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

Jawak, Shridhar D.; Wankhede, Sagar F.; Luis, Alvarinho J.

doi:10.3390/rs11101207

Cited by 15 publications

(23 citation statements)

References 102 publications

(147 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is common in glacier mapping studies to evaluate the classification accuracy by comparing the derived glacier boundary with the manually digitised outlines or global inventory of glacier outlines [18,21,57]. Therefore, the glacier boundaries obtained from the classification of different datasets and glaciers (denoted by yellow colour) were compared in this section against the reference datasets of the manually digitised (MD) glacier boundary (denoted by maroon colour) and the manually corrected vector boundary of RGI version 6.0 (denoted by blue colour) as shown in Figures 10-12.…”

Section: Appreciation Of Glacier Boundariesmentioning

confidence: 99%

“…Nonetheless, OBIA has the potential to discriminate classes using spatial attributes. Additionally, the mountain shadows and some valley rock areas needed manual corrections, making it a tedious task [21]. We could automatically mask shadowed regions from the WorldView-2 + ancillary dataset using the shadow detection index and brightness temperature rulesets for both the glaciers and from the LISS-4 + ancillary dataset using the brightness attribute with accuracies lying in between 96.0 and 100%.…”

Section: Comparison With Other Debris-covered Glacier Mapping Obia Me...mentioning

confidence: 99%

See 1 more Smart Citation

Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis Approach

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Appreciation Of Glacier Boundariesmentioning

confidence: 99%

Section: Comparison With Other Debris-covered Glacier Mapping Obia Me...mentioning

confidence: 99%

Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis Approach

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The selection of ROI was done on the basis of visual interpretation, spectral reflectance, user knowledge and experience. The ROI for Edithbreen was created and the spectral reflectance was compared to previous studies (Jawak et al 2019) and spectral libraries were used to assign class to each ROI. The supervised classification of the glacier was performed using the TERCAT tool of the ENVI 5.3 into 10 classes which are crevasses, debris, dry snow, melting ice, offglacier, percolation snow, shadow, water stream, wet snow, dirty ice.…”

Section: Ground Resolution (M)mentioning

confidence: 99%

“…The supervised classification of the glacier was performed using the TERCAT tool of the ENVI 5.3 into 10 classes which are crevasses, debris, dry snow, melting ice, offglacier, percolation snow, shadow, water stream, wet snow, dirty ice. We used advanced supervised classifiers: MXL, MHD, and MD, as these have been reported to deliver higher accuracies in the previous studies (Jawak et al 2018(Jawak et al , 2019.…”

Section: Ground Resolution (M)mentioning

confidence: 99%

High-resolution multispectral mapping facies on glacier surface in the Arctic using WorldView-3 data

Luis¹,

Singh²

2020

Czech Polar Rep.

Self Cite

View full text Add to dashboard Cite

Glaciers are important and sensitive part of our environment which can be used as indicator of global warming and climate change. Glacier facies represent distinct regions of a glacier surface characterized by near surface structure and density that develop as a function of spatial variations in surface melt and accumulation. The facies mapping aids in delineating different zones of the glacier, which are useful in computing glacier mass balance and modeling. In this study we tested traditional and advanced classification techniques on the Edithbreen glacier situated in Ny-lesund, Svalbard, using WorldView-3 and Landsat 8 OLI. The comparison of the accuracy was conducted using error matrices. Six measures of accuracy were derived from the error matrices and were compared with each other to find the method delivering the most adequate output for facies mapping. The pixel-based approach applied to Landsat-8 data yielded higher accuracies (>80%) when compared to that. The object-oriented classification revealed a much better accuracy and high kappa coefficient for both low and high-resolution datasets. The study clearly indicates that the object-oriented classification provides better results for glacier facies classification when high spatial resolution is used, but for lower spatial resolution, pixel-based methods are adequate.

show abstract

“…Extraction of surface facies has been demonstrated using methods incorporating data from a single sensor [2][3][4][5] as well as multiple sensors and data products [6][7][8][9][10][11]. Many of the persistent problems of mapping mountain glaciers revolve around solving the spectral complexities of supraglacial debris [6,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 15 publications

References 102 publications

Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis Approach

Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis Approach

High-resolution multispectral mapping facies on glacier surface in the Arctic using WorldView-3 data

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

Contact Info

Product

Resources

About