Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Detection of Large-Scale Floods Using Google Earth Engine and Google Colab

Rosa Johary
* ORCID logo ,
Christophe Révillion
ORCID logo ,
Thibault Catry
,
Cyprien Alexandre
,
Pascal Mouquet
,
Solofoarisoa Rakotoniaina
,
Gwenaëlle Pennober
,
Solofo Rakondraompiana
Version 1 : Received: 18 September 2023 / Approved: 19 September 2023 / Online: 20 September 2023 (09:47:41 CEST)

A peer-reviewed article of this Preprint also exists.

Johary, R.; Révillion, C.; Catry, T.; Alexandre, C.; Mouquet, P.; Rakotoniaina, S.; Pennober, G.; Rakotondraompiana, S. Detection of Large-Scale Floods Using Google Earth Engine and Google Colab. Remote Sens. 2023, 15, 5368. Johary, R.; Révillion, C.; Catry, T.; Alexandre, C.; Mouquet, P.; Rakotoniaina, S.; Pennober, G.; Rakotondraompiana, S. Detection of Large-Scale Floods Using Google Earth Engine and Google Colab. Remote Sens. 2023, 15, 5368.

Abstract

This paper presents an operational approach for detecting floods and establishing flood extent using Sentinel-1 radar imagery with Google Earth Engine. Flooded areas are identified using a change-detection method based on the normalized difference. The HAND algorithm is used to delineate zones for processing. The approach was tested and calibrated at small scale to identify optimal parameters for flood detection. It was then applied to the whole of the island of Madagascar after the cyclone Batsirai in 2022. The proposed method is enabled by the computing power and data availability of Google Earth Engine and Google Colab. The results show satisfactory accuracy in delineating flooded areas. The advantages of this approach are its rapidity, online availability and ability to detect floods over a wide area. The approach relying on Google tools thus offers an effective solution for generating a large-scale synoptic picture to inform hazard management decision-making. However, one of the method’s drawbacks is that it depends to a large extent on frequent radar imagery being available at the time of flood events and on free access to the platform. These drawbacks will need to be taken into account in an operational scenario.

Keywords

flood; radar imagery; Sentinel-1; Google Earth Engine; Python

Subject

Environmental and Earth Sciences, Remote Sensing

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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