Detection and Characterization of Ice Surface Elevation Profiles from Micropulse Photon-Counting Lidar Data

• Main Author: Medley, Gavin Christopher
• Format: Text
• Language: unknown
• Published: CU Scholar 2016
• Subjects: altimetry; glacier; greenland; icesat; lidar; simpl; Applied Mathematics; Geographic Information Sciences; Remote Sensing; Greenland
• Online Access: https://scholar.colorado.edu/appm_gradetds/100; https://scholar.colorado.edu/cgi/viewcontent.cgi?article=1107&context=appm_gradetds

Quantitative analysis of the cryosphere relies on accurate and very precise data describing the ice surfaces of the earth. These data can be used to study surfaces of the Earth to better understand the dynamics that govern our planet. As technology improves, these data become more readily available but with more data, the analysis inevitably becomes more complex. We present an analysis and application of data collected by NASA’s Slope Imaging Multi-Polarization Photon Counting LiDAR (SIMPL) instrument using a density dimension algorithm (DDA) in conjunction with geostatistical classification methods. SIMPL is an airborne predecessor instrument of the next-generation multi-beam micropulse photon-counting LiDAR instrument – the Advanced Topographic Laser Altimeter System (ATLAS) – that will be used during NASA’s Ice Cloud and Land Elevation Satellite (ICESat)-2 mission. SIMPL uses four spatially distinct beams at two wavelength and in two polarization modes. High-resolution elevation profiles of ice surfaces in western Greenland are constructed from SIMPL photon return data obtained during preliminary test flights conducted in August, 2015. Using a density dimension algorithm, noise is filtered out and the photon returns from the true ice surface are retained. The retained points are then weighted to estimate the surface elevation profile along the flight track. We present an estimate of optimal parameters for each of the 16 channels and show the estimated surface from each. Further, we make observations about data and discuss its characteristics in different conditions. Finally, to demonstrate the potential usefulness of the SIMPL data, we perform a surface classification study using geostatistical methods.