The climate-induced changes in glaciers are taken as the best proxies to evaluate the associated environmental processes. Alpine glaciers are the largest reservoirs of freshwater outside the poles. The increasing number of extreme weather events is shifting the dynamics of glaciers and promoting glacial disasters. Consequently, it becomes essential to perform temporal monitoring of glacier dynamics. Glaciological research relies on a very primary data, i.e., a glacier inventory. This database can be compiled using techniques which can map the glaciers and related features fast and accurate. The present research work tries to contribute to this by developing automated mapping algorithms. The research objectives are (1) To develop automated algorithms for mapping glacial terrain, (2) To develop automated glacial lake detection and mapping algorithm, and (3) To model hypsometric seasonal snow cover using meteorological parameters.
The first objective aims to develop an automated glacier mapping methodology using Landsat 4 and 5 data. This is the first attempt to generalise the methodology for automation of glacier mapping using freely available Landsat multispectral and thermal data. The algorithm classifies the glacier area with an overall accuracy of 91%. After realizing the bottlenecks in using the Landsat 4/5 data, we performed another study incorporating Landsat 8 data for the automated glacier facies characterisation and crevasse detection and mapping. The achieved accuracy is satisfactory and the future possibilities of using such automated algorithms are immense.
The second objective intends to develop an automated glacial Lake Detection Algorithm (LDA) using Landsat 8 data. We proposed a new normalised index called Moisture Index (MI), along with two new indices for the validation of the mapping outcomes: Lake Detection Index (LDI) for false detections and Lake Fraction (LF) for areal underestimation. This is the first such attempt using Landsat 8 data. The algorithm works better than pre-existing indices. The methodology has the potential applicability in any mountain range with slight threshold adjustments.
The third objective focuses on exploring empirical interdependence between temperature, snowfall, elevation and satellite-derived snow cover area in a particular season of the year for all elevation ranges within a glacier catchment. This is the first such attempt to model hypsometric seasonal snow cover in the Indian Himalaya, and to find out interpolation techniques for meteorological parameters. This approach is globally applicable and can improve the hydrological and energy balance modelling.
The LDA can be employed for developing a glacial lake inventory of entire Himalaya to facilitate their monitoring easier for disaster response. The glacier mapping algorithms can be applied for efficiently monitoring temporal glacial changes in the wake of changing climate and cryosphere. The estimation of hypsometric snow cover can modify our understanding of seasonal snow-melt contribution from various elevation zones of a glacier and can modify hydrological models. In order to overcome the spatial, spectral, and temporal resolution limitations, new satellite sensors and technology such as UAVs and LiDAR should be explored for future glacier research.