Deriving large-scale glacier velocities from a complete satellite archive: Application to the Pamir–Karakoram–Himalaya

Abstract

Mountain glaciers are pertinent indicators of climate change and their dynamics, in particular surface velocity change, is an essential climate variable. In order to retrieve the climatic signature from surface velocity, largescale study of temporal trends spanning multiple decades is required. Satellite image feature-tracking has been successfully used to derive mountain glacier surface velocities, but most studies rely on manually selected pairs of images, which is not adequate for large datasets. In this paper, we propose a processing strategy to exploit complete satellite archives in a semi-automated way in order to derive robust and spatially complete glacier velocities and their uncertainties on a large spatial scale. In this approach, all available pairs within a defined time span are analysed, preprocessed to improve image quality and features are tracked to produce a velocity stack; the final velocity is obtained by selecting measures from the stack with the statistically higher level of confidence. This approach allows to compute statistical uncertainty level associated with each measured image pixel. This strategy is applied to 1536 pairs of Landsat 5 and 7 images covering the 3000 km long Pamir-KarakoramHimalaya range for the period of 1999-2001 to produce glacier annual velocity fields. We obtain a velocity estimate for 76,000 km 2 or 92% of the glacierized areas of this region. We then discuss the impact of coregistration errors and variability of glacier flow on the final velocity. The median 95% confidence interval ranges from 2.0 m/year on the average in stable areas and 4.4 m/year on the average over glaciers with variability related to data density, surface conditions and strain rate. These performances highlight the benefits of processing of a complete satellite archive to produce glacier velocity fields and to analyse glacier dynamics at regional scales.

Glacier change along West Antarctica's Marie Byrd Land Sector and links to inter-decadal atmosphere–ocean variability

“…To do this, we used the MEaSUREs (Making Earth System Data Records for Use in Research Environments) version 2 data set and additional ice velocity fields generated from feature-tracking of Landsat 8 imagery for the period 2013-2016 (Dehecq et al, 2015) to calculate and assign the position that each CryoSat-2 swath measurement would have had at the beginning of the CryoSat-2 operational period (July 2010). Finally, we corrected for the effects of surface mass balance and firn compaction processes throughout MBLS using the RACMO2.3p2 (5.5 km) and IMAU-FDM (5.5 km) models (Ligtenberg et al, 2011;Lenaerts et al, 2012Lenaerts et al, , 2017Gourmelen et al, 2017a).…”

Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits

“…These processes of interest often occur in either inaccessible or dangerous locations (e.g., due to icefall) which favors remote sensing methods. In particular the Landsat-archive is a popular resource for worldwide glacier velocity estimation [4], due to its long history and free availability [5,6]. Nonetheless, for many applications, the matching of whiskbroom (up to Landsat 7) and pushbroom sensors (Landsat 8) is limited to acquisitions from the same relative orbit.…”

Coseismic displacements of the 14 November 2016 <i>M</i><sub>w</sub> 7.8 Kaikoura, New Zealand, earthquake using the Planet optical cubesat constellation

“…In combination with the high spatial image resolution of around 3 m, details in the displacement field can thus become apparent that are not detected in Sentinel-2 or Landsat 8 data. The envisaged daily repeat by PlanetScope data will further improve the above displacement accuracy by enabling to measure displacements in several image pair combinations and thus exploiting a temporal stack of images and displacements (Dehecq et al, 2015;Kääb et al, 2016;Altena and Kääb, 2017;Stumpf et al, 2017).…”