## 2 Methodology
### Processing  
Geo-referencing is a critical step to relate data from different dates, particularly in areas with active tectonic movement. Because each scan is normally recorded in its own coordinate system, one scan survey is not relatable to another unless it is transformed into a common coordinate system (Alaska State Plane Coordinate System Zone 4, North American Datum 1983 (2011) Epoch 2010.00, Geoid 12A). This process is generally accomplished through a least squares, rigid-body coordinate transformation, determining both rotations and translations along orthogonal axes, which results in the minimization of the sum of the square errors between point pairs (either from pre-marked targets or matching points in the point cloud). For the static scans collected in the 2012 survey, reflective targets were placed throughout the scan scene, which were linked with a total station to ground control points established with RTK GPS. The coordinates of the base station were established using the static processing through the National Geodetic Survey Online Positioning User Service, OPUS [41]. The mobile lidar data was processed using proprietary software with direct geo-referencing from the GPS, Inertial Navigation System (INS), and scanner sensors on the mobile lidar unit. For the 2013 and 2014 surveys, both the baseline and _in-situ_ scans were geo-referenced following the methodology presented in [27, 42] using the program _PointReg_. The methodology was developed for efficient surveying in dynamic environments (e.g., coastal areas, active landslides) where survey control can be difficult to establish and maintain. Although other geo-referencing procedures can be implemented, such as the approach implemented in the 2012 survey, the _PointReg_ approach can minimize field time, since targets are not required [43]. During the field data collection, the operator obtains most of the geo-referencing transformation parameters needed for scan alignment. Translation vectors (X, Y, Z) were obtained for the scanner origin using the co-acquired GPS data (20 min for each scan), which were processed in OPUS-RS. These coordinates were corrected both for offsets between the scanner origin and GPS antenna reference point and accounting for a slightly out of level setup, typically \(<2\)\(\circ\)-\(3\)\(\circ\). The leveling information (rotations about the X (roll) and Y (pitch) axes) is determined using internal inclination sensors in the scanner, avoiding the need to level the scanner. [PERSON] and [PERSON] [PERSON] and [PERSON] (2007) highlight the importance of these sensors and their ability to provide quality data for scan geo-referencing. The azimuthal values for scan positions are initially estimated using a digital compass and then converted into a rotation about the Z-axis (yaw). This angle is then improved using the least squares solution presented in [PERSON] _et al._[PERSON] _et al._ (2007), which calculates the optimal rotation of a scan about the Z-axis (centered at the scan origin) to best fit with nearby points in neighboring scans. The methodology utilized does not require each epoch to be registered together since the each survey is independently georeferenced into the same coordinate system. ([PERSON] _et al._, 2007; [PERSON] _et al._, 2007) have shown that the methodology provides consistent results between multi-temporal surveys. In addition, the geo-referencing was validated by examining concrete structures, roadways, guardrails, and other objects present in the scene that were observed to be stable with time. Following geo-referencing, point cloud data is typically filtered ([PERSON] _et al._, 2007) to eliminate unwanted features (e.g., passing vehicles, vegetation, reflections from wet surfaces, moisture in the air). Filtering processes vary substantially in the degree of user interaction required and the type and quantity of noise present. These data were all removed manually using Leica Cyclone and Maptek I-Site software by creating polygon windows around unwanted objects.