Crevasse Webinar Series

In June 2021, NSF sponsored the Crevasse Risk Management and Safety Workshop. In continuing the conversations of this workshop, the NSF Office of Polar Programs is hosting a four-part webinar series on using technology to increase the ability to detect crevasses, an essential need while doing fieldwork in rapidly changing, ice-laden landscapes.

There is no need to register in advance. Simply follow the link listed below. 

2021 Crevasse Risk Management and Safety Workshop Review
2023 Crevasse Webinar Series Review

Using Modeling to Predict Where Crevasses Form 

• Monday, March 27th at 10:30-11:30 am Eastern
• Panelists:
  • Ching-Yao Lai, Princeton University
  • Ellyn Enderlin, Boise State University
  • Tim Bartholomaus, University of Idaho
• Recording: Using Modeling to Predict Where Crevasses Form
  • Passcode: rMKJy0!9

Using Drones, Automated Radar Collection and Other Techniques in the Field to Detect Crevasses

• Tuesday, March 28th at 3-4 pm Eastern
• Panelists:
  • Austin Lines, Cold Regions Research and Engineering Laboratory
  • Laurent Mingo, Blue System Integration Ltd.
  • Seth Campbell, University of Maine
• Recording: Using Drones, Automated Radar Collection and Other Techniques in the Field to Detect Crevasses
  • Passcode: #++$E9?B

Using Satellite Imagery and Remote Sensing for Crevasse Detection

• Tuesday, April 4th at 1-2 pm Eastern
• Panelists:
  • Leigh Stearns, University of Kansas
  • Eli Deeb, Cold Regions Research and Engineering Laboratory
  • Oliver Marsh, British Antarctic Survey
  • Dan Price, University of Canterbury
• Recording: Using Satellite Imagery and Remote Sensing for Crevasse Detection
  • Passcode: xR*FtO46

Automated Detection of Crevasses from Remote Sensing

• Thursday, April 6th at 4-5 pm Eastern
• Panelists:
  • Matt Siegfried, Colorado School of Mines
  • Joanna Millstein, Massachusetts Institute of Technology / Woods Hole Oceanographic Institution
  • Shane Grigsby, Colorado School of Mines
  • Gabe Lewis, University of Nevada, Reno
  • Ching-Yao Lai, Princeton University
• Recording: Automated Detection of Crevasses from Remote Sensing
  • Passcode: PT0zqX4!