Workflow for crevasse detection with OIB/ATM laser altimeter data.
This workflow implements the crevasse detection with OIB/ATM Laser Altimeter Data algorithm, in parallel over a range of years and user specified operational parameters.
The procedure follows the analysis used to detect the morphology of sea ice features (e.g. pressure ridges) across the Arctic using OIB data (Petty et al., 2016) but applied to crevasses over specific glaciers in Greenland. Some important differences that need to be noted:
The crevasse detection algorithm requires NASA Airborne Topographic Mapper (ATM) laser altimetry data collected by NASA’s Operation IceBridge, downloaded from https://nsidc.org/data/ILATM1B/versions/2 , and, corresponding POS AV smoothed best estimate of trajectory (SBET) files downloaded from https://daacdata.apps.nsidc.org/pub/DATASETS/ICEBRIDGE/IPAPP1B_GPSInsCorrected_v01 .
Petty, A. A., M. C. Tsamados, N. T. Kurtz, S. L. Farrell, T. Newman, J. P. Harbeck, D. L. Feltham, and J. A. Richter-Menge (2016), Characterizing Arctic sea ice topography using high-resolution IceBridge data, The Cryosphere, 10(3), 1161–1179, doi:10.5194/tc-10-1161-2016.
Studinger, M. 2013, updated 2020. IceBridge ATM L1B Elevation and Return Strength, Version 2 . [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/19SIM5TXKPGT .
Poinar, K., Jones-Ivey, R., Sperhac, J., & Petty, A. (2025). ATM-Based Crevasse Detection & Extraction workflow. Zenodo. https://doi.org/10.5281/zenodo.14873214
Researchers should cite this resource as follows:
Poinar, K., Jones-Ivey, R., Sperhac, J., & Petty, A. (2025). ATM-Based Crevasse Detection & Extraction workflow. Zenodo. https://doi.org/10.5281/zenodo.14873214.