EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Saurabh Kaushik1,2,3, Tejpal Singh1,2, Pawan Kumar Joshi4,5, and Andreas J Dietz3
Saurabh Kaushik et al.
  • 1Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, India.
  • 2CSIR-Central Scientific Instrument Organisation, Chandigarh 160 030, India.
  • 3German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Str. 20, 82234 Wessling, Germany
  • 4School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110 067, India.
  • 5Special Centre for Disaster Research, Jawaharlal Nehru University, New Delhi 110 067, India.

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

How to cite: Kaushik, S., Singh, T., Joshi, P. K., and Dietz, A. J.: Automated mapping of Eastern Himalayan glacial lakes using deep learning and multisource remote sensing data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2904,, 2022.

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