A hierarchical knowledge-based classification for glacier terrain mapping: a case study from Kolahoi Glacier, Kashmir Himalaya

A glacierized terrain comprises different land covers, and their mapping using satellite data is challenged by their spectral similarity. We propose a hierarchical knowledge-based classification (HKBC) approach for differentiation of glacier terrain classes and mapping of glacier boundaries, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery and Global Digital Elevation Model (GDEM). The methodology was tested over Kolahoi Glacier, Kashmir Himalaya. For the sequential extraction of various glacier terrain classes, several input layers were generated from the primary datasets by applying image-processing techniques. Noticeable differences in temperature and spectral response between supraglacial debris and periglacial debris facilitated the development of a thermal glacier mask and normalized-difference debris index, which together with slope enabled their differentiation. These and the other layers were then used in several discrete tests in HKBC, to map various glacier terrain classes. An ASTER visible near-infrared image and 42 field points were used to validate results. The proposed approach satisfactorily classified all the glacier terrain classes with an overall accuracy of 89%. The Z-test reveals that results obtained from HKBC are significantly (at 95% confidence level) better than those from a maximum likelihood classifier (MLC). Glacier boundaries obtained from HKBC were found to be plausibly better than those obtained from MLC and visual interpretation.