Skip to Main content Skip to Navigation
New interface
Journal articles

Revealing Active Mars with HiRISE Digital Terrain Models

Abstract : Many discoveries of active surface processes on Mars have been made due to the availability of repeat high-resolution images from the High Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter. HiRISE stereo images are used to make digital terrain models (DTMs) and orthorectified images (orthoimages). HiRISE DTMs and orthoimage time series have been crucial for advancing the study of active processes such as recurring slope lineae, dune migration, gully activity, and polar processes. We describe the process of making HiRISE DTMs, orthoimage time series, DTM mosaics, and the difference of DTMs, specifically using the ISIS/SOCET Set workflow. HiRISE DTMs are produced at a 1 and 2 m ground sample distance, with a corresponding estimated vertical precision of tens of cm and ∼1 m, respectively. To date, more than 6000 stereo pairs have been acquired by HiRISE and, of these, more than 800 DTMs and 2700 orthoimages have been produced and made available to the public via the Planetary Data System. The intended audiences of this paper are producers, as well as users, of HiRISE DTMs and orthoimages. We discuss the factors that determine the effective resolution, as well as the quality, precision, and accuracy of HiRISE DTMs, and provide examples of their use in time series analyses of active surface processes on Mars
Document type :
Journal articles
Complete list of metadata

https://hal-insu.archives-ouvertes.fr/insu-03813332
Contributor : Susan Conway Connect in order to contact the contributor
Submitted on : Thursday, October 13, 2022 - 11:38:11 AM
Last modification on : Thursday, October 20, 2022 - 4:02:00 AM

File

remotesensing-14-02403.pdf
Publisher files allowed on an open archive

Identifiers

Citation

Sarah S Sutton, Matthew Chojnacki, Alfred S Mcewen, Randolph L Kirk, Colin M Dundas, et al.. Revealing Active Mars with HiRISE Digital Terrain Models. Remote Sensing, 2022, 14 (10), pp.2403. ⟨10.3390/rs14102403⟩. ⟨insu-03813332⟩

Share

Metrics

Record views

0

Files downloads

0