Large-scale debris cover glacier mapping can be efficiently conducted from high spatial resolution (HSR) remote sensing imagery using object-based image analysis (OBIA), which works on a group of pixels. This paper presents the spectral and spatial capabilities of OBIA to classify multiple glacier cover classes using a multisource approach by integrating multispectral, thermal, and slope information into one workflow. The novel contributions of this study are effective mapping of small yet important geomorphological features, classification of shadow regions without manual corrections, discrimination of snow/ice, ice-mixed debris, and supraglacial debris without using shortwave infrared bands, and an adaptation of an area-weighted error matrix specifically built for assessing OBIA’s accuracy. The large-scale glacier cover map is produced with a high overall accuracy of ≈94% (area-weighted error matrix). The proposed OBIA approach also proved to be effective in mapping minor geomorphological features such as small glacial lakes, exposed ice faces, debris cones, rills, and crevasses with individual class accuracies in the range of 96.9–100%. We confirm the portability of our proposed approach by comparing the results with reference glacier inventories and applying it to different sensor data and study areas.
This study estimates energy and mass balance at the edge of the Antarctic ice sheet close to a non-glaciated area. An automatic weather station was installed on the ice sheet, near an ice free area of Schirmacher Oasis in Dronning Maud Land, East Antarctica. Hourly snow-meteorological parameters were recorded and observed during the summer of the year 2007-08. Hourly radiative and turbulent energy fluxes were estimated at the ice surface. An ultrasonic sensor was used to measure accumulation or ablation at the glacier surface. Ground Penetrating Radar was also used to measure the changes in ice thickness at the observation point. The net radiative flux was the main heat source and the latent heat flux was the main heat sink for the ice sheet with seasonal average values of 98 W m -2 and -86.7 W m -2 respectively. There was a high ablation rate for the ice sheet near the non-glaciated area with a seasonal mean of 0.0172 m w.e. per day. Over the period 10 November 2007-7 February 2008 the mass balance was -1.53 m w.e. Good correlation (r 2 5 0.97) was observed between estimated and observed hourly ablation of the glacier. Sublimation and melt processes contributed 16.5% and 83.5% respectively to the net summer ablation.
In the present study we estimate the velocity and thickness of the Patseo glacier, Himachal Pradesh, India. The average velocity of the glacier was estimated as ~5.47 m/year using co-registration of optically sensed images and correlation (COSI-Corr) method. The glacier thickness was found to vary between 12 and 278 m, with an average value 59 m. The total glacier ice volume was estimated as ~15.8 × 10 7 m 3 , with equivalent water reservoir of ~14.5 × 10 7 m 3 . Ground penetrating radar (GPR) surveys were conducted during 2004 and 2013 for validation of the estimated glacier thickness. The glacier thickness estimated using COSI-Corr method was found to be in agreement with GPR-retrieved glacier thickness (RMSE = 4.75 m; MAE = 3.74 m). The GPR profiles collected along the same geographic locations on the glacier during 2004 and 2013 showed a reduction in ice thickness of ~1.89 m, and thus resulting in an annual ice thickness decrease of ~0.21 m. The glacier area was estimated for 2004 and 2013 using LISS IV satellite data and found to be ~2.52 and ~2.30 sq. km respectively. This shows an annual reduction of ~0.024 sq. km in glacier area. The total annual loss in glacier ice volume was estimated as ~4.55 × 10 5 m 3 . This loss in the glacier ice volume of the Patseo glacier is supported by the snow and meteorological observations collected at a nearby field observatory of Snow and Avalanche Study Establishment (SASE). The climate data collected at SASE meteorological observatory at Patseo (3800 m), between 1993-94 and 2014-15 showed an increasing trend in the mean annual temperature and a decreasing trend in winter precipitation.
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