Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data

Saurabh Kaushik,Pawan K Joshi,Tejpal Singh,Andreas J Dietz,Anshuman Bhardwaj

doi:10.3390/rs14061352

Abstract

High-mountain glaciers can be covered with varying degrees of debris. Debris over glaciers (supraglacial debris) significantly alter glacier melt, velocity, ice geometry, and, thus, the overall response of glaciers towards climate change. The accumulated supraglacial debris impedes the automated delineation of glacier extent owing to its similar reflectance properties with surrounding periglacial debris (debris aside the glaciated area). Here, we propose an automated scheme for supraglacial debris mapping using a synergistic approach of deep learning and multisource remote sensing data. A combination of multisource remote sensing data (visible, near-infrared, shortwave infrared, thermal infrared, microwave, elevation, and surface slope) is used as input to a fully connected feed-forward deep neural network (i.e., deep artificial neural network). The presented deep neural network is designed by choosing the optimum number and size of hidden layers using the hit and trial method. The deep neural network is trained over eight sites spread across the Himalayas and tested over three sites in the Karakoram region. Our results show 96.3% accuracy of the model over test data. The robustness of the proposed scheme is tested over 900 km2 and 1710 km2 of glacierized regions, representing a high degree of landscape heterogeneity. The study provides proof of the concept that deep neural networks can potentially automate the debris-covered glacier mapping using multisource remote sensing data.

Highlights

Glaciers are considered as proxies of climate change, as the Earth’s glacierized region has witnessed significant ice mass loss due to atmospheric warming [1,2]
The proposed deep neural network (DNN) architecture outperformed the existing automated scheme for supraglacial debris mapping, as the results exhibited an overall classification accuracy of 96.3% over test data (Figure 4)
The results obtained via SGDNet have a varying degree of isolated pixels due to similar spectral, thermal, and motion characteristics of the supraglacial debris (SGD) and surrounding (PGD)

Summary

Introduction

Glaciers are considered as proxies of climate change, as the Earth’s glacierized region has witnessed significant ice mass loss due to atmospheric warming [1,2]. Mountain glaciers are likely to play an imperative role in sea-level rise in the coming decades, as. High Mountain Asia is expected to contribute 16 ± 5 mm sea-level equivalent as per RCP 4.5 scenario [3], significantly influencing the hydrological regime of glacierized catchments [4,5]. The changes occurring in the glacierized mountain regions have a close link to their hydrology [6], ecology [7,8], hydrochemistry [9,10], climate [11,12], and economy [2]. Recent studies have shown that the glacierized regions are quite dynamic [13–15] and are closely associated with hazards (e.g., avalanches and glacial lake outburst floods) [16,17]. Timely monitoring of glaciers is of the utmost importance to comprehend climate change at regional and global scales, and so the study of the cryosphere has received global scientific attention in the last few decades [1,18–20].

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Discussion

Conclusion