Numerical and Experimental Evaluation of Terrestrial LiDAR for Parameterizing Centimeter-Scale Sea Ice Surface Roughness

• Published in: IEEE Transactions on Geoscience and Remote Sensing
• Main Authors: Landy, Jack C., Komarov, Alexander S., Barber, David G.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2015
• Subjects: Geophysical measurements; laser applications; laser measurementapplications; modeling; numerical analysis; radar scattering; rough surfaces; sea ice; surfaces; Sea ice
• Online Access: https://hdl.handle.net/1983/b26a02b5-e7c6-4c32-9762-18facb631343; https://research-information.bris.ac.uk/en/publications/b26a02b5-e7c6-4c32-9762-18facb631343; https://doi.org/10.1109/TGRS.2015.2412034; http://www.scopus.com/inward/record.url?scp=85027923397&partnerID=8YFLogxK

Terrestrial light detection and ranging (LiDAR) offers significant advantages over conventional techniques for measuring the centimeter-scale surface roughness of natural surfaces, such as sea ice. However, the laser scanning technique is inherently limited, principally by the following: 1) the high inclination scanning angle of the sensor with respect to nadir; 2) the precision of the laser ranging estimate; and 3) the beam divergence of the laser. In this paper, we introduce a numerical model that has been designed to simulate the acquisition of LiDAR data over a regular rough surface. Results from the model compare well (r 2 = 0.97) with LiDAR observations collected over two experimental surfaces of known roughness that were constructed from medium-density fibreboard using a computer numerical control three-axis router. The model demonstrates that surface roughness parameters are not sensitive to minor variations in the LiDAR sensor's range and laser beam divergence, but are slightly sensitive to the precision of the ranging estimate. The model also demonstrates that surface roughness parameters are particularly sensitive to the inclination angle of the LiDAR sensor. The surface RMS height is underestimated, and the correlation length is overestimated as either the inclination angle of the sensor or the true roughness of the surface increases. An isotropic surface is also increasingly observed as an anisotropic surface as either the inclination angle or the true surface roughness increases. Based on the model results, we propose a set of calibration functions that can be used to correct in situ LiDAR measurements of surface roughness.