Temporal Variability of the Satopanth Glacier Facies at Sub-pixel Scale, Garhwal Himalaya, India

Yousuf, Bisma; Shukla, Anurag; Arora, Manoj K.

doi:10.1007/978-3-030-70238-0_5

Cited by 3 publications

(5 citation statements)

References 51 publications

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“…Increase in the DMI and SGD areas are suggestive of the glacier's health deteriorating. These ndings corroborate with that of Yousuf et al (2022), which demonstrated that ice melting dominates the transition of glacier characteristics.…”

Section: Relation Between Glacier Features Changes and Climate Variablessupporting

confidence: 89%

Linking Variations in Gangotri Glacier Features Mapped at a Large-Scale with Climate Variables

Mitkari

Sofat

Arora

et al. 2023

Preprint

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Changes in glacier area, glacial lakes, debris cover, and geomorphological features such as debris fans have a significant impact on glacial dynamics. Therefore, precise and timely observation and tracking of glacier surface changes is a necessity. The availability of high spatial resolution remote sensing images has made it viable to analyse the glacier surface changes at a local level. However, with increasing spatial resolution, the spectral variability increases, giving rise to additional challenges (such as false changes and misregistration) in the change detection process. These challenges can preferably be dealt with using object-based change detection approach than the conventional pixel-based change detection approach. In this study, a class-object change detection approach has been followed in which the independently classified objects from multi-temporal images are compared to detect the changes. As a case study, the changes in Gangotri Glacier (Uttarakhand Himalayas) features have been studied using high spatial resolution WorldView-2 image and Linear Imaging Self-Scanning System (LISS)-4 image for a three-year period 2011–2014. The spectral correspondences between glacier surface and non-glacier surface have been handled by considering brightness temperature and slope as ancillary data to improvise their distinction. A change detection accuracy of ~ 84% has been obtained using the OBCD approach.

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Section: Relation Between Glacier Features Changes and Climate Variablessupporting

confidence: 89%

Linking Variations in Gangotri Glacier Features Mapped at a Large-Scale with Climate Variables

Mitkari

Sofat

Arora

et al. 2023

Preprint

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“…Rule sets themselves are knowledge-driven on account of stepwise development. Even sub-pixel mapping of IMD utilizing SVM has relied on ancillary information from both DEMs and thermal bands when using medium-to-high resolution imagery [70]. Our results point toward the efficacy of VHR imagery in congruence with appropriate mapping methods for effective characterization of debris.…”

Section: Manifestation Of Faciesmentioning

confidence: 73%

“…The mapping procedures outlined in the current study may be applied on medium resolution satellite data. However, subpixel classification may be necessary to enhance the characterization of facies in coarser resolutions [70]. The direct alternative would be to utilize fine spatial resolution imagery [82] such as that used in the current study combined with the proposed methodology to map facies.…”

Section: Discussionmentioning

confidence: 99%

“…Accurate mapping of IMD like dirty ice and debris is important because the conversion of IMD to debris can be a direct assessment of the impact of climatic variations on the health of the glacier [92]. A reliance on thermal bands to isolate ice mixed debris and debris from the overall glacier body is well reported in literature [5,70,[92][93][94]. In the current study, we lack thermal bands in the sensor.…”

Section: Manifestation Of Faciesmentioning

confidence: 98%

“…); however, PBIA can be effective if the image processing and mapping methods are selected carefully. Recently, Yousuf et al [70] mapped wet snow along with other available facies using AWiFS and Sentinel-2A. The authors utilized ancillary data such as elevation, band ratios, and variances derived from principal components to perform sub-pixel classification using a pair wise classification strategy in a support vector machine (SVM).…”

Section: Manifestation Of Faciesmentioning

confidence: 99%

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Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines

Jawak¹,

Wankhede

Luis

et al. 2022

Remote Sensing

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Fundamental image processing methods, such as atmospheric corrections and pansharpening, influence the signal of the pixel. This morphs the spectral signature of target features causing a change in both the final spectra and the way different mapping methods may assign thematic classes. In the current study, we aim to identify the variations induced by popular image processing methods in the spectral reflectance and final thematic maps of facies. To this end, we have tested three different atmospheric corrections: (a) Quick Atmospheric Correction (QUAC), (b) Dark Object Subtraction (DOS), and (c) Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods: (a) Hyperspherical Color Sharpening (HCS) and (b) Gram–Schmidt (GS). WorldView-2 and WorldView-3 satellite images over Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard are tested via spectral subsets in traditional (BGRN1), unconventional (CYRN2), visible to near-infrared (VNIR), and the complete available spectrum (VNIR_SWIR). Thematic mapping was comparatively performed using 12 pixel-based (PBIA) algorithms and 3 object-based (GEOBIA) rule sets. Thus, we test the impact of varying image processing routines, effectiveness of specific spectral bands, utility of PBIA, and versatility of GEOBIA for mapping facies. Our findings suggest that the image processing routines exert an extreme impact on the end spectral reflectance. DOS delivers the most reliable performance (overall accuracy = 0.64) averaged across all processing schemes. GEOBIA delivers much higher accuracy when the QUAC correction is employed and if the image is enhanced by GS pansharpening (overall accuracy = 0.79). SWIR bands have not enhanced the classification results and VNIR band combination yields superior performance (overall accuracy = 0.59). The maximum likelihood classifier (PBIA) delivers consistent and reliable performance (overall accuracy = 0.61) across all processing schemes and can be used after DOS correction without pansharpening, as it deteriorates spectral information. GEOBIA appears to be robust against modulations in atmospheric corrections but is enhanced by pansharpening. When utilizing GEOBIA, we find that a combination of spatial and spectral object features (rule set 3) delivers the best performance (overall accuracy = 0.86), rather than relying only on spectral (rule set 1) or spatial (rule set 2) object features. The multiresolution segmentation parameters used here may be transferable to other very high resolution (VHR) VNIR mapping of facies as it yielded consistent objects across all processing schemes.

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Relationship between the variations in glacier features classified on a large scale with climate variables: a case study of Gangotri Glacier

Mitkari,

Sofat,

Arora

et al. 2024

Environ Monit Assess

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Temporal Variability of the Satopanth Glacier Facies at Sub-pixel Scale, Garhwal Himalaya, India

Cited by 3 publications

References 51 publications

Linking Variations in Gangotri Glacier Features Mapped at a Large-Scale with Climate Variables

Linking Variations in Gangotri Glacier Features Mapped at a Large-Scale with Climate Variables

Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines

Relationship between the variations in glacier features classified on a large scale with climate variables: a case study of Gangotri Glacier

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