A change detection approach to flood inundation mapping using multi-temporal Sentinel-1 SAR images, the Brahmaputra River, Assam (India): 2015–2020

Vekaria, Darshil; Chander, Shard; Singh, Ronak; Dixit, Sudhanshu

doi:10.1007/s12040-022-02020-x

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…Zhengzhou, in China's Henan Province, experienced flooding on 20 July 2021 resulting from heavy rainfall. Flood disasters caused 398 deaths, mountain floods, landslides, serious urban waterlogging, and significant damage to subways and underground spaces (Vekaria et al., 2022; Zhao et al., 2023). Moreover, the ZZF disaster was characterized by a wide range of impacts, a large affected area, and evident flood features, which facilitated CD and flood mapping (Peter et al., 2020).…”

Section: Data Setmentioning

confidence: 99%

D‐CDA: A Denoise and Change Detection Approach for Flood Disaster Location From SAR Images

Xie,

Yang,

Zhang

et al. 2024

Radio Science

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Floods are among the most devastating natural disasters worldwide. Such disasters are often accompanied by strong precipitation and other weather factors, making it more difficult to identify affected areas. Moreover, synthetic aperture radar (SAR) technology can capture images in a 24‐hr window and penetrate clouds and fog. Change detection (CD) technology based on SAR images is generally utilized to locate disaster‐stricken areas by analyzing the differences between pre‐ and post‐disaster images. However, this method faces two main challenges: the presence of speckle noise, which reduces the difference detection accuracy, and the lack of a suitable SAR data set for flood disaster CD. Therefore, this study proposes a novel two‐stage approach for locating flood disaster areas, known as the denoising‐change detection approach (D‐CDA). The first stage comprises a nine‐layer denoising network with an encoder‐decoder structure known as the SAR denoising network (SDNet). It utilizes a multiresidual block and a parallel convolutional block attention module to extract features during the encoding process to suppress the noise component. In the second stage, a novel convolution neural network is proposed to detect the changes between bitemporal SAR images, namely, the coordinate attention fused network, which combines the siamese network and UNet++ as the backbone, and fuses coordinate attention modules to enhance the change features. Moreover, a CD data set (Zhengzhou flood data set) was constructed using Sentinel‐1 SAR images based on the 2021 flood disaster in Zhengzhou, China. Simulations verify the effectiveness of the proposed method. The experimental results indicate that D‐CDA achieves favorable detection performance in locating flood disaster areas.

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Section: Data Setmentioning

confidence: 99%

D‐CDA: A Denoise and Change Detection Approach for Flood Disaster Location From SAR Images

Xie,

Yang,

Zhang

et al. 2024

Radio Science

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“…Vekaria et al. (2022) used multi‐temporal Sentinel‐1 SAR images to detect floods in the Brahmaputra River, Assam (India) (Vekaria et al., 2022), and Tripathi et al. (2020) mapped flood inundation using multi‐temporal optical and SAR satellite data in Darbhanga District, Bihar, India (Tripathi et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

SatVITS‐Flood: Satellite Vegetation Index Time Series Flood Detection Model for Hyperarid Regions

Burstein,

Grodek,

Enzel

et al. 2023

Water Resources Research

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We present the satellite vegetation index time series model for detecting historical floods in ungauged hyperarid regions (SatVITS‐Flood). SatVITS‐Flood is based on observations that floods are the primary cause of local vegetation expansion in hyperarid regions. To detect such expansion, we used two time‐series metrics: (a) trend change detection from the Breaks For Additive Season and Trend and (b) a newly developed seasonal change metric based on Temporal Fourier Analysis (TFA) and the growing‐season integral anomaly (TFA‐GSIanom). The two metrics complement each other by capturing changes in perennial plant species following extreme, rare floods and ephemeral vegetation changes following more frequent floods. Metrics were derived from the time series of the normalized difference vegetation index, the modified soil‐adjusted vegetation index, and the normalized difference water index, acquired from MODIS, Landsat, and Advanced Very High‐Resolution Radiometer. The timing of the change was compared with the date of the flood and the magnitude of change with its volume and duration. We tested SatVITS‐Flood in three regions on different continents with 40‐year‐long, systematic, reliable gauge data. Our results indicate that SatVITS‐Flood can predict flood occurrence with an accuracy of 78% and precision of 67% (Recall = 0.69 and F1 = 0.68; p < 0.01), and the flood volume and duration with NSE of 0.79 (RMSE = 15.4 × 106 m3 event−1), and R2 of 0.69 (RMSE = 5.7 days), respectively. SatVITS‐Flood proved useful for detecting historical floods and may provide valuable long‐term hydrological information in poorly documented areas, which can help understand the impacts of climate change on the hydrology of hyperarid regions.

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“…For example, Dao et al (2019) used a MODIS-Landsat image fusion with object-based image analysis to detect flood inundation in a heterogeneous vegetated area (Dao et al, 2019). Vekaria et al (2022) used multi-temporal Sentinel-1 SAR images to detect floods in the Brahmaputra River, Assam (India) (Vekaria et al, 2022), andTripathi et al (2020) mapped flood inundation using multi-temporal optical and SAR satellite data in Darbhanga District, Bihar, India (Tripathi et al, 2020 2021) used stacking hybrid machine-learning algorithms with radar and optical satellite data to detect floods in Bangladesh (Rahman et al, 2021), while Shahabi et al (2020) used data from Sentinel-1 in ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to forecast flooding in a watershed in northern Iran (Shahabi et al, 2020).…”

mentioning

confidence: 99%

SatVITS-Flood: Satellite Vegetation Index Time Series Flood detection model for hyperarid regions

Burstein¹,

Grodek

Enzel

et al. 2023

Preprint

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We present the Satellite Vegetation Index Time Series model for detecting historical floods in ungauged hyperarid regions (SatVITS-Flood). SatVITS-Flood is based on observations that floods are the primary cause of local vegetation expansion in hyperarid regions. To detect such expansion, we used two time series metrics: (1) trend change detection from the Breaks For Additive Season and Trend (BFAST-trend) and (2) a newly developed seasonal change metric based on Temporal Fourier Analysis (TFA) and the growing-season integral anomaly (TFA-GSIanom). The two metrics complement each other by capturing changes in perennial species following extreme, rare floods and ephemeral vegetation changes following more frequent floods. Metrics were derived from the time series of the normalized difference vegetation index (NDVI), the modified soil-adjusted vegetation index (MSAVI), and the normalized difference water index (NDWI), acquired from MODIS, Landsat, and AVHRR. The timing of the change was compared with the date of the flood and the magnitude of change with its volume and duration. We tested SatVITS-Flood in three regions on different continents with 40 years long, systematic, reliable gauge data. Our results indicate that SatVITS-Flood can predict flood occurrence with an accuracy of 78% and precision of 67% (Recall=0.69 and F1=0.68; p<0.01), and the flood volume and duration with NSE of 0.79 (RMSE=15.4 Mm3 event–1), and R2 of 0.69 (RMSE=5.7 days), respectively. SatVITS-Flood proved useful for detecting historical floods and may provide valuable long-term hydrological information in poorly-documented areas, which can help understand the impacts of climate change on the hydrology of hyperarid regions.

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A change detection approach to flood inundation mapping using multi-temporal Sentinel-1 SAR images, the Brahmaputra River, Assam (India): 2015–2020

Cited by 6 publications

References 26 publications

D‐CDA: A Denoise and Change Detection Approach for Flood Disaster Location From SAR Images

D‐CDA: A Denoise and Change Detection Approach for Flood Disaster Location From SAR Images

SatVITS‐Flood: Satellite Vegetation Index Time Series Flood Detection Model for Hyperarid Regions

SatVITS-Flood: Satellite Vegetation Index Time Series Flood detection model for hyperarid regions

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