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Exploitation of optical and SAR amplitude imagery for landslide identification: a case study from Sikkim, Northeast India

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A Correction to this article was published on 28 July 2021

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Abstract

Detection and mapping of landslides is one of the most important techniques used for reducing the impact of natural disasters especially in the Himalaya, owing to its high amount of tectonic deformation, seismicity, and unfavorable climatic conditions. Moreover, the northeastern part of the Himalaya, severely affected by landslides every monsoon, is poorly studied. The information on the inventories is inhomogeneous and lacking. In this context, satellite-based earth observation data, which has significantly advanced in the last decade and often serves as a potential source for data collection, monitoring, and damage assessment for disasters in a short time span, has been implemented. Keeping in mind the above framework, this study aims to exploit the potentials of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery for identifying new landslides in vegetated and hilly areas of the northeastern part of India. In order to assess the potentials of our data and methodology, a landslide event which occurred on 13 August 2016 13:30 h (IST) in North Sikkim, India, triggered due to rainfall has been explored in detail. The landslide also resulted in the formation of a lake, 2.2 km in length and 290 m in width. Difficulty in procurement of cloud-free datasets immediately after the event led us to the use of Sentinel-1 SAR backscatter data, to assess its potential for this purpose. It is observed that the potential of SAR amplitude imagery is limited to different aspects as per the sensor look direction during the mode of acquisition. Furthermore, the present study also incorporates a change detection algorithm to evaluate the performance of the Sudden Landslide Identification Product (SLIP) model to identify new landslides using Sentinel-2 multispectral imagery. Overall, the results exhibit that integrated usage of both optical and SAR amplitude imagery may provide a plethora of information for identification and mapping of new landslides for damage assessment and early warning. All the above results combined together suggest this method for rapid identification of landslides in the Himalayan terrain with special emphasis on the northeastern part of the Himalaya. The automation of this method for future operational usage is also suggested.

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Availability of data and material

The satellite data (Sentinel-1 and Sentinel-2) that supports the findings of this study are available in the Copernicus Open Access Hub at https://scihub.copernicus.eu/. These datasets are derived from Google Earth Engine, a cloud-based platform. The Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) final run version 4 product was implemented in the present study for the analysis of precipitation. This research product in the IMERG suite can be downloaded from https://pmm.nasa.gov/data-access/downloads/gpm. Elevation dataset was available from Shuttle Radar Topography Mission (SRTM) and derived from Google Earth Engine. However, all the datasets will be available from the authors upon reasonable request.

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Acknowledgements

We acknowledge European Space Agency (ESA) for Sentinel-1 SAR and Sentinel-2 optical data provision free of cost. We also gratefully acknowledge Google LLC for offering the Google Earth Engine platform. We would also like to thank Google Inc. for providing Google Earth Pro.

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  1. NIIT University, Neemrana (Rajasthan), India

    Thota Sivasankar

  2. Indian School of Mines), Indian Institute of Technology, Dhanbad (Jharkhand), 826004, India

    Swakangkha Ghosh

  3. G. B. Pant, National Institute of Himalayan Environment and Sustainable Development, Sikkim Regional Center, Gangtok (Sikkim), India

    Mayank Joshi

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Sivasankar, T., Ghosh, S. & Joshi, M. Exploitation of optical and SAR amplitude imagery for landslide identification: a case study from Sikkim, Northeast India. Environ Monit Assess 193, 386 (2021). https://doi.org/10.1007/s10661-021-09119-6

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  • DOI: https://doi.org/10.1007/s10661-021-09119-6

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