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Detection and mapping of May 2021 flood in Beaufort, Sabah using Sentinel-1 SAR and Sentinel-2 multispectral in Google Earth Engine

Recurring floods severely impacted the livelihood and socio-economic. It causes disruption of clean water, electricity, communications, properties damages and sometimes loss of life. Information on flooded areas is crucial for effective emergency responses support. In this study we used Sentinel 1 (...

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Main Authors: Stanley Anak Suab, Hitesh Supe, Ram Avtar, Ramzah Dambul, Xinyu Chen
Format: Conference or Workshop Item
Language:English
English
Published: 2022
Subjects:
GB400-649 Geomorphology. Landforms. Terrain
Online Access:https://eprints.ums.edu.my/id/eprint/34597/1/Abstract.pdf
https://eprints.ums.edu.my/id/eprint/34597/2/Full%20text.pdf
https://eprints.ums.edu.my/id/eprint/34597/
https://iopscience.iop.org/article/10.1088/1755-1315/1064/1/012003/meta
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spelling my.ums.eprints.345972022-10-31T02:43:15Z https://eprints.ums.edu.my/id/eprint/34597/ Detection and mapping of May 2021 flood in Beaufort, Sabah using Sentinel-1 SAR and Sentinel-2 multispectral in Google Earth Engine Stanley Anak Suab Hitesh Supe Ram Avtar Ramzah Dambul Xinyu Chen GB400-649 Geomorphology. Landforms. Terrain Recurring floods severely impacted the livelihood and socio-economic. It causes disruption of clean water, electricity, communications, properties damages and sometimes loss of life. Information on flooded areas is crucial for effective emergency responses support. In this study we used Sentinel 1 (S-1) C-band and Sentinel 2 (S-2) Multispectral satellite imageries where wider area covered in 12 days repeat satellite pass. The flood event on the 26 May 2021 was identified and we retrieved the S-1 GRD SAR imagery and S-2 level-2A BOA in GEE environment. We analysed the S-1 VV, VH, VV/VH imagery by pixels clustering using object based SNIC classification and Machine Learning (ML) algorithm for extraction of waterbody. Meanwhile for the S-2 we used MNDWI and extracted the waterbody area using thresholding value. We obtained the final flooded area of S-1 and S-2 by subtraction with permanent waterbody. The S-2 flood estimation results were better than S-1. However, S-2 limited to cloud free and less cloudy coverage while S-1 lacking of ability to identify flood in detailed was influenced by slope shadow area. This study provides the basis of detection and mapping floods using S-1 and S-2 imageries through Machine Learning techniques in GEE for local scope of Sabah, Borneo region and Malaysia. 2022 Conference or Workshop Item PeerReviewed text en https://eprints.ums.edu.my/id/eprint/34597/1/Abstract.pdf text en https://eprints.ums.edu.my/id/eprint/34597/2/Full%20text.pdf Stanley Anak Suab and Hitesh Supe and Ram Avtar and Ramzah Dambul and Xinyu Chen (2022) Detection and mapping of May 2021 flood in Beaufort, Sabah using Sentinel-1 SAR and Sentinel-2 multispectral in Google Earth Engine. In: 11th IGRSM International Conference and Exhibition on Geospatial & Remote Sensing, 7 - 9 March 2022, Virtually. https://iopscience.iop.org/article/10.1088/1755-1315/1064/1/012003/meta
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic GB400-649 Geomorphology. Landforms. Terrain
spellingShingle GB400-649 Geomorphology. Landforms. Terrain
Stanley Anak Suab
Hitesh Supe
Ram Avtar
Ramzah Dambul
Xinyu Chen
Detection and mapping of May 2021 flood in Beaufort, Sabah using Sentinel-1 SAR and Sentinel-2 multispectral in Google Earth Engine
description Recurring floods severely impacted the livelihood and socio-economic. It causes disruption of clean water, electricity, communications, properties damages and sometimes loss of life. Information on flooded areas is crucial for effective emergency responses support. In this study we used Sentinel 1 (S-1) C-band and Sentinel 2 (S-2) Multispectral satellite imageries where wider area covered in 12 days repeat satellite pass. The flood event on the 26 May 2021 was identified and we retrieved the S-1 GRD SAR imagery and S-2 level-2A BOA in GEE environment. We analysed the S-1 VV, VH, VV/VH imagery by pixels clustering using object based SNIC classification and Machine Learning (ML) algorithm for extraction of waterbody. Meanwhile for the S-2 we used MNDWI and extracted the waterbody area using thresholding value. We obtained the final flooded area of S-1 and S-2 by subtraction with permanent waterbody. The S-2 flood estimation results were better than S-1. However, S-2 limited to cloud free and less cloudy coverage while S-1 lacking of ability to identify flood in detailed was influenced by slope shadow area. This study provides the basis of detection and mapping floods using S-1 and S-2 imageries through Machine Learning techniques in GEE for local scope of Sabah, Borneo region and Malaysia.
format Conference or Workshop Item
author Stanley Anak Suab
Hitesh Supe
Ram Avtar
Ramzah Dambul
Xinyu Chen
author_facet Stanley Anak Suab
Hitesh Supe
Ram Avtar
Ramzah Dambul
Xinyu Chen
author_sort Stanley Anak Suab
title Detection and mapping of May 2021 flood in Beaufort, Sabah using Sentinel-1 SAR and Sentinel-2 multispectral in Google Earth Engine
title_short Detection and mapping of May 2021 flood in Beaufort, Sabah using Sentinel-1 SAR and Sentinel-2 multispectral in Google Earth Engine
title_full Detection and mapping of May 2021 flood in Beaufort, Sabah using Sentinel-1 SAR and Sentinel-2 multispectral in Google Earth Engine
title_fullStr Detection and mapping of May 2021 flood in Beaufort, Sabah using Sentinel-1 SAR and Sentinel-2 multispectral in Google Earth Engine
title_full_unstemmed Detection and mapping of May 2021 flood in Beaufort, Sabah using Sentinel-1 SAR and Sentinel-2 multispectral in Google Earth Engine
title_sort detection and mapping of may 2021 flood in beaufort, sabah using sentinel-1 sar and sentinel-2 multispectral in google earth engine
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/34597/1/Abstract.pdf
https://eprints.ums.edu.my/id/eprint/34597/2/Full%20text.pdf
https://eprints.ums.edu.my/id/eprint/34597/
https://iopscience.iop.org/article/10.1088/1755-1315/1064/1/012003/meta
_version_ 1760231316955070464
score 13.211869

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