Semi-automated Workflow for Mapping the Extent and Elevation Profile of Intertidal Zone of Parts of Gulf of Kutch, India, Using Landsat Time Series Data

Sharma, Supriya; Paul, Arnab; Mitra, Debashis; Chauhan, Prakash

doi:10.1007/s12524-020-01291-5

Cited by 10 publications

(2 citation statements)

References 86 publications

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“…Due to the abundant supply of sediment from the Yellow River and Yangtze River estuaries, the sediment source in this region is rich. As a result, the intertidal zone exhibits significant and sustained geomorphic changes, characterized by pronounced fluctuations and evident erosion and deposition trends [47], [48], [49]. The selection of this area was based on the fact that the coastal zone of Jiangsu exhibiting a transition regime from accretion to erosion, caused by the decreasing supply of sediment from the abandoned Yellow River over the years.…”

Section: A Study Areamentioning

confidence: 99%

Near Real-Time Monitoring of Muddy Intertidal Zones Based on Spatiotemporal Fusion of Optical Satellites Data

Gu,

Chen,

Chen

et al. 2024

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

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The investigation of the evolution of intertidal zones is a significant and extensively discussed subject in estuary and coastal research, both nationally and internationally. Recent advances in real-time global satellite products, such as GOCI, provide a potential approach for monitoring intertidal zones by utilizing waterline inversion based on spatial distribution processes. While these products offer potential for monitoring intertidal zones through waterline inversion based on spatial distribution processes, they often suffer from coarse spatial resolution that smooths critical spatial heterogeneity. To overcome this limitation, a flexible spatiotemporal fusion model was employed to generate hourly time-series images with a spatial resolution of 10 meters. This was achieved by combining Sentinel-2 satellite data (10m spatial resolution; 5-day revisit frequency) and GOCI-II data (500 m spatial resolution; 1-hour revisit frequency). The resulting fusion images were used to construct an intertidal pseudo-digital elevation model (DEM) by extracting waterlines and incorporating tidal level information. The accuracy of the DEM was validated using surveyed real-time kinematic (RTK) and drone data, with a root mean square error of 0.28m. Analysis of the annual accretion and erosion evolution in the intertidal zone for the year 2023 revealed significant erosion in the central part of the zone, with a maximum erosion depth of 1m at the bottom. This study contributes to the understanding of the response processes and mechanisms of the intertidal zone to natural and human disturbances, thus supporting coastal planning projects related to the intertidal zone.

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Section: A Study Areamentioning

confidence: 99%

Near Real-Time Monitoring of Muddy Intertidal Zones Based on Spatiotemporal Fusion of Optical Satellites Data

Gu,

Chen,

Chen

et al. 2024

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

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“…The former usually attributes tidal height to every remote sensing image, and then nd out two remote sensing images corresponding to the lowest and highest tide respectively, nally, tidal wetlands between the lowest and highest tide are considered as the real extent of tidal wetlands. Based on this method, some studies were conducted, for example, (Yan et al, 2021) used tidal height data to determine waterlines corresponding to the lowest and highest tide respectively and then used determined waterlines to modify the extent of tidal wetlands extracted from machine learning algorithms; (Sharma et al, 2021) sequenced remote sensing images according to tidal height data and then extract the tidal wetland by setting threshold; (Murray et al, 2014) selected Landsat images within 10% of high tide and low tide according to tide height data, and used the selected Landsat images to extract tidal wetlands in the Yellow Sea.…”

Section: Introductionmentioning

confidence: 99%

Extraction of tidal wetland from remote sensing image based on spatio-temporal data fusion algorithm

Wang

Jiang

Gao

2022

Preprint

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Due to the influence of the temporal resolution and clouds on remote sensing images, it is difficult for traditional remote sensing based methods to obtain remote sensing image corresponding to the lowest tide, which can result in an inaccurate extraction of tidal wetlands from remote sensing images. To deal with this deficiency, in this study, we proposed to extract tidal wetlands by using spatio-temporal data fusion algorithm. And then we compared the performance of three typical spatio-temporal data fusion algorithms —— Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion model (FSDAF) and GAN-based Spatio-Temporal Fusion Model (GANSTFM) —— in the extraction of tidal wetlands. Finally, the most suitable spatio-temporal data fusion algorithm in extracting large-scale and long time series tidal wetlands was selected. Our study had three main results: (1) Fusion images reflected the spatial distribution of tidal wetlands well; GANSTFM was the best and FSDAF algorithm ranked the second in extracting tidal wetlands. (2) The tidal wetland extraction results from different fusion images were in good agreement with the reference result, and the overall accuracies of extraction results were greater than 94%. (3) Considering the calculation amount, required amount of input data and the accuracy of the result, we selected FSDAF algorithm as the most suitable spatio-temporal data fusion algorithm used to extract large-scale and long time series tidal wetlands. The proposed method can sever as a reliable method to conduct extraction of large-scale and long time series tidal wetlands.

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Temporal Investigation of Chlorophyll-A in the Bhimgoda Barrage and Wetland Landscape Using Remote Sensing and GIS

Khali,

Mishra,

Yadav

et al. 2024

Natural Resource Monitoring, Planning and Management Based on Advanced Programming

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Semi-automated Workflow for Mapping the Extent and Elevation Profile of Intertidal Zone of Parts of Gulf of Kutch, India, Using Landsat Time Series Data

Cited by 10 publications

References 86 publications

Near Real-Time Monitoring of Muddy Intertidal Zones Based on Spatiotemporal Fusion of Optical Satellites Data

Near Real-Time Monitoring of Muddy Intertidal Zones Based on Spatiotemporal Fusion of Optical Satellites Data

Extraction of tidal wetland from remote sensing image based on spatio-temporal data fusion algorithm

Temporal Investigation of Chlorophyll-A in the Bhimgoda Barrage and Wetland Landscape Using Remote Sensing and GIS

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