A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data

Pedzisai, Ezra; Mutanga, Onisimo; Odindi, John; Bangira, Tsitsi

doi:10.1016/j.heliyon.2023.e13332

Cited by 6 publications

(3 citation statements)

References 62 publications

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“…Therefore, a common complementary approach is to use different SAR and optical datasets together because their combined use provides better results than using individual sensors ( Nhangumbe, Nascetti & Ban, 2023 ). In this regard, the thresholding method to the SAR image combined with the analysis of a multispectral image and the comparison with pre-event imagery (containing information on the permanent water bodies) produced a flood map with a relatively high accuracy (90%) which is comparable to those obtained with complex methods ( Pedzisai et al., 2023 ; Tupas et al., 2023 ).…”

Section: Discussionmentioning

confidence: 94%

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

Hernández-Guzmán,

Ruiz-Luna

2024

PeerJ

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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.

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Section: Discussionmentioning

confidence: 94%

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

Hernández-Guzmán,

Ruiz-Luna

2024

PeerJ

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“…One of the fundamental and essential issues in remote sensing is the interpretation of very-high-resolution (VHR) remote sensing (RS) images. VHR remote sensing image change detection (RSCD) is useful for a variety of remote sensing applications, including urban land use analysis [4], [5], [6], building detection [7], [8], [9], deforestation monitoring [10], [11], urban planning [12], [13], urban sprawl analysis [14], [15], disaster assessment [16], [17], and so on. All of the aforementioned are necessary for local governments to effectively manage their local urban development, and precise and effective RSCD procedures enable cities to be assessed and planned for in order to minimize or prevent adverse effects.…”

Section: Introductionmentioning

confidence: 99%

BD-MSA: Body Decouple VHR Remote Sensing Image Change Detection Method Guided by Multiscale Feature Information Aggregation

Tan,

Li,

Chen

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Two particle search algorithm is established to optimize the RCS index of the vertical tail [ 31 ]. Multi-platform radar detection technology has wide applications in reconnaissance, terrain tasks, meteorological parameters and hydrological monitoring [ [32] , [33] , [34] ]. Cognitive radar systems and cognitive electronic warfare systems have been presented, and floating small targets and magnetic intercalation expansion models have been investigated [ [35] , [36] , [37] ].…”

Section: Introductionmentioning

confidence: 99%

Facilitating learning adaptive V-tail of a supersonic missile for radar cross-section reduction during interval flight

Zhou,

Huang

2023

Heliyon

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A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data

Cited by 6 publications

References 62 publications

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

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

BD-MSA: Body Decouple VHR Remote Sensing Image Change Detection Method Guided by Multiscale Feature Information Aggregation

Facilitating learning adaptive V-tail of a supersonic missile for radar cross-section reduction during interval flight

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