Automatic Extraction of Tide-Coordinated Shoreline Using Open Source Software and Landsat Imagery

Gonçalves, Gil; Duro, N.; Sousa, Ercília; Figueiredo, Isabel N.

doi:10.5194/isprsarchives-xl-7-w3-953-2015

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…In fact, the increasing availability, resolution and spatial coverage of satellite imagery in recent years now provides a powerful alternative to derive reliable, global scale shoreline data. In this direction, in many recent studies the satellite images coupled with image processing techniques have been used [69][70][71][72][73].…”

Section: Discussionmentioning

confidence: 99%

Application of a Coastal Vulnerability Index. A Case Study along the Apulian Coastline, Italy

Pantusa

D’Alessandro

Riefolo

et al. 2018

Water

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The coastal vulnerability index (CVI) is a popular index in literature to assess the coastal vulnerability of climate change. The present paper proposes a CVI formulation to make it suitable for the Mediterranean coasts; the formulation considers ten variables divided into three typological groups: geological; physical process and vegetation. In particular, the geological variables are: geomorphology; shoreline erosion/accretion rates; coastal slope; emerged beach width and dune width. The physical process variables are relative sea-level change; mean significant wave height and mean tide range. The vegetation variables are width of vegetation behind the beach and posidonia oceanica. The first application of the proposed index was carried out for a stretch of the Apulia region coast, in the south of Italy; this application allowed to (i) identify the transects most vulnerable to sea level rise, storm surges and waves action and (ii) consider the usefulness of the index as a tool for orientation in planning strategies. For the case study presented in this work, the most influential variables in determining CVI are dune width and geomorphology. The transects that present a very high vulnerability are characterized by sandy and narrow beaches (without dunes and vegetation) and by the absence of Posidonia oceanica.

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Section: Discussionmentioning

confidence: 99%

Application of a Coastal Vulnerability Index. A Case Study along the Apulian Coastline, Italy

Pantusa

D’Alessandro

Riefolo

et al. 2018

Water

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“…From the georeferenced images, the modified normalized difference water index (MNDWI) was extracted. This allows to distinguish, in an efficient way, the information of waterbodies of the others, such as soil, buildings, and vegetation (Gonçalves et al 2015). The MNDWI generation was performed through the application of Equation (1), using the green bands and shortwave infrared (SWIR 1) (Gautam et al 2015):…”

Section: Remote-sensing Imaging Preprocessingmentioning

confidence: 99%

Creation of a coastal evolution prognostic model using shoreline historical data and techniques of digital image processing in a GIS environment for generating future scenarios

Sousa

Souto

Matos

et al. 2018

International Journal of Remote Sensing

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The process of coastal erosion is a global problem that impacts approximately 70% of coastal regions of the Earth. It causes loss of property, infrastructure, and biodiversity, besides generating major economic impacts. Therefore, the analysis and monitoring of coastal erosion is an issue that needs to be addressed. In this sense, remotesensing data have been widely used in studies that evaluate the spatial and temporal changes of land use. In addition, the use of time series of satellite imagery applied in the investigation of changes in the Earth's coverage and its spatio-temporal pattern has been proven as an extremely efficient approach. Thus, remote sensing and geoprocessing are effective techniques to obtain continuous and dynamic information from coastal regions at different levels and scales. In this context, the main objective of this work was to create a prognostic model for the generation of future scenarios, based on the analysis of the spatialtemporal changes of the shorelines from past decades to the present, having as the pilot area the coast of the municipality of Icapuí, in the State of Ceará, Northeastern Brazil. For that, Statistical Regression technique was used. In addition, the techniques of Digital Image Processing and the extraction of the modified normalized difference water index were used. As a result, the prognosis of coastal erosion was generated for the year 2021, based on the time series of the years 1985, 1991, 1997, 2003, 2009, and 2015. After the extrapolation process, the results were validated through the mean absolute error. Furthermore, through the Python programming language and the OpenCV library, a computational solution was implemented to be executed in a Geographic Information Systems environment that automated the process of generating future prognostic and the extraction of the shoreline in a shapefile format.

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“…Several geomatic techniques have been used for this purpose. Up to now, the simple visual interpretation was generally used but, recently, topographic data collection, photogrammetry, digital image processing techniques, satellite and video remote sensing have been considered valid methods to detect shoreline objectively [10][11][12][13][14][15][16][17]. Satellite images are complementary to video and photogrammetric aerial-based monitoring, and are used to generate shorelines at regional, national or global scales with high temporal frequency (few days).…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

New Methodology for Shoreline Extraction Using Optical and Radar (SAR) Satellite Imagery

Zollini

Dominici

Alicandro

et al. 2023

JMSE

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Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Erosional trends have unfortunately been intensifying for decades due to anthropic factors and an accelerated sea level rise might exacerbate the problem. It is crucial to preserve these areas for safeguarding not only coastal ecosystems and cultural heritage, but also the population living there. In this context, monitoring coastal areas is essential and geomatics techniques, especially satellite remote sensing imagery, might prove very advantageous. In this paper, a semi-automatic methodology to extract shorelines from SAR (Synthetic Aperture Radar) Sentinel-1 and optical Sentinel-2 satellite images was developed. An experimental algorithm, called J-Net Dynamic, was tested in two pilot sites. The semi-automatic methodology was validated with GNSS (Global Navigation Satellite System) reference shorelines and demonstrated to be a powerful tool for a robust extraction of the shoreline both from optical and SAR images. The experimental algorithm was able to extract the shoreline closer to the reference with SAR images on the natural beach of Castelldefels and it was demonstrated to be less sensitive to speckle effects than the commonly used Canny Edge Detector. Using the SAR images of the urban beach of Somorrostro, the Canny detector was not able to extract the shoreline, while the new algorithm could do it but with low accuracy because of the noise induced by man-made structures. For further investigation, the Sentinel-2-extracted shorelines were also compared to the ones extracted by a state-of-the-art tool, CoastSat, in the two beaches using both automatic and manual thresholds. The mean errors obtained with J-Net Dynamic were generally higher than the ones from CoastSat using the manual threshold but lower if using the automatic one. The proposed methodology including the J-Net Dynamic algorithm proves to extract the shorelines closer to the reference in most of the cases and offers the great advantage of being able to work with both optical and SAR images. This feature could allow to reduce the time lag between satellite derived shorelines paving the way to an enhanced monitoring and management of coastal areas.

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Automatic Extraction of Tide-Coordinated Shoreline Using Open Source Software and Landsat Imagery

Cited by 5 publications

References 12 publications

Application of a Coastal Vulnerability Index. A Case Study along the Apulian Coastline, Italy

Application of a Coastal Vulnerability Index. A Case Study along the Apulian Coastline, Italy

Creation of a coastal evolution prognostic model using shoreline historical data and techniques of digital image processing in a GIS environment for generating future scenarios

New Methodology for Shoreline Extraction Using Optical and Radar (SAR) Satellite Imagery

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