Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study on Brahmaputra River in Assam State, India

Surampudi, Samvedya; Yarrakula, Kiran

doi:10.1007/s11356-019-06849-6

Cited by 24 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The monsoon hits India in June and lasts till October, and such incessant rainfall causes an overflow in major Himalayan Rivers (Kale, 2003). The increased river discharge increases the water level in the reservoirs, rivers, and other water bodies beyond the permissible limit, causing frequent floods in the Himalayan river basins (Surampudi & Yarrakula, 2020). The lower Brahmaputra River Basin receives high rainfall and is frequently affected by large-magnitude floods (Dhar & Nandargi, 2000;Panda et al, 2020).…”

mentioning

confidence: 99%

Characterization and assessment of flood inundated areas of lower Brahmaputra River Basin using multitemporal Synthetic Aperture Radar data: A case study from NE India

et al. 2021

View full text Add to dashboard Cite

Floods are known to be one of the greatest disasters in documented human history. In the Indian subcontinent, the Ganga-Brahmaputra plains annually accommodate monsoon-driven flooding of the Brahmaputra River and its tributaries, causing widespread flood inundation and geomorphic changes. In the present study, we present flood inundation maps of Assam and suggest a possible geomorphic explanation for the frequent flood inundation areas. We have used a novel SAR (Synthetic Aperture Radar)based approach for a comprehensive flood inundation mapping of Assam. The Google Earth Engine (GEE), a cloud-based data computing and analysis platform, is used to access and analyse the Sentinel SAR 1 GRD datasets for 30 events from 2018 to 2020.The Sentinel-2 imagery, WWF Hydrosheds, 90 m DEM (SRTM), and JRC Global Surface Water Dataset are also used to conduct the flood inundation mapping for 80,236(1 Â 1 km) grids in Assam and morphometric analysis in the lower Brahmaputra River Basin. We also carried out detailed mapping and geomorphic analysis on channel and sediment bar area and channel width in 100 (6.25 km wide) grids on the Brahmaputra River in Assam. Our results suggest that high flood inundation areas are well scattered in Assam, and Lakhimpur, Nagaon, Sibsagar, Sonitpur, and Barpeta are the most affected districts from 2018 to 2020. We also observe that the geomorphology can act as primary control over the frequent flood inundation in highlighted areas in Assam. On a basin scale, we recognize a low correlation between the stream power index, topographic wetness index, plan curvature, slope, and flood inundated areas, mainly due to the coarse resolution of the digital elevation model. In a braided river system like the Brahmaputra River, the channel and bar area and channel width are well correlated during flood events. Finally, the channel sinuosity also controls channel and bar area and width during regular and influential flood events.

show abstract

mentioning

confidence: 99%

Characterization and assessment of flood inundated areas of lower Brahmaputra River Basin using multitemporal Synthetic Aperture Radar data: A case study from NE India

et al. 2021

View full text Add to dashboard Cite

show abstract

“…On the other hand, field surveys provide highly accurate, first-hand data. However, the process is both time-consuming and labor-intensive (Surampudi and Yarrakula, 2020;Feng et al, 2022), making it challenging to support the collection of historical flood events on a large scale.…”

Section: Introductionmentioning

confidence: 99%

Extracting Spatiotemporal Flood Information from News Texts Using Machine Learning for a National Dataset in China

Fu,

Schultz,

Lyu

et al. 2024

Preprint

View full text Add to dashboard Cite

Abstract. Urban floods present a threat in China, demanding an understanding of their spatiotemporal distribution. Current flood datasets primarily offer provincial-scale insights and lack temporal continuity, which leads to a challenge in detailed analysis. To create a consistent national dataset of flood events, this study introduces a machine learning framework by applying online news media as a primary data source to construct a county-level dataset of urban flood events from 2000 to 2022. Using the Bidirectional Encoder Representations from Transformers (BERT) model, we achieved robust performance in information extraction, with an F1 score of 0.86 and an exact match score of 0.82. Further, a combined model of Bidirectional Long Short-term Memory (BiLSTM) networks with a Conditional Random Field (CRF) layer effectively identified flood locations. Our analysis reveals that the temporal trend of flooded cities in the news-based dataset is similar to the China Flood and Drought Bulletin. Furthermore, the consistency of flood events in the news with the typhoon trajectory in two cases, and the connection between flood occurrences and flood conditioning factors, confirm the accuracy of spatial distribution. The validated news-based dataset analyzes urban floods in China from both temporal and spatial perspectives. First, this dataset shows the seasonal characteristics of flood events, which are concentrated in the summer. From 2000 to 2022, the peak year for floods was 2010, and excluding the influence of peak year, the overall temporal trend of total flood occurrence shows an increase. Spatially, the distribution of floods decreases from southeast to northwest, with Guangxi Province having the highest number of floods. Additionally, the Yangtze and Pearl River basins are most frequently affected by urban floods. The subtropical climate zone is the most susceptible to flooding. This study provides an automated and effective method for constructing a national flood event dataset and reveals the spatiotemporal characteristics of urban flooding in China.

show abstract

“…While, the remotely-sensed derivative of flood inundation area by Synthetic Aperture Radar (SAR) has been widely used, but, this issue in urban areas is ongoing challenging (Li et al, 2019). In particular, radar satellite images have the potential to monitor flood-inundated areas compared to optical images (Li et al, 2018;Surampudi & Yarrakula, 2020;Tanim et al, 2022). Polarimetric radar images from Sentinel-1, an active remote sensing satellite that produces intensities of backscattered signals from the Earth's surface (DeVries et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Mapping of Urban Flood Inundation Using 3d Digital Surface Model and Sentinel-1 Images

Sharif

Heidari²,

Hosseini³

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Flooding in urban areas poses serious risks to citizens, infrastructures, and transportation. Precise and real-time delineation of the inundated areas is crucial for a better understanding of the extent of damage and high-risk areas and people evacuation actions. It also increases citizens' awareness that living in areas with high flood risk. Yazd city is characterized by low rainfall (<70 mm/yr) and the desert climate is considered the study area of this research. This city encountered a flash flood event that was generated by severe rainfall with a depth of 75 mm in 3hr (i.e., the intensity of 25 mm/hr) on July 29, 2022. Many strategic infrastructures of this city especially the railway station were flooded, which caused heavy casualties and financial losses. This study aims to monitor the flood inundated areas of Yazd city due to this flood event using remote sensing. In this research, the Sentinel-1 polarimetric radar images and the 3D model of the Yazd city surf ace were used to delineate the flooded areas. The field information of the flooded areas and the available Sentinel-1 images during or near the occurrence time of maximum flood extension were adopted. The Convolutional Neural Network (CNN) model in combination with the 3D model of the studied area was used to identify the flooded pixels in the city of Yazd. The results showed that the adopted 3D model and CNN algorithm indicated a good ability to identify flooded areas with an accuracy of 88% and a kappa coefficient of 0.83.

show abstract

Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study on Brahmaputra River in Assam State, India

Cited by 24 publications

References 25 publications

Characterization and assessment of flood inundated areas of lower Brahmaputra River Basin using multitemporal Synthetic Aperture Radar data: A case study from NE India

Characterization and assessment of flood inundated areas of lower Brahmaputra River Basin using multitemporal Synthetic Aperture Radar data: A case study from NE India

Extracting Spatiotemporal Flood Information from News Texts Using Machine Learning for a National Dataset in China

Mapping of Urban Flood Inundation Using 3d Digital Surface Model and Sentinel-1 Images

Contact Info

Product

Resources

About