ArticlePublisher preview available

Monitoring the tropical cyclone ‘Yass’ and ‘Amphan’ affected flood inundation using Sentinel‑1/2 data and Google Earth Engine

September 2022
Modeling Earth Systems and Environment

DOI:10.1007/s40808-022-01359-w

Project: Coastal environmental stress and vulnerability assessment

Authors:

Bijay Halder

Vidyasagar University

Jatisankar Bandyopadhyay

Vidyasagar University

The tropical cyclone is infuenced the natural environmental conditions, biodiversity, mangrove forest degradation; salinity increased, and flood inundation at the global landscape and frequently in the coastal regions. Flood inundation is increase due to global sea-level rise and shoreline shifting. In India, several tropical cyclones are infuenced by the natural environ- ment, like Fani, Bulbul, Amphan, and Yass. This study is to identify the flood inundation and land use land cover (LULC) affected area due to tropical cyclone Amphan and Yass. Cloud computing-based Google Earth Engine (GEE) platform was used for the development of flood inundation area calculation in affected Indian coast. Random forest algorithm was used for the LULC classifcation and Sentinel-1 Synthetic Aperture Rader (SAR) of pre and post-cyclone was used for mapping and monitoring the cyclonic effect. The tropical cyclone Amphan and Yass have infuenced the agricultural land and mangrove forest areas, like Sundarban and Bhitarkanika, and massive economic losses. Saltwater intrusion in the coastal area has been increased the salinity and frequently those types of cyclonic activities are triggering the coastal vulnerability. Total 11,405.21 sq. km of West Bengal and 10,437.58 sq. km Odisha coastal regions are inundated due to Yass and 22,735.29 sq. km areas are flooded due to Amphan cyclone. Mostly agricultural lands are affected because most of the areas are covered by agricultural land. This study results could be useful for the post-cyclone emergency response, coastal planners, and administrators for sustainable development of people life.

Cyclonic activities in South-East coast of India. The fore maps were denoted the cyclonic scenarios of Amphan and Yass cyclone. a Amphan intensity and water vapour concentration in cyclonic clear eye (Brown) were occurring from MODIS Aqua; b Amphan cyclone is infrared visibility in West Bengal and Odisha coast; c Yass intensity and water vapour concentration in cyclonic clear eye (Brown) occurring from MODIS Aqua; and d Yass cyclone is infrared visibility in West Bengal and Odisha coast

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Study area map of affected Indian coast

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Entire methodology for generation the flood inundation map due to Amphan and

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Backscatter images of Yass cyclone, Pre and post cyclone images are 1st and 2nd map. The 3rd map is RGB colour map, which is indicated the flood inundation area

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Flood inundation ma generation using Google Earth Engine (GEE); a flood inundation due to cyclone Amphan; b flood inundation due to Yass cyclone in Indian coast

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Modeling Earth Systems and Environment (2022) 8:4317–4332

https://doi.org/10.1007/s40808-022-01359-w

ORIGINAL ARTICLE

Monitoring thetropical cyclone ‘Yass’ and‘Amphan’ aﬀected ﬂood

inundation using Sentinel‑1/2 data andGoogle Earth Engine

BijayHalder1 · JatisankarBandyopadhyay1

Received: 9 October 2021 / Accepted: 31 January 2022 / Published online: 5 March 2022

Abstract

The tropical cyclone is inﬂuenced the natural environmental conditions, biodiversity, mangrove forest degradation; salinity

increased, and ﬂood inundation at the global landscape and frequently in the coastal regions. Flood inundation is increase

due to global sea-level rise and shoreline shifting. In India, several tropical cyclones are inﬂuenced by the natural environ-

ment, like Fani, Bulbul, Amphan, and Yass. This study is to identify the ﬂood inundation and land use land cover (LULC)

aﬀected area due to tropical cyclone Amphan and Yass. Cloud computing-based Google Earth Engine (GEE) platform was

used for the development of ﬂood inundation area calculation in aﬀected Indian coast. Random forest algorithm was used for

the LULC classiﬁcation and Sentinel-1 Synthetic Aperture Rader (SAR) of pre and post-cyclone was used for mapping and

monitoring the cyclonic eﬀect. The tropical cyclone Amphan and Yass have inﬂuenced the agricultural land and mangrove

forest areas, like Sundarban and Bhitarkanika, and massive economic losses. Saltwater intrusion in the coastal area has been

increased the salinity and frequently those types of cyclonic activities are triggering the coastal vulnerability. Total 11,405.21

sq. km of West Bengal and 10,437.58 sq. km Odisha coastal regions are inundated due to Yass and 22,735.29 sq. km areas are

ﬂooded due to Amphan cyclone. Mostly agricultural lands are aﬀected because most of the areas are covered by agricultural

land. This study results could be useful for the post-cyclone emergency response, coastal planners, and administrators for

sustainable development of people life.

Keywords Flood inundation· Tropical cyclone· Cloud computing· Land transformation· RGB clustering

Introduction

Cyclonic activities are increased in the tropical region in

some last decades. Due to climate change, sea-level rise and

temperature variation ﬂood inundation is a common phe-

nomenon in the coastal regions (Sadat-Noori etal. 2021).

Saltwater intrusion, soil fertility decreased, reducing agri-

cultural productivities, life losses, and massive economic

disasters are notiﬁed during and after the cyclone. Many

coastal and near-coastal countries are aﬀected by cyclonic

storms, ﬂood inundation, deforestation, and soil salinity

increased (Guzman and Jiang 2021). Temperature variation

and massive development of water, tidal eﬀect, and activi-

ties are increased in the near-shore areas. Also, the global

sea-level rise is prompting cyclone-related activities in the

coastal regions. A tropical cyclone is occurring due to cli-

mate change conditions and water pressure in the sea area.

The cyclonic storm, ﬂood inundation, and massive vegeta-

tion losses are noticed in the tropical regions (Xu and Zhao

2021; Nadimpalli etal. 2021). Indirectly anthropogenic

activities are also triggering the cyclonic scenarios in the

coastal regions.

The cyclonic activities are increased the coastal geo-

morphological change, shoreline shifting, mangrove deg-

radation, ecological imbalance and saltwater intrusion.

Monitoring the disaster-related information and natural

environmental degradation is an important part of coastal

development and planning (Carreño Conde and De Mata

Muñoz 2019; Kwak 2017; Rahman and Thakur 2018). The

ﬂooded area monitoring and identifying the natural haz-

ards related phenomenon are more time consuming and

diﬃcult for identifying the ﬂood inundated area. Satellite

imageries, ground truth data, and skilled persons are need

for this study (Rahman and Thakur 2018). The mangrove

area is more valuable land cover for human life but that

* Bijay Halder

halder06bijay@gmail.com

1 Department ofRemote Sensing andGIS, Vidyasagar

University, Midnapore, India

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

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Feb 2021
COMPUT GEOSCI-UK

The availability of advanced Machine Learning algorithms has made the estimation process of biophysical parameters more efficient. However, the efficiency of those methods seldom compared with the efficiency of already established semi-empirical procedures. Aboveground biomass (AGB) of mangrove forests is a crucial biophysical parameter as it is positively correlated to the carbon stocks and fluxes. The free availability of Sentinel-1 C-band SAR data and machine learning algorithms hold promises in estimating AGB of tropical mangrove forests. We reported high AGB (70 t/ha to 666 t/ha) using 185 field quadrats of 0.04ha each from Bhitarkanika Wildlife Sanctuary, located on the eastern Indian coast that could be attributed to species composition. The AGB maps generated using Interferometric Water Cloud Model (IWCM) and Deep Learning models were different from each other as they rely on different variables. IWCM was more dependent, especially on ground and vegetation components of coherence, while canopy height acted as the most crucial variable in the Deep Learning model. However, the negligible variations in Deep Learning-based AGB maps can be attributed to interpreting the importance of coherence and VH backscatter. Due to low canopy penetration power of C-band SAR, high temporal decorrelation resulting from longer time gap between interferometric image pairs, and high spatial heterogeneity of mangrove forests, IWCM found as an unsuitable method for AGB estimation. Interestingly, a Deep Learning algorithm could translate the exact relationship between predictor variables and mangrove AGB in Bhitarkanika Wildlife Sanctuary. The AGB estimation studies in mangrove forests using Sentinel data should focus more on using machine learning algorithms like Deep Learning rather than semi-empirical models.