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Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas

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  • Vishakha Sood

    (Aiotronics Automation, Palampur 176 061, Himachal Pradesh, India
    Civil Engineering Department, Indian Institute of Technology (IIT), Ropar 140 001, Punjab, India)

  • Reet Kamal Tiwari

    (Civil Engineering Department, Indian Institute of Technology (IIT), Ropar 140 001, Punjab, India)

  • Sartajvir Singh

    (Civil Engineering Department, Indian Institute of Technology (IIT), Ropar 140 001, Punjab, India
    Chitkara University School of Engineering and Technology, Chitkara University, Baddi 174 103, Himachal Pradesh, India)

  • Ravneet Kaur

    (APEX Institute of Technology, Department of Computer Science Engineering, Chandigarh University, Mohali 140 413, Punjab, India)

  • Bikash Ranjan Parida

    (Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi 835 222, Jharkhand, India)

Abstract

Glacier, snow, and ice are the essential components of the Himalayan cryosphere and provide a sustainable water source for different applications. Continuous and accurate monitoring of glaciers allows the forecasting analysis of natural hazards and water resource management. In past literature, different methodologies such as spectral unmixing, object-based detection, and a combination of various spectral indices are commonly utilized for mapping snow, ice, and glaciers. Most of these methods require human intervention in feature extraction, training of the models, and validation procedures, which may create bias in the implementation approaches. In this study, the deep learning classifier based on ENVINet5 (U-Net) architecture is demonstrated in the delineation of glacier boundaries along with snow/ice over the Bara Shigri glacier (Western Himalayas), Himachal Pradesh, India. Glacier monitoring with Landsat data takes the advantage of a long coverage period and finer spectral/spatial resolution with wide coverage on a larger scale. Moreover, deep learning utilizes the semantic segmentation network to extract glacier boundaries. Experimental outcomes confirm the effectiveness of deep learning (overall accuracy, 91.89% and Cohen’s kappa coefficient, 0.8778) compared to the existing artificial neural network (ANN) model (overall accuracy, 88.38% and kappa coefficient, 0.8241) in generating accurate classified maps. This study is vital in the study of the cryosphere, hydrology, agriculture, climatology, and land-use/land-cover analysis.

Suggested Citation

  • Vishakha Sood & Reet Kamal Tiwari & Sartajvir Singh & Ravneet Kaur & Bikash Ranjan Parida, 2022. "Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13485-:d:946864
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    References listed on IDEAS

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    1. Bo Pang & Erik Nijkamp & Ying Nian Wu, 2020. "Deep Learning With TensorFlow: A Review," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 227-248, April.
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    1. Arvind Chandra Pandey & Tirthankar Ghosh & Bikash Ranjan Parida & Chandra Shekhar Dwivedi & Reet Kamal Tiwari, 2022. "Modeling Permafrost Distribution Using Geoinformatics in the Alaknanda Valley, Uttarakhand, India," Sustainability, MDPI, vol. 14(23), pages 1-19, November.
    2. Gurwinder Singh & Sartajvir Singh & Ganesh Sethi & Vishakha Sood, 2022. "Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data," Geographies, MDPI, vol. 2(4), pages 1-10, November.
    3. Elmer Calizaya & Wilber Laqui & Saul Sardón & Fredy Calizaya & Osmar Cuentas & José Cahuana & Carmen Mindani & Walquer Huacani, 2023. "Snow Cover Temporal Dynamic Using MODIS Product, and Its Relationship with Precipitation and Temperature in the Tropical Andean Glaciers in the Alto Santa Sub-Basin (Peru)," Sustainability, MDPI, vol. 15(9), pages 1-20, May.

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