Large-scale cover glacier mapping using multisource object-based image analysis approach

Abstract

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.