Remote sensing flow velocity of debris-covered glaciers using Landsat 8 data

Abstract

Changes in ice velocity of a glacier regulate its mass balance and dynamics. The estimation of glacier flow velocity is therefore an important aspect of temporal glacier monitoring. The utilisation of conventional ground-based techniques for detecting glacier surface flow velocity in the rugged and alpine Himalayan terrain is extremely difficult. Remote sensing-based techniques can provide such observations on a regular basis for a large geographical area. Obtaining freely available high quality remote sensing data for the Himalayan regions is challenging. In the present work, we adopted a differential band composite approach, for the first time, in order to estimate glacier surface velocity for non-debris and supraglacial debris covered areas of a glacier, separately. We employed various bandwidths of the Landsat 8 data for velocity estimation using the COSICorr (co-registration of optically sensed images and correlation) tool. We performed the accuracy assessment with respect to field measurements for two glaciers in the Indian Himalaya. The panchromatic band worked best for non-debris parts of the glaciers while band 6 (SWIR -short wave infrared) performed best in case of debris cover. We correlated six temporal Landsat 8 scenes in order to ensure the performance of the proposed algorithm on monthly as well as yearly timescales. We identified sources of error and generated a final velocity map along with the flow lines. Over-and underestimates of the yearly glacier velocity were found to be more in the case of slow moving areas with annual displacements less than 5 m. Landsat 8 has great capabilities for such velocity estimation work for a large geographic extent because of its global coverage, improved spectral and radiometric resolutions, free availability and considerable revisit time.

Analyzing Glacier Surface Motion Using LiDAR Data

“…However, the size and extent of glaciers, along with challenging environmental conditions, can severely limit data collection in the field or, in particularly hazardous conditions, field work may be precluded entirely [2][3][4]. As remote sensing data becomes more widely available, it is becoming a primary data collection technique in the cryosphere, particularly in areas that are inaccessible to traditional field methods [2].…”

“…The optical and geodetic surveys require little post-processing but only provide data at discrete points, usually covering a fraction of the entire glacier. Remote sensing techniques, including synthetic aperture radar (SAR) and multispectral imagery, are increasingly being used to detect and monitor changes in the cryosphere [4,[9][10][11][12]. In difficult to reach areas of the Himalayas, SAR has been used to measure flow velocities at the Kangshung and Khumbu Glaciers [11].…”

Comparative Study of Hydrology and Icemelt in Three Nepal River Basins Using the Glacio-Hydrological Degree-Day Model (GDM) and Observations From the Advanced Scatterometer (ASCAT)

“…Regionally, it is expected to increase discharge until 2050 and then decrease (Lutz et al, 2016). In western Himalaya, glacier melt's contribution to runoff is projected to increase by 16-50%, with a 1-3 • C increase in temperature (Singh and Kumar, 1997;Tahir et al, 2011;Sam et al, 2016). While about 53 million people inhabit the 2400 km of the Himalayas, more than one billion people living downstream depends on the water from HMA, for the food and energy production (Apollo, 2017).…”

Changing climate and glacio‐hydrology in Indian Himalayan Region: a review

“…However, there are several studies which have successfully mapped glacial features in glacierized catchments with debris cover . Freely available remotely sensed data have recently been used to develop algorithms for mapping features, , and dynamics of debris‐covered glaciers.…”

“…This is in agreement with other studies in the region, which suggest that mass loss has increased in recent decades in Lahaul and Spiti region. The mass balance reconstruction using surface albedo of Chhota Shigri glacier shows significantly more negative value (−0.68 ± 0.10 m w.e./year) when compared to other mass balance data of the same glacier . A valid reason for this disagreement can be the use of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images having too coarse of a spatial resolution (500 m) to be compared with the point‐based measurements of mass balance on the glacier.…”