Glacier surface characteristics derivation and monitoring using Hyperspectral datasets: a case study of Gepang Gath glacier, Western Himalaya

Gore, Akshay; Mani, Sneh; HariRam, R P; Shekhar, Chander; Ganju, Ashwagosha

doi:10.1080/10106049.2017.1357766

Cited by 10 publications

(13 citation statements)

References 34 publications

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“…Figure 5 and Supplementary Figure S2 portray the range of scaled reflectance as derived over each of the glaciers in the WV-2 imagery. The lack of short-wave infrared (SWIR) bands prevents comparison to in situ derived reflectance measurements, such as that performed by Gore et al [40] and Negi et al [107]. The graph of scaled reflectance ( Figure 5) obtained here holds a striking resemblance to that derived by Shukla et al [90], in terms of the reflectance of snow.…”

Section: Resultssupporting

confidence: 53%

“…Such rectifications are much more important when mapping is performed over large regions [33]. The basis of differentiation between the reflectance characteristics of snow, ice, ice-mixed debris, crevasses, debris, and shadowed area (elaborated in Section 3.2.2) is their reduced reflectance properties [40]. The NIR-2 band however displays a higher average reflectance than is usually observed.…”

Section: Resultsmentioning

confidence: 99%

“…Singular use of the visual to near infrared (VNIR) and short wave infrared (SWIR) component of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor has also been demonstrated [33]. However, a large number of studies have now begun to integrate varying sensors, data products, field spectra, and freely available digital elevation models (DEMs) into pixel-and object-based environments to extract surface glacier facies [34][35][36][37][38][39][40]. These studies provide a vast working knowledge base to design and implement an effective facies mapping technique.…”

Section: Glacier Facies Via Remote Sensingmentioning

confidence: 99%

“…This study not only incorporates atmospherically corrected bands of the visible spectrum but also of the near-infrared region. Therefore, after investigations of relevant literature [78][79][80], this study employed the FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction module [40,66,69,81] to perform this task, which is based on the moderate resolution atmospheric transmission 4 (MODTRAN4) radiative transfer code [82]. This consisted of two steps; the first was the retrieval of atmospheric parameters, including an aerosol description (most importantly, the visibility or optical depth) and the column water vapor amount (Supplementary Material: Equation (S1)).…”

Section: Preprocessingmentioning

confidence: 99%

“…The shadowed area maintains low reflectance throughout the multispectral range. The identification of each of these facies and their distinctions from snow and ice are based on their variations in reduced reflectance [40]. A detailed graph with enlarged scales for each individual facies can be found in Supplementary Figure S2.…”

Section: Spectral Characteristics Of Surface Faciesmentioning

confidence: 99%

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Explorative Study on Mapping Surface Facies of Selected Glaciers from Chandra Basin, Himalaya Using WorldView-2 Data

2019

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Mapping of surface glacier facies has been a part of several glaciological applications. The study of glacier facies in the Himalayas has gained momentum in the last decade owing to the implications imposed by these facies on the melt characteristics of the glaciers. Some of the most commonly reported surface facies in the Himalayas are snow, ice, ice mixed debris, and debris. The precision of the techniques used to extract glacier facies is of high importance, as the result of many cryospheric studies and economic reforms rely on it. An assessment of a customized semi-automated protocol against conventional and advanced mapping algorithms for mapping glacier surface facies is presented in this study. Customized spectral index ratios (SIRs) are developed for effective extraction of surface facies using thresholding in an object-based environment. This method was then tested on conventional and advanced classification algorithms for an evaluation of the mapping accuracy for five glaciers located in the Himalayas, using very high-resolution WorldView-2 imagery. The results indicate that the object-based image analysis (OBIA) based semi-automated SIR approach achieved a higher average overall accuracy of 87.33% (κ = 0.85) than the pixel-based image analysis (PBIA) approach. Among the conventional methods, the Maximum Likelihood performed the best, with an overall accuracy of 78.71% (κ = 0.75). The Constrained Energy Minimization, with an overall accuracy of 68.76% (κ = 0.63), was the best performer of the advanced algorithms. The advanced methods greatly underperformed in this study. The proposed SIRs show a promise in the mapping of minor features such as crevasses and in the discrimination between ice-mixed debris and debris. We have efficiently mapped surface glacier facies independently of short-wave infrared bands (SWIR). There is a scope for the transferability of the proposed SIRs and their performance in varying scenarios.

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Section: Resultssupporting

confidence: 53%

Section: Resultsmentioning

confidence: 99%