An improved coupled framework for Glacier classification: an integration of optical and thermal infrared remote-sensing bands. Issue 20 (18th October 2018)

• Record Type: Journal Article
• Authors: Singh, Vishal; Goyal, Manish Kumar
• Is Part Of: International journal of remote sensing. Volume 39:Issue 20(2018)
• Page Start: 6864
• Page End: 6892
• Publication Date: 2018-10-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2018.1468104
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: This study evaluates the applicability of optical and thermal infrared (TIR) bands by the applications of two different satellite sensors for characterizing debris covers of Zemu glacier situated in the North Sikkim Himalayas India. Differentiating supraglacial debris (SGD), clean ice, and ice-covered glaciers from periglacial debris (PGD) cover is a challenging task due to similarities between their spectra. Previous studies suggest that the difference between thermal and optical properties can be used in the separation of SGD (part of the glacier boundary) and PGD (outside the glacier boundary) accurately. Thus a novel multi-criteria approach has been constructed to characterize the glacier cover utilizing Maximum Likelihood Classifier (MLC) and hybrid classification (HC) methods. Based on the integration of optical and TIR bands from two satellite systems such as Landsat and Indian Remote Sensing Satellite Linear Imaging Spectral Scanner 3, total six different image combinations were prepared to explore glacier features under various mathematical and image processing operations. Each image band combination represents a distinctive characterization of the debris cover of Zemu glacier. The spatial-spectral profiles of glacial features (spectral signatures) were drawn to distinguish SGD and PGD. The accuracy assessment was performed to test the correctness of classified maps resulted from MLC and HC methods. The HC maps show a larger efficiency (near to 90%) than MLC maps (near to 85%). The outcomes offered better understandings in the classification of SGD and PGD by using the combined products of optical and TIR remote-sensing sensors. The ground-based observations such as GPS points, field photographs of the Zemu glacier topography, and high-resolution Google Earth images helped to reduce the misclassification and improved the classification accuracy. Furthermore, a cloud removal method was applied for TIR bands to improve the accuracy of the glacier classification. The glacier features and their area statistics have been compared to temporal time scales, illustrating annual fluctuations in glacial characteristics.