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Supervised Geomorphic Mapping of Himalayan Rivers Based on Sentinel-2 Data

Zarka Mukhtar,S. Bizzi,Francesco Comiti

Published 2023 in Remote Sensing

ABSTRACT

The Himalayan region is a hotspot in terms of expected future hydrological and geomorphological variations induced by climate change on proglacial areas and the related implications for human societies established along the downstream rivers. Due to the remoteness of the proglacial zones in the Himalayas and the associated logistical problems in carrying out traditional field and UAV-based morphological monitoring activities, remote sensing here plays a crucial role to monitor past and current fluvial dynamics, which could be used to anticipate future changes; however, there has been, so far, limited research on morphological changes in Himalayan proglacial rivers. To address this gap, a morphological classification model was designed to classify recent changes in Himalayan proglacial rivers using the Google Earth Engine platform. The model is the first of its kind developed for the Himalayan region and uses multispectral S-2 satellite data to delineate submerged water channels, vegetated surfaces, and emerged, unvegetated sediment bars, and then to track their variations over time. The study focused on three training sites: Langtang-Khola (Nepal), Saltoro (Pakistan), and Nubra (Jammu and Kashmir) rivers, and one testing site, the Ganga-Bhagirathi River (India). A total of 900 polygons were used as training samples for the random forest classifier, which were further divided into 70% calibration and 30% validation datasets for the training sites, and a separate validation dataset was acquired from the testing site to assess the model performance. The model achieved high accuracy, with an average overall accuracy of 96% and a kappa index of 0.94, indicating the reliability of the S2 data for modeling proglacial geomorphic features in the Himalayan region. Therefore, this study provides a reliable tool to detect past and current morphological changes occurring in the Himalayan proglacial rivers, which will be of great value for both research and river management purposes.

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    Remote Sensing

  • Publication date

    2023-09-25

  • Fields of study

    Geography, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs15194687
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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