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

Vishakha Sood, Reet Kamal Tiwari, Sartajvir Singh (), Ravneet Kaur and Bikash Ranjan Parida
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Vishakha Sood: Aiotronics Automation, Palampur 176 061, Himachal Pradesh, 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
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

Sustainability, 2022, vol. 14, issue 20, 1-13

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.

Keywords: deep learning; ENVINet5 (U-Net); artificial neural network (ANN); cryosphere; glacier boundaries (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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