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Flood impact assessment on agricultural and municipal areas using Sentinel-1 and 2 satellite images (case study: Kermanshah province)

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Abstract

Flooding stands as one of the most devastating natural occurrences, warranting immediate investigation to mitigate its destructive impact. The inundation of agricultural lands and settlements has led to adverse consequences. Remote sensing emerges as a widely applicable and expeditious method for addressing these challenges. Within the scope of this study, S1A SAR data with VH descending pass and S2 data from 01/03/2019 to 20/03/2019 and 25/03/2019 to 20/04/2019 were leveraged to assess the pre- and post-flood periods in Kermanshah province. MNDWI and NDWI techniques were employed to identify water zones in the S2 imagery, subsequently was used for validating of S1 images. The calculated RMSE and correlation coefficients yielded values of 0.27 and 0.93, respectively. It was observed that radar imagery exhibits superior quality to optical imagery in flood scenarios characterized by cloudy and rainy weather. MODIS, Hydrosheds, and SRTM satellite images were utilized as distinct filters to identify land use, permanent water bodies, and areas with a slope exceeding 5%. The findings indicated that a total of 36,849 ha of land were affected by the flood, encompassing 7073 and 4224 ha of agricultural and urban areas, respectively, which were susceptible to destruction during this period. The NDWI and MNDWI indices estimated the flooded area to be 30,179 and 32,540 ha, respectively, representing lower values compared to the results obtained from the S1 data.

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Data availability

The S1 and S2 data from the real case were taken from the GEE platform (https://code.earthengine.google.com/) and are available in https://github.com/maryamhafezparast/Google-Earth-Engin.

Abbreviations

AWEI:

Automated Water Extraction Index

CD:

Change detection

CM:

Centimeters

EO:

Earth Observation

ESA:

European Space Agency

FMA:

Flood Mapping Algorithm

GEE:

Google Earth Engine

GFM:

Global Flood Mapper

GRD:

Ground Range Detected

HA:

Hectare

IHS:

Intensity Hue and Saturation

IW:

Interferometry Wide

LEE:

Local Statistic Lee Filter

MNDWI:

Modified Normalized Difference Water Index

NDWI:

Normalized Difference Water Index

NDVI:

Normalized Difference Vegetation Index

NCI:

Normalized Change Index

NDR:

Normalized Difference Ratio

NIR:

Near Infrared

OLI:

Operational Land Imager

PCA:

Principal Component Analysis

RF:

Random Forest

RI:

Ratio Index

RMSE:

Root Mean Square Error

RS:

Remote Sensing

S1:

Sentinel-1

S2:

Sentinel-2

SAR:

Synthetic aperture radar

SBAS:

Small Baseline Subsets

S1TBX:

Sentinel Application Platform for Sentinel-1 Toolbox

SRTM:

Shuttle Radar Topography Mission

SVM:

Support Vector Machine

SVR:

Support Vector Regression

SWIR:

Short-Wave Infrared Red

VH:

Vertical Receive, Horizontal Transmit SAR Polarization

VV:

Vertical transmit, Vertical receive SAR polarization

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Acknowledgements

Thanks to ESA's Copernicus program for making high-resolution (10m) satellite images freely available to the public.

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The authors received no financial support for the research, authorship, and publication of this article.

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Authors and Affiliations

  1. Department of Water Engineering, Razi University, Kermanshah, Iran

    Sadaf Gord, Maryam Hafezparast Mavaddat & Rasool Ghobadian

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  1. Sadaf Gord
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Contributions

This manuscript is the result of the research of S.G under the supervision of M.H. and the Advising of R.Gh. All authors designed the study, developed the methodology, discussed the results and wrote the paper.

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Correspondence to Maryam Hafezparast Mavaddat.

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Gord, S., Hafezparast Mavaddat, M. & Ghobadian, R. Flood impact assessment on agricultural and municipal areas using Sentinel-1 and 2 satellite images (case study: Kermanshah province). Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06514-3

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