An assessment of flood event along Lower Niger using Sentinel-1 imagery

Adedeji, Oluwatola; Olusola, Adeyemi; Babamaaji, R. A.; Adelabu, Samuel

doi:10.1007/s10661-021-09647-1

Cited by 8 publications

(3 citation statements)

References 80 publications

(112 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some researchers recognize technical inconveniences of the optical sensors as the revisit time and their limitations due to cloudiness, a typical condition during the rainy season, when there is a greater probability of flooding ( Hernández-Guzmán et al, 2016 ; Adedeji et al., 2021 ; Tarpanelli, Mondini & Camici, 2022 ), suggesting the use of SAR data to elude or reduce them. However, our findings highlight the importance of using Sentinel-2 data for flood mapping compared with the Sentinel-1 data.…”

Section: Discussionmentioning

confidence: 99%

“…Although floods are natural phenomena that occasionally occur in all rivers and drainage systems, they are also a source of major disasters that affect many countries in the world ( Zhang et al., 2020 ; Adedeji et al., 2021 ). Besides damaging lives, natural resources, and the environment, reducing biodiversity and injuring wetland areas, also causes economic losses ( Zhang et al., 2015 ; Bai et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combining multisensor images and social network data to assess the area flooded by a hurricane event

Hernández-Guzmán,

Ruiz-Luna

2024

PeerJ

View full text Add to dashboard Cite

In this study, multisensor remote sensing datasets were used to characterize the land use and land covers (LULC) flooded by Hurricane Willa which made landfall on October 24, 2018. The landscape characterization was done using an unsupervised K-means algorithm of a cloud-free Sentinel-2 MultiSpectral Instrument (MSI) image, acquired during the dry season before Hurricane Willa. A flood map was derived using the histogram thresholding technique over a Synthetic Aperture Radar (SAR) Sentinel-1 C-band and combined with a flood map derived from a Sentinel-2 MSI image. Both, the Sentinel-1 and Sentinel-2 images were obtained after Willa landfall. While the LULC map reached an accuracy of 92%, validated using data collected during field surveys, the flood map achieved 90% overall accuracy, validated using locations extracted from social network data, that were manually georeferenced. The agriculture class was the dominant land use (about 2,624 km2), followed by deciduous forest (1,591 km2) and sub-perennial forest (1,317 km2). About 1,608 km2 represents the permanent wetlands (mangrove, salt marsh, lagoon and estuaries, and littoral classes), but only 489 km2 of this area belongs to aquatic surfaces (lagoons and estuaries). The flooded area was 1,225 km2, with the agricultural class as the most impacted (735 km2). Our analysis detected the saltmarsh class occupied 541 km2in the LULC map, and around 328 km2 were flooded during Hurricane Willa. Since the water flow receded relatively quickly, obtaining representative imagery to assess the flood event was a challenge. Still, the high overall accuracies obtained in this study allow us to assume that the outputs are reliable and can be used in the implementation of effective strategies for the protection, restoration, and management of wetlands. In addition, they will improve the capacity of local governments and residents of Marismas Nacionales to make informed decisions for the protection of vulnerable areas to the different threats derived from climate change.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining multisensor images and social network data to assess the area flooded by a hurricane event

Hernández-Guzmán,

Ruiz-Luna

2024

PeerJ

View full text Add to dashboard Cite

show abstract

“…Several research on incidents of flooding and their implications have been accomplished. Backscattering from microwave sensors offers exceptionally beneficial data for emphasising inundated areas, which could potentially be used in predicting floods, tracking, and precision-based flood anticipation tactics shortly before, during, and following flood events [4].…”

Section: Introductionmentioning

confidence: 99%

Flood Inundation Mapping Using Sentinel 1A Satelite Data in Major Blocks of Mayiladuthurai District, Tamil Nadu, India

Sneka,

Pazhanivelan,

Ragunath

et al. 2023

IJPSS

View full text Add to dashboard Cite

A research study was carried out for mapping flood affected areas from Sentinel 1A satellite data in the major blocks of Mayiladuthurai district, Tamil Nadu. A very high-resolution Sentinel 1A satellite data was acquired and processed with GIS tools. Ground truth gathered during the survey were utilized to identify the dB values for flood affected areas, which were then used to classify the flood pixels in the study regions. The flood affected areas of Mayiladuthurai district was found to be 18002 ha during the year 2022. Accuracy assessment and cross validation was done using confusion matrix with the ground truth points collected. The classification resulted with an overall accuracy of 86.2 % area with a Kappa score of 0.72.

show abstract