G1A-0796
Optimization of Terrestrial Laser Scanning Survey Design for Dynamic Terrain Monitoring M. J. Starek , H. Mitasova , R.S. Harmon 1: Marine, Earth, and Atmospheric Sciences Department North Carolina State University, Raleigh, NC 2: US Army Research Office E-mail:
[email protected] 1,2
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I. Abstract
IV. Multiple Scan Location Optimization
Terrestrial laser scanning (lidar) technology offers great potential as a rapid mapping technology for monitoring dynamic terrain evolution at localized scales but there are inherent limitations. Because such systems have limited scanning ranges and are static, multiple scans must be merged together to form a seamless model of the terrain scene. For contiguous mapping of terrain over wide-areas (> few hundred meters) this poses several obstacles that must be overcome. The selected measurement set-up, sampling resolution, and other survey design factors as well as inherent system characteristics will influence the measurement capabilities and efficiency of repeat-coverage surveys for monitoring terrain change. Additionally, relative to airborne lidar, developments in the utilization of terrestrial lidar for terrain mapping have lagged behind. In an effort to develop more effective methods for terrain monitoring with terrestrial lidar, this research investigates the influence of scan configuration on surface change detection capability with the goal of optimizing data acquisition while minimizing information loss. Results are based on terrestrial lidar surveys conducted at an experimental watershed maintained by North Carolina State University. First, an optimization approach for measurement setup is developed using multiple viewshed analysis and simulated annealing constrained by the system performance characteristics and survey specifications. The resultant method provides a powerful tool for characterizing data acquisition capabilities for a given laser scanner and terrain scene enabling more efficient survey design. Then, examples of repeat-coverage data collected at the watershed for detecting subtle terrain change are shown, and finally, an example of surface flow modeling using high-resolution DEMs generated from the data is demonstrated.
Problem Statement: for a given scanner configuration (range, FOV, height) and digital terrain model of survey area, identify m scan positions (viewpoints) which maximize the percentage of n pixels that are visible in the model.
II. Experimental Watershed
VI. Cumulative Viewsheds DEM of survey area used to compute viewsheds
where m = # of scan points, n = # of pixels in the grid. Combinatorial Optimization the multiple viewpoint problem runs on the order of ~O(N^m) for N>>m. Quickly becomes computationally intractable. Example: N = 3000 candidate scan locations and m= 5 viewpoints ~2 x 10^17 Approach: Viewsheds are computed for each scan position candidate using a lidar-derived DEM of study area to determine visibility. Simulated Annealing approach is implemented to solve for “optimal” scan locations.
150 meters
Example of viewshed computed for a scan location Cumulative viewshed for m=3 scan locations selected via SA. Viewshed Parameters Scan range = 150 m Scan height = 1.6 m
Candidate scan locations sampled at 5 m spacing
Cumulative viewshed for m=4 scan locations selected via SA.
Cumulative viewshed for m=5 scan locations selected via SA.
VII. Survey Examples
Field of view = 360 deg
V. Simulated Annealing Annealing Control Parameters
Generate initial solution X at random Set initial temperature T 2 While not frozen LOOP = L times Generate random neighbor X’ of X Compute Δ = C(X) - C(X’) If Δ < 0 X = X’ ∆ Else − X = X’ with probability 𝑒 𝑇 Lower T If acceptance ratio > 0.7 T = T/2 Else T = R*T 3 Return X 1
Digital orthophoto of study area. Boundary lines indicate two subwatersheds. 1
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1 m digital elevation model (DEM) of surface generated from airborne lidar data used to perform viewshed analysis.
Digital image draped over 1 m DEM. Lower right is main drainage zone (star), and shaded box is survey area of focus. 3
III. Terrestrial Laser Scanner
Leica ScanStation 2
Point cloud generated from two co-registered scans.
Scanner Specifications Class 3R Laser Green Beam Divergence 0.15 mrad Pulse Rate (pulses/sec)
