Airborne lidar (light detection and ranging) topography, acquired before and after an earthquake, can provide an estimate of the coseismic surface displacement field by differencing the preevent and postevent lidar point clouds. However, estimated displacements can be contaminated by the presence of large systematic errors in either of the point clouds. We present three-dimensional displacements obtained by differencing airborne lidar point clouds collected before and after the El Mayor-Cucapah earthquake, a M w 7.2 earthquake that occurred in 2010. The original surface displacement estimates contained large, periodic artifacts caused by systematic errors in the preevent lidar data. Reprocessing the preevent data, detailed herein, removed a majority of these systematic errors that were largely due to misalignment between the scanning mirror and the outgoing laser beam. The methodology presented can be applied to other legacy airborne laser scanning data sets in order to improve change estimates from temporally spaced lidar acquisitions.

ABSTRACT:We report on a calibration and stability analysis of the Velodyne VLP-16 LiDAR scanner. The sensor is evaluated for long-term stability, geometric calibration and the effect of temperature variations. To generalize the results, three separate VLP-16 sensors were examined. The results and conclusions from the analysis of each of the individual sensors was similar. We found that the VLP-16 showed a consistent level of performance, in terms of range bias and noise level over the tested temperature range from 0-40 °C. A geometric calibration was able to marginally improve the accuracy of the VLP-16 point cloud (by approximately 20%) for a single collection, however the temporal stability of the geometric calibration negated this accuracy improvement. Overall, it was found that there is some long-term walk in the ranging observations from individual lasers within the VLP-16, which likely causes the instability in the determination of geometric calibration parameters. However, despite this range walk, the point cloud delivered from the VLP-16 sensors tested showed an accuracy level within the manufacturer specifications of 3 cm RMSE, with an overall estimated RMSE of range residuals between 22 mm and 27 mm.

Differential light detection and ranging (LiDAR) from repeated surveys has recently emerged as an effective tool to measure the three-dimensional (3-D) change. Currently, the primary method for determining 3-D change from LiDAR is through the use of the iterative closest point (ICP) algorithm and its variants, with a simplistic assumption of a uniform accuracy for the entire LiDAR point cloud. This common practice ignores the localization anisotropy and results in local convergence and spurious error estimation. To rigorously determine spatial change, this paper introduces an anisotropic-weighted ICP (A-ICP) algorithm, and proposes to model the random error for every LiDAR observation in the differential point cloud, and use this as a priori weights in the ICP algorithm. The implementation is evaluated by qualitatively and quantitatively comparing the estimation performance on point clouds with synthetic fault ruptures between standard ICP and A-ICP algorithm. As a further enhancement, we also present a moving window technique to improve A-ICP. Practical application of the combined moving window A-ICP technique is evaluated by estimating post-earthquake slip for the 2010 El Mayor-Cucapah Earthquake (EMC) using pre-and postevent LiDAR. Based on the analysis, moving window A-ICP is able to better estimate the synthetic surface ruptures, and provides a smoother estimate of actual displacement for the EMC earthquake.Index Terms-Change detection, earthquake, iterative closest point (ICP), light detection and ranging (LiDAR), moving window

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