Automated mapping of Eastern Himalayan glacial lakes using deep learning and multisource remote sensing data

The Himalayan glacierized region has experienced a substantial rise in number and area of glacial lakes in the past two decades. These glacial lakes directly influence glacier melt, velocity, geometry, and thus overall response of the glacier to climate change. The sudden release of water from these glacial lakes poses a severe threat to downstream communities and infrastructure. Thereby, regular monitoring and modelling of these lakes bear significance in order to understand regional climate change, and mitigating the anticipated impact of glacial lake outburst flood. Here, we proposed an automated scheme for Himalayan glacial lake extent mapping using multisource remote sensing data and a state-of-the-art deep learning technique. A combination of multisource remote sensing data [Synthetic Aperture Radar (SAR) coherence, thermal, visible, near-infrared, shortwave infrared, Advanced Land Observing Satellite (ALOS) DEM, surface slope and Normalised Difference Water Index (NDWI)] is used as input to a fully connected feed-forward Convolutional Neural Network (CNN). The CNN is trained on 660 images (300×300×10) collected from 11 sites spread across Himalaya. The CNN architecture is designed for choosing optimum size, number of hidden layers, convolutional layers, filters, and other hypermeters using hit and trial method. The model performance is evaluated over 3 different sites of Eastern Himalaya, representing heterogenous landscapes. The novelty of the presented automated scheme lies in its spatio-temporal transferability over the large geographical region (~8477, 10336 and 6013 km²). The future work involves Intra-annual lake extent mapping across High-Mountain Asian region in an automated fashion.

Keywords: Glacial Lake, convolutional neural network, semantic segmentation, remote sensing, Himalaya, SAR and climate change