Rock glaciers as proxy for machine learning based debris-covered glacier mapping of Kinnaur District, Himachal Pradesh

Abstract

This research introduces an innovative approach by utilising rock glaciers (RGs) as a proxy for mapping debris-covered glaciers (DCGs). This approach focuses on the interconnected nature of glaciers, DCGs and RGs in a continuum where DCGs can transform into RGs over time due to various processes. This study utilises six machine learning models—logistic regression (LR), support vector machine (SVM), K-nearest neighbour (KNN), Naïve Bayes (NB), decision tree (DT) and random forest (RF)—combined with multispectral satellite data (Sentinel-2 and Landsat 8) and topographical data derived from ALOS PALSAR DEM. Performance metrics such as accuracy, area under the curve (AUC) score, precision, recall and F1-score were evaluated to assess model performance. This detailed mapping provides a precise estimation of the extent of DCGs in the Kinnaur district. The estimated DCG areas revealed intriguing variation across models, with RF (9.71%), KNN (9.67%) and NB (9.41%) yielding similar predictions. SVM (11.61%) projected a slightly larger DCG area, whereas DT (5.54%) and LR (25.55%) provided contrasting results. Validation against high-resolution satellite images, Google Earth images and glacier inventories confirmed the accuracy and reliability of our approach. Based on our findings for our specific study, the most effective method for mapping DCGs is RF, followed by KNN, NB, DT and SVM. The combination of machine learning models and RG data presents a novel and promising approach to remote sensing-based DCG mapping, with potential applications for other regions and broader environmental studies.