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Assessment of tropical cyclone amphan affected inundation areas using sentinel-1 satellite data

Assessment of tropical cyclone amphan affected inundation areas using sentinel-1 satellite data
Assessment of tropical cyclone amphan affected inundation areas using sentinel-1 satellite data
Tropical cyclones as natural disturbances, influence ecosystem structure, function and dynamics at the global scale. This study assesses the inundation due to the super cyclone Amphan in coastal districts of eastern India by leveraging the computational power of Google Earth Engine (GEE) and the availability of high resolution Sentinel-1 Synthetic Aperture Radar (SAR) data. A cloud-based image processing framework was developed and implemented in GEE for classification using Random Forest algorithm. The inundation areas due to storm surge owing to cyclone Amphan, were mapped and further categorised to different land use and land cover classes based on an existing land cover map. Sentinel-1 images were useful in post-cyclone studies for the change detection analysis due to its higher temporal resolution and cloud penetration ability. The study found that the majority of agricultural and agricultural fallow lands were inundated in the coastal districts. The availability of open-source cloud-based data processing platforms provides cost effective way to rapidly gather accurate geospatial information. Such information could be useful for emergency response planning and post-event disaster management including relief, rescue and rehabilitation measures; and crop yield loss assessment. Cyclone and Land Use and Land Cover (LULC) change can have significant impacts on the human population and if both coexist, the consequences for people and the surrounding environment may be severe.
Cloud computing, Eastern India, Gray level co-occurrence matrix, Land use and land cover, RGB clustering, Random forest
0564-3295
9-19
Behera, Mukunda Dev
6e4169d4-2c20-422c-b2c2-1a15dc41e29d
Prakash, Jaya
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Paramanik, Somnath
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Mudi, Sujoy
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Dash, Jadunandan
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Varghese, Roma
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Roy, Partha Sarathi
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Abhilash, P. C.
a6d7c3fb-b743-43b2-891e-ea84f8ececf7
Gupta, Anil Kumar
f06e7b97-1d55-479f-857f-17c468854709
Srivastava, Prashant Kumar
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Behera, Mukunda Dev
6e4169d4-2c20-422c-b2c2-1a15dc41e29d
Prakash, Jaya
976aad16-cdf1-4a79-bfe3-b193538d641f
Paramanik, Somnath
d71db5f8-af87-4409-9b93-471e65ce7f65
Mudi, Sujoy
c4e392e8-6faa-48db-bc6b-a5c4015e330d
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Varghese, Roma
29f167b1-ca5e-45d5-9aef-eb4539488b07
Roy, Partha Sarathi
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Abhilash, P. C.
a6d7c3fb-b743-43b2-891e-ea84f8ececf7
Gupta, Anil Kumar
f06e7b97-1d55-479f-857f-17c468854709
Srivastava, Prashant Kumar
200160d0-24e8-4969-93f7-66d0a5f100d2

Behera, Mukunda Dev, Prakash, Jaya, Paramanik, Somnath, Mudi, Sujoy, Dash, Jadunandan, Varghese, Roma, Roy, Partha Sarathi, Abhilash, P. C., Gupta, Anil Kumar and Srivastava, Prashant Kumar (2021) Assessment of tropical cyclone amphan affected inundation areas using sentinel-1 satellite data. Tropical Ecology, 63 (1), 9-19. (doi:10.1007/s42965-021-00187-w).

Record type: Article

Abstract

Tropical cyclones as natural disturbances, influence ecosystem structure, function and dynamics at the global scale. This study assesses the inundation due to the super cyclone Amphan in coastal districts of eastern India by leveraging the computational power of Google Earth Engine (GEE) and the availability of high resolution Sentinel-1 Synthetic Aperture Radar (SAR) data. A cloud-based image processing framework was developed and implemented in GEE for classification using Random Forest algorithm. The inundation areas due to storm surge owing to cyclone Amphan, were mapped and further categorised to different land use and land cover classes based on an existing land cover map. Sentinel-1 images were useful in post-cyclone studies for the change detection analysis due to its higher temporal resolution and cloud penetration ability. The study found that the majority of agricultural and agricultural fallow lands were inundated in the coastal districts. The availability of open-source cloud-based data processing platforms provides cost effective way to rapidly gather accurate geospatial information. Such information could be useful for emergency response planning and post-event disaster management including relief, rescue and rehabilitation measures; and crop yield loss assessment. Cyclone and Land Use and Land Cover (LULC) change can have significant impacts on the human population and if both coexist, the consequences for people and the surrounding environment may be severe.

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Accepted/In Press date: 6 July 2021
Published date: 13 July 2021
Additional Information: Funding Information: The Sentinel data from Copernicus program was used in GEE cloud computing platform is duly acknowledged. This paper has been benefitted from Discussion with colleagues from UNOOSA (S Ravan), JAMSTEC (S Behera) and University of Georgia (D Mishra). The lead author acknowledge the support provided by IIT Kharagpur authorities during this study. Publisher Copyright: © 2021, International Society for Tropical Ecology. Copyright: Copyright 2022 Elsevier B.V., All rights reserved.
Keywords: Cloud computing, Eastern India, Gray level co-occurrence matrix, Land use and land cover, RGB clustering, Random forest

Identifiers

Local EPrints ID: 457154
URI: http://eprints.soton.ac.uk/id/eprint/457154
ISSN: 0564-3295
PURE UUID: 19f288e0-db35-4895-992e-eafed7359c83
ORCID for Somnath Paramanik: ORCID iD orcid.org/0000-0002-4509-8801
ORCID for Jadunandan Dash: ORCID iD orcid.org/0000-0002-5444-2109

Catalogue record

Date deposited: 25 May 2022 16:32
Last modified: 28 Feb 2024 02:42

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Contributors

Author: Mukunda Dev Behera
Author: Jaya Prakash
Author: Somnath Paramanik ORCID iD
Author: Sujoy Mudi
Author: Jadunandan Dash ORCID iD
Author: Roma Varghese
Author: Partha Sarathi Roy
Author: P. C. Abhilash
Author: Anil Kumar Gupta
Author: Prashant Kumar Srivastava

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