PyTrx : a python-based monoscopic terrestrial photogrammetry toolset for glaciology

This work was affiliated with the CRIOS project (Calving Rates and Impact On Sea Level), which was supported by the Conoco Phillips-Lundin Northern Area Program. PH was funded by a NERC Ph.D. studentship (reference number 1396698). Terrestrial time-lapse photogrammetry is a rapidly growing method fo...

Full description

Bibliographic Details
Published in:Frontiers in Earth Science
Main Authors: How, Penelope, Hulton, Nicholas R. J., Buie, Lynne, Benn, Douglas I.
Other Authors: University of St Andrews.School of Geography & Sustainable Development, University of St Andrews.Bell-Edwards Geographic Data Institute
Format: Article in Journal/Newspaper
Language:English
Published: 2020
Subjects:
Glacier dynamics
Photogrammetry
Python
Tidewater glaciers
Time-lapse
G Geography (General)
DAS
G1
glacier
Svalbard
Tidewater
Online Access:https://hdl.handle.net/10023/19499
https://doi.org/10.3389/feart.2020.00021
Description
Summary:This work was affiliated with the CRIOS project (Calving Rates and Impact On Sea Level), which was supported by the Conoco Phillips-Lundin Northern Area Program. PH was funded by a NERC Ph.D. studentship (reference number 1396698). Terrestrial time-lapse photogrammetry is a rapidly growing method for deriving measurements from glacial environments because it provides high spatio-temporal resolution records of change. Currently, however, the potential usefulness of time-lapse data is limited by the unavailability of user-friendly photogrammetry toolsets. Such data are used primarily to calculate ice flow velocities or to serve as qualitative records. PyTrx (available at https://github.com/PennyHow/PyTrx) is presented here as a Python-alternative toolset to widen the range of monoscopic photogrammetry (i.e., from a single viewpoint) toolsets on offer to the glaciology community. The toolset holds core photogrammetric functions for template generation, feature-tracking, camea calibration and optimization, image registration, and georectification (using a planar projective transformation model). In addition, PyTrx facilitates areal and line measurements, which can be detected from imagery using either an automated or manual approach. Examples of PyTrx's applications are demonstrated using time-lapse imagery from Kronebreen and Tunabreen, two tidewater glaciers in Svalbard. Products from these applications include ice flow velocities, surface areas of supraglacial lakes and meltwater plumes, and glacier terminus profiles. Peer reviewed

Similar Items