Mapping glacial lakes and glacial lake outburst floods in the Alaknanda river Basin, Uttarakhand: A remote sensing and GIS-based approach Vishwas Poonam1,*, Tiwari K. C.2,**, Rongali Gopinadh3,*** 1Post-Doctoral Fellow, Multidisciplinary Centre for Geoinformatics, Delhi Technological University, Delhi-110042, India 2Professor and Head, Multidisciplinary Centre for Geoinformatics, Delhi Technological University, Delhi-110042, India 3Project Scientist-II, National Center for Medium Range Weather Forecasting (NCMRWF), MoES, Noida, Uttar Pradesh, India (*Corresponding author) email id: dr.poonamvishwas@gmail.com
**kcchtphd@gmail.com
***gopinadh01@gmail.com
Online Published on 23 April, 2024. Abstract Over the past half-century, numerous lakes have surfaced in various alpine regions worldwide, posing a significant threat. This phenomenon occurs as lakes expand from melting glaciers, driven by global warming, leading to potential bursting and extensive damage. India, with a concentration of glaciers in Ladakh, Sikkim, Himachal Pradesh, and Uttarakhand, along with a few in Arunachal Pradesh, faces such challenges. The 2013 glacial lake outburst flood (GLOF) in Uttarakhand impacted over 100,000 lives, caused thousands of deaths, and inflicted substantial infrastructural damage. This study delves into the risk assessment of potentially hazardous glacier lakes in the Alaknanda River basin in Uttarakhand, India. Identification of vulnerable or perilous lakes utilizes metrics such as surface area, position relative to the parent glacier, slope, distance from the basin’s outlet, and the presence of downstream lakes. The initial focus involves accurate mapping of glaciers and glacial lakes in the study area, employing multispectral satellite images from Landsat satellite data. For glacier mapping, we utilized the Normalized Difference Snow Index (NDSI) image, Band Ratio, and a slope map of the area. However, the NDSI and Band Ratio methods proved insufficient in differentiating the extent of debris -covered glacier ice due to similar spectral signatures from the surrounding debris. Specifically, NDSI (GREEN/SWIR) and Band Ratio (VIS/NIR) misclassified proglacial lakes in threshold glacier areas. On the other hand, Band Ratio NIR/SWIR accurately classified clean glacier ice with partly glacier ponds. Hence, Band Ratio NIR/SWIR emerges as a more suitable method for identifying clean glacier ice compared to NDSI (GREEN/SWIR). Moreover, Band Ratio NIR/SWIR outperforms NDSI (GREEN/SWIR) in shadow areas. In this work, manual identification of glaciers involved using a slope map in conjunction with the NDSI technique. The text describes the process of glacial lake mapping in the Alaknanda basin using methods such as NDWI, NDSI, and unsupervised classification. The results were overlaid on Google Earth to identify 463 glacial lakes. Manual editing was then performed using Google Earth images. Subsequently, a comprehensive inventory of the glacial lakes in the Alaknanda basin was created, highlighting four potentially dangerous lakes based on specific factors. Further investigation In ArcGIS 10.1, various metrics were computed, including volume and area, along with other parameters like cross-sections from the lake to the outlet. Consequently, the area of the potentially hazardous Lake 196 is 0.231 square km, with a volume of 508.315 cubic meters. The distance of this lake from the outlet of the basin is 1.372 km. Top Keywords Glacial lake outburst flood (GLOF), GIS, Remote Sensing, NDWI. Top |