDAR technology: (A) scan target; (B) plan view with two merged scans; (C) oblique view of rendered solid surface model; (D) close-up view used to measure specific landslide blocks and deformations. The direction arrows are the local orientation of the scanner and not the compass orientation.

3-D imaging software was used to rotate the LIDAR imagery into map view looking down from above through the roof of the portal (Figure 8).  In this view, the portal separation (35 cm) and left-lateral displacement (21 cm) from the tunnel (right) can be seen at the top and bottom of the tunnel wall.  The data-hole in the center is the non-illuminated area beneath the tripod. 

A final example of a natural slope failure is the White Rock landslide, which dislodged rock debris along a steep un-buttressed cliff corner on the banks of the Shinano River, along Route 17 at Myoken, Ojiya-Shi.  This large rockslide dislodged an entire cliff-face of soft and friable weathered mud stone with laminated sand.  It killed several people driving on this portion of Route 17.  The rock slope, a portion of the highway cut-and-fill, and five vehicles were swept into the Shinano River in a catastrophic collapse of the bluff.  Eight LIDAR scans were taken on the north, western and southern sides of the slide to characterize the volume, runout and morphology of the slide.  The roadbed on the north side of the slide collapsed into the river. On the south side of the landslide, block debris completely covered the roadbed just north of the highway bridge (Figure 4). The height of the cliff at Shirowa is approximately 35 meters above the Shinano River and the run out distance averages 130 meters. The road bed at the southern end of Route 17 just north of the bridge was completely covered with debris.  That portion of the road bed was 13 meters above the Shinano River toward the base of the slope, well down slope from the crest of the cliff that released the rock avalanche.  Thus the impact of the falling rock on the road must have been devastating.  A number of the largest intact boulders in the avalanche are in excess of 6 meters to a side.

Figure 7. Photograph (A) and LIDAR image (B) of damage to the Juetsu Railroad portal at Kita-Horinouchi, looking north.  Lateral displacement of the portal relative to the tunnel wall and distress of the rail track is precisely recorded in the LIDAR scan (B).

Figure 8. Map view perspective of the LIDAR image of damage to the Juetsu Railroad portal at Kita-Horinouchi. Portal displacement is 21 cm lateral-toward the left.  The direction arrows are the local orientations of the vertical and horizontal planes about the scanner and are not absolute directions.

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Reconnaissance Team 2

Between November 18 and November 20, 2004, the second team focused on documenting landslide morphologies with the USU-owned LIDAR.  The Niigata Chuetsu earthquake precipitated a variety of landslide types in a variety of hillslope settings reconnaissance.  The largest density of landslides occurred to the east and south of the town of Yamakoshi.  Team members were accompanied by Prof. I. Towhata of the University of Tokyo, Profs. Y. Yamakawa, N. Inukai, and H. Toyota of Nagaoka University, and several students from the University of Tokyo and Tokyo Denki University.

A LIDAR survey was taken of a debris slide / debris flow complex near Nagaoka in a canyon to the north of the Yamakoshi Village area. This landslide was chosen as it was considered typical of many debris flows generated in the region. The survey was principally conducted from an instrument setup on the debris deposit and looking up. Figure 9 shows a texel image acquired by the LIDAR from a tripod setup within the debris fan.  Superimposed on the image is the location of the profile extracted from the LIDAR data and shown as Figure 10.  The field investigation determined that the slide started within a road fill, then through progressive failure, mobilized into a debris flow.  The data indicate that the slide has a relief of approximately 100 meters and an average transport gradient of 26°.

Figure 9. Texel image of a debris slide/flow acquired by the USU LIDAR from a tripod setup within the debris deposit area.  The profile line shown corresponds with the profile shown as Figure 10.

Figure 10.  Profile of a debris slide/flow extracted from the LIDAR data set.

An earth slump located east of Yamakoshi (Figure 11) was imaged with LIDAR, as it illustrates earthquake-generated slumping seen in many areas proximal to the epicenter.  The survey was conducted from two locations on a hillside opposite the slump, took approximately three hours, and was conducted in steady rain.

Figure 11 is a LIDAR-generated texel image of the center portion of the slump showing the headscarp, a demolished house, a deformed retaining wall and a small pond formed at the base of the slope due to the damming of the creek.  The demolished house was once at the elevation of the blue house hanging at the top of the headscarp.  The profile line plots the location of the profile given in Figure 12.  The profile indicates that the slump head scarp is approximately 8 meters high, the slump has a total relief is 42 meters and has an average slope is 16.5°.  The 3D LIDAR data, once collected, can be viewed from any chosen perspective in a 3D computer graphics environment.  Figure 13 shows a plan view of the earth slump showing the limits as determined by the LIDAR survey as if one were viewing it from the air.  Because the data was collected at ground level, many parts of the slide surface were hidden from view and hence could not be sensed by the LIDAR.  The black areas shown in Figure 13 are areas that could not be seen from the two tripod setups. Survey data of the front faces of buildings show up as lines when projected in a birds-eye view. The geometry of the retaining wall can also be seen. The extent of the slump is measured to be approximately 170 meters wide, 140 meters from head to toe, with a total relief of 42 meters.

Figure 11.  Image acquired by the imager onboard the LIDAR system. Superimposed on the image is the location of the profile plotted below.

Figure 12. Profile of slump extracted from the LIDAR data showing a total relief of approximately 42 meters and an average slope of approximately 16.5°.

Figure 13. Map view of earth slump showing the approximate limits of major movement as determined by the LIDAR survey.

A LIDAR survey was conducted of one of two translational rock slides, east of Ojiya along Highway 291, close to the location where the highway exits the mountains at the Shinano River.  This area is also just south of the White Rock landslide surveyed by the first reconnaissance team and shown in Figure 4.  Figure 14 is an aerial photograph of the two rockslides.  Both occurred along bedding planes on dip slopes dipping about 25° to the west.  The LIDAR survey was conducted on the left-most rockslide as shown in Figure 14. The shear surface of this particular rockslide is remarkably planar and occurred along sandstone bedding planes with a dip-slope orientation. It is this dip-slope geometry along Highway 291 that likely led to the multiple failures in the area and subsequent burial of this section of the highway.

Figure 15 shows a contour map of the rockslide.  This map was derived from the LIDAR survey by processing the raw data into a digital elevation model using ESRI ArcGIS mapping software.  The stripes of color represent elevation bands sliced into 1 meter intervals.  Note the location of the cross-section and profile that are plotted below.  The LIDAR survey was conducted from one strategically positioned tripod setup marked in Figure 15.  The cross-section line shown in the figure corresponds with the cross-section plot shown in Figure 16.  This plot shows the asymmetry of the side scarp due to the orientation of the rock bedding plane relative to the ground surface.  This can also be seen visually in Figure 5.  The scarp is approximately 5 meters high on the right (south) flank of the slide and has virtually no relief on the left (north) flank of the slide.  The profile line shown in Figure 15 is plotted in Figure 17.  This profile shows the planarity of the slide surface that failure along a bedding layer in the downslope direction.  Remarkably, the trees found on the lower part of this profile were “rafted” in an upright position down the shear plane riding on a translating block of rock.  The rockslide is approximately 70 meters long and 40 meters wide. The uniform slope is 24.6°.

Between the time the aerial photo (Figure 14) was taken and the time of the LIDAR survey, a substantial amount of debris had been excavated from, and adjacent to, the highway.  Thus this evidence of slide volume has been lost.  This underlines the need for rapid response documentation of earthquake damage before repairs remove critical evidence.

Fig. 14.  Aerial photograph of two rockslides in east Ojiya (37.3294N, 138.8259E).  (Aerial photo courtesy of ORIS http://www.oris.co.jp/jishin2004/h16jisin_top.htm).

Fig. 15.  Contour map of the rockslide derived from the LIDAR survey. These data were all collected from one tripod setup as shown.

Figure 16. Rockslide profile showing linear shear surface. Note the location of trees and rock debris carried to the foot of the slope adjacent to the highway.

Figure 17. Rockslide cross-section showing the asymmetry of the rockslide depth. The original ground surface is assumed to be a linear projection of the adjacent ground surface.


In this study, two reconnaissance groups used ground-based tripod-mounted LIDAR systems to map the complex topography of geotechnical and structural failures that occurred during the Niigata Ken Chuetsu earthquake. There are several benefits in acquiring these LIDAR data in the initial reconnaissance effort after the earthquake.  First, the detailed failure morphologies of damaged ground and structures allow engineers to make measurements that are either impractical or impossible by conventional survey means.  LIDAR systems can successfully resolve and map surface features that are a few centimeters in size, and precisely characterize the highly complex surfaces of areas more than 300 meters across.  Second, LIDAR surveys can collect a huge amount of data in a short time.  This is important in rapid-response situations where clean-up and repair operations quickly destroy critical evidence.  Third, digital terrain models (DTM’s) allow the enlargement, enhancement, and rotation of data in order to visualize damage in orientations and scales not previously possible.  This ability to visualize damage allows one to better understand failure modes.  Furthermore, cross-sections and profiles can be extracted from the DTM’s to further explore and understand the geometry.  Where brush and trees interfere, last signal arrival data collection and post processing filtering techniques allow for the removal of vegetation, so that deformations on the bare earth can be assessed.  Finally, LIDAR allows one to archive 3-D terrain models of damaged ground and structures so that the engineering community can evaluate analytical and numerical models of deformation potential against detailed field measurements.  LIDAR has proven to be a useful addition to the various tools engineers and scientists bring to earthquake reconnaissance, and will likely be a standard component of future response efforts.

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Bawden, G.W., Kayen, R., Silver, M.H., Brandt, J.T., Collins, B.D. (2004) Evaluating Tripod Lidar as an earthquake response tool, Eos Trans. AGU, 85(47), Fall Meet. Suppl., Abstract S51C-0170R.

Collins, B.D. and Sitar, N. (2004) Application of High Resolution 3D Laser Scanning to Slope Stability Studies, 39th Symposium on Engineering Geology and Geotechnical Engineering, Butte, Montana, 14 p.

Kayen, R., Barnhardt, W., Carkin, B., Collins, B.D., Grossman, E.E., Minasian, D., Thompson, E. (2004) Imaging the M7.9 Denali Fault Earthquake 2002 rupture at the Delta River using LIDAR, RADAR, and SASW Surface Wave Geophysics, Eos Trans. AGU,85(47), Fall Meet. Suppl., Abstract S11A-0999.

Niemi, T.M., Kayen, R., Zhang, H.,Dunn, C.R., Doolin, D.M. (2004) LiDAR Imagery of the San Andreas Fault Zone at the Vedanta and Olema Ridge Paleoseismic Trench Sites, Pt. Reyes, CA, Eos Trans. AGU, 85(47), Fall Meet. Suppl., Abstract G13B-0811.

Paar, G., B. Nauschnegg, and A Ullrich. 2000. Laser scanning monitoring -- technical concepts, possibilities and limits. Natural Hazards Workshop, Igls Austria, 5-7 June.

Pack, R.T. 2002. Engineering geologic mapping using 3D imaging technology.  37th Annual Symposium on Engineering Geology and Geotechnical Engineering, Boise, Idaho.

RMI, 2004. LDViewer™ and LDModeler™ LIDAR Software. Rappid Mapper Inc., Salt Lake City, UT. www.rappidmapper.com.

Rowens, K.A., L.D. Jones & M. Whitworth. 2003. Landslide laser scanning: a new look at an old problem. Quarterly Journal of Engineering Geology and Hydrogeology, v.36, p.155-157.

Scheikl, M., G. Poscher, and H. Grafinger. 2000. Application of the new Automatic Laser Remote Monitoring System (ALARM) for the continuous observation of the mass movement at the Eiblschrofen rockfall area, Tyrol, Austria. Natural Hazards Workshop, Igls Austria, 5-7 June.



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