Flood Hazard Mapping in Assam Using Sentinel-1 SAR Data

Mudi, Sujoy; A, Jaya Prakash; Das, Pulakesh

doi:10.1007/978-3-030-75197-5_20

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…This study used optical and microwave satellite images for ood mapping and validation at a high spatial resolution for the past seven years. The ood inundated area identi ed in different years well corroborates the reported areas in past studies (Pandey et al, 2022;Mudi et al, 2022). The ML algorithms were competent in recognizing the factors in uencing ooding in the study area.…”

Section: Discussionsupporting

confidence: 87%

“…Several methods have been developed for water inundation area mapping using SAR data, such as thresholding, clustering, deep learning method, etc., that discriminates the waterbodies and other land surface features (Mudi et al, 2022;Borah et al, 2018;Konapala et al, 2021;Pandey et al, 2022). Otsu's algorithm has been one of the most robust methods for automated surface water area mapping in the absence of reference observations.…”

Section: Introductionmentioning

confidence: 99%

“…The threshold value is estimated by minimizing the intra-class intensity or inter-class variance. Several studies have employed this algorithm for surface water inundation area mapping and reported high accuracy (Mudi et al, 2022;Prakash et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Development of an Automated Method for Flood Inundation Monitoring, Flood Hazard and Soil Erosion Susceptibility Assessment Using Machine Learning and AHP-MCE Techniques

Prakash

Begum

Vilímek

et al. 2023

Preprint

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Operational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms such as Google Earth Engine (GEE) and robust methodology (Otsu’s algorithm). The current study employs the Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam state, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART) and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. The highest prediction accuracy was observed for the RF model (overall accuracy [OA]: > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Further, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. Over 26% of the study area indicated high flood hazard-prone areas, and about 60% showed a high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as they help to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness of emergency response efforts. The periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, climate resilience studies, etc.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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Development of an Automated Method for Flood Inundation Monitoring, Flood Hazard and Soil Erosion Susceptibility Assessment Using Machine Learning and AHP-MCE Techniques

Prakash

Begum

Vilímek

et al. 2023

Preprint

Self Cite

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“…In contrast, long-term monitoring and periodic mapping can aid in developing suitable policies and readiness (Mudi et al, 2022). Satellite-based ood assessment is a critical input during, before, and after the ood occurrence for determining the extent and severity of the ood (Mishra et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Near real-time flood inundation and hazard mapping of Baitarani River Basin using Google Earth Engine and SAR imagery

Atchyuth,

Swain,

Das

2023

Environ Monit Assess

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Flood Inundation mapping and satellite imagery monitoring are critical and effective responses during ood events. Mapping of a ood using optical data is limited due to the unavailability of cloud-free images. Because of its capacity to penetrate clouds and operate in all kinds of weather, synthetic aperture radar is preferred for water inundation mapping. Flood mapping in Eastern India's Baitarani River Basin for 2018, 2019, 2020, 2021, and 2022 was performed in this study using Sentinel-1 imagery and Google Earth Engine with Otsu's algorithm. Different machine-learning algorithms were used to map the LULC of the study region. Dual polarizations VH and VV and their combinations VV×VH, VV + VH, VH-VV, VV-VH, VV/VH, and VH/VV were examined to identify non-water and water bodies. The Normalized Difference Water Index (NDWI) map derived from Sentinel-2 data validated the surface water inundation with 80% accuracy. The total inundated areas were identi ed as 440.

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