Leveraging Machine Learning and Google Earth Engine for Snowline Altitude Analysis: Insights from the Parbati Basin, India

Abstract

Glaciers are highly responsive to climate variations, yet monitoring them in the rugged Himalaya region poses significant challenges. This study explores the effectiveness and cost-efficiency of using machine learning models integrated with remote sensing data from Google Earth Engine (GEE) to map glacier accumulation (snow) zones in the Pārbati Valley. We tested various machine learning algorithms, including Otsu (image segmentation), K-means and cascade K-means (unsupervised classification), and random forest, minimum distance, smile CART, naive Bayes, robust tree, and support vector machine (supervised classification). Our analysis shows that the Otsu method, along with K-means, cascade K-means, and all supervised classification methods except smile CART and naive Bayes, perform similarly in mapping snowlines. Notably, the Otsu method achieved a maximum predictable error of 57 meters, which is a substantial improvement over traditional methods and indicates higher accuracy in snowline mapping. The study reveals that the regional snowline in the Pārbati Valley ranged between 5048 meters and 5113 meters during the study period. Given its superior performance, the Otsu method is recommended for identifying snowline altitudes across a wide range of glaciers in the Himalayas.