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image of Flood Mapping and Damage Analysis Using Multispectral Sentinel-2 Satellite Imagery and Machine Learning Techniques

Abstract

Background

Floods are among the deadliest natural calamities, devastating ecosystems and human lives worldwide. In India, Bihar is a state grappling with economic hardships and faces severe agricultural devastation due to recurring floods, destroying crops and natural resources, which significantly impacts local farmers. This research addresses the critical need to deeply understand the flood dynamics of selected study areas.

Objective

This research presents a case study that focuses on leveraging Remote Sensing tools and Machine Learning techniques for comprehensive flood mapping and damage analysis in Gopalganj District, Bihar, India, using remote sensing data. More specifically, this research presents three major objectives: (i) Flood damage mapping and change analysis before and after the flood using the Sentinel-2 satellite dataset, (ii) Evaluation of the impact of integrating spectral indices on the accuracy of classification, (iii) Identification of most robust predictor spectral indices for the classification.

Methods

The Sentinel-2 satellite dataset encompasses 13 bands with resolutions of 10m, 20m, and 60m. Here, four spectral bands (NIR, Red, Green, and Blue) with the finest resolution of 10m have been selected for this study. These bands are integrated with four spectral indices, namely Normalized Difference Water Index (NDWI), MNDWI (Modified NDWI), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI). Two ML classifiers, namely Support Vector Machine (SVM) and Random Forest (RF) have been employed for pixel-based supervised classification.

Results

Results have shown that RF outperformed and worked well in extracting water bodies and flood-damaged areas effectively. The results demonstrated that RF obtained (Overall Accuracy (OA)= 89.54% and kappa value () = 0.872) and SVM reported (OA= 87.69%, = 0.849) for pre-crisis data, whereas, for post-crisis, RF reported (OA=91.54%, = 0.897), SVM reported (OA= 89.77%, = 0.875).

Conclusion

It was reported that the integration of spectral indices improved the OA by +3.41% and +2.86% using RF and SVM, respectively. The results of this study demonstrated that the waterbody area increased from 12.72 to 88.23 km2, as shown by the RF classifier. The variable importance computation results indicated that MNDWI is the most important predictor variable, followed by NDWI. This study recommends the use of these two predictor variables for flood mapping.

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2024-07-15
2025-01-13

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Flood Mapping and Damage Analysis Using Multispectral Sentinel-2 Satellite Imagery and Machine Learning Techniques
Bentham Science Publishers ; https://doi.org/10.2174/0126662558309143240529104953
/content/journals/rascs/10.2174/0126662558309143240529104953
/content/journals/rascs/10.2174/0126662558309143240529104953
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  • Article Type:     Research Article
Keywords: MNDWI ; RF ; SVM ; NDWI ; Flood mapping ; sentinel-2 ; machine learning ; remote sensing

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