Modeling Permafrost Distribution Using Geoinformatics in the Alaknanda Valley, Uttarakhand, India

Arvind Chandra Pandey, Tirthankar Ghosh, Bikash Ranjan Parida (), Chandra Shekhar Dwivedi and Reet Kamal Tiwari
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Arvind Chandra Pandey: Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi 835222, India
Tirthankar Ghosh: Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi 835222, India
Bikash Ranjan Parida: Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi 835222, India
Chandra Shekhar Dwivedi: Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi 835222, India
Reet Kamal Tiwari: Department of Civil Engineering, Indian Institute of Technology Ropar, Ropar 140001, India

Sustainability, 2022, vol. 14, issue 23, 1-19

Abstract: The Indian Himalayan region is experiencing frequent hazards and disasters related to permafrost. However, research on permafrost in this region has received very little or no attention. Therefore, it is important to have knowledge about the spatial distribution and state of permafrost in the Indian Himalayas. Modern remote sensing techniques, with the help of a geographic information system (GIS), can assess permafrost at high altitudes, largely over inaccessible mountainous terrains in the Himalayas. To assess the spatial distribution of permafrost in the Alaknanda Valley of the Chamoli district of Uttarakhand state, 198 rock glaciers were mapped (183 active and 15 relict) using high-resolution satellite data available in the Google Earth database. A logistic regression model (LRM) was used to identify a relationship between the presence of permafrost at the rock glacier sites and the predictor variables, i.e., the mean annual air temperature (MAAT), the potential incoming solar radiation (PISR) during the snow-free months, and the aspect near the margins of rock glaciers. Two other LRMs were also developed using moderate-resolution imaging spectroradiometer (MODIS)-derived land surface temperature (LST) and snow cover products. The MAAT-based model produced the best results, with a classification accuracy of 92.4%, followed by the snow-cover-based model (91.9%), with the LST-based model being the least accurate (82.4%). All three models were developed to compare their accuracy in predicting permafrost distribution. The results from the MAAT-based model were validated with the global permafrost zonation index (PZI) map, which showed no significant differences. However, the predicted model exhibited an underestimation of the area underlain by permafrost in the region compared to the PZI. Identifying the spatial distribution of permafrost will help us to better understand the impact of climate change on permafrost and its related hazards and provide necessary information to decision makers to mitigate permafrost-related disasters in the high mountain regions.

Keywords: permafrost; logistic regression model; rock glacier; Indian Himalayas (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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