Abstract

Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. 71km2 in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring.

Highlights

  • Mapping and monitoring glacial lakes in the high mountain ranges are essential due to the vulnerability of the downstream population to the glacial lakes outburst floods (GLOF)

  • We explored the potential of PlanetScope imagery for glacial lakes mapping in HKKH

  • Due to inherent difficulties in mapping surface water in mountainous regions, deep learning-based model was used for mapping of surface water

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Summary

Introduction

Mapping and monitoring glacial lakes in the high mountain ranges are essential due to the vulnerability of the downstream population to the glacial lakes outburst floods (GLOF). The high mountain ranges of Hindu Kush, Karakoram and Himalaya (HKKH) contain a large number of glaciers and glacial lakes. The retreating glaciers, as well as the warming climate, has increased the number and size of glacial lakes. A recent GLOF event in the Chitral district of the Hindu Kush range was caused by a newly formed glacial lake at an elevation of 4500 m [7]. The surge of the Khordopin glacier and the Shishper glacier in the Karakoram range have created such lakes and the release of water from these lakes caused flooding in the downstream area [8,10,11]. It is essential to find automatic methods for mapping surface water using remote sensing imagery

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