Influence of hydrological state on trophic state in dam induced seasonally inundated flood plain wetland

Singha, Pankaj; Pal, Swades

doi:10.1016/j.ecohyd.2023.01.001

Cited by 6 publications

(1 citation statement)

References 117 publications

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“…Diverse tools included in GIS allow for the mapping of the degree and complexity of flood hazards and environmental consequences through the classification of satellite images. For instance, for coastal regions in deltaic areas prone to floods, the time series of RS data are useful for visualising the dynamics of the ecological patterns related to flood events and monsoon cycles [10,16,17]. Moreover, the analysis of satellite images covering countries located close to shorelines can help to better understand the effects of climate changes on coastal landscapes.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Lemenkova

2024

Water

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Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world’s largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Lemenkova

2024

Water

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Hydrological alteration and its effect on the eco-hydrological state in Tangon river traversing Bangladesh and India

Pal,

Singha

2023

Environ Dev Sustain

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Economic and socioecological perspectives of urban wetland loss and processes: a study from literatures

Ghosh

Pal

2023

Environ Sci Pollut Res

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Influence of hydrological state on trophic state in dam induced seasonally inundated flood plain wetland

Cited by 6 publications

References 117 publications

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Hydrological alteration and its effect on the eco-hydrological state in Tangon river traversing Bangladesh and India

Economic and socioecological perspectives of urban wetland loss and processes: a study from literatures

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