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SHORELINE IDENTFICATION USING SATELLITE IMAGES

Profile image of Luciana EstevesLuciana Esteves

eproceedings.worldscinet.com

https://doi.org/10.1142/9789814282475_0117
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11 pages

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Abstract

Shoreline identification using satellite images is compared with in situ shoreline measurements in the Yucatan Peninsula to evaluate its potential for studying shoreline changes in places with a paucity of data. This study firstly tests the detection limits of shoreline identification by comparing a SPOT image with ground shoreline measurements, and secondly we show examples of overlaying satellite-derived shorelines from three different years to assess the ability of the technique to quantify real shoreline changes. The mean (-0.19 m) and the standard deviation (4 m) between the ground and satellite-derived shoreline are much smaller than the pixel size. Shoreline changes of more than 30 m were measured between images spanning several years (2004, 2006 and 2008) in areas near to coastal structures and near urban areas without coastal vegetation.

Figures (8)
Table 1. Wavelengths detected by each band from the SPOT satellite. Table 2. Tide levels relative to BMI for each SPOT image used to extract shoreline and the measured slope f rom beach profiles and their dates.
Table 1. Wavelengths detected by each band from the SPOT satellite. Table 2. Tide levels relative to BMI for each SPOT image used to extract shoreline and the measured slope f rom beach profiles and their dates.
Figure 1. Spectral profiles on cross shore sections between shallow water and land showing the spectral intensities from the three bands (Green, Red, Near Infrared) for locations where the largest offset between the ground and satellite-derived shoreline occurs (A) and where the offset is less than 0.5 m (B). = Sto =e UL oe TS ES ee Oe ee ee ee ee es ea ee ee lm el el ee The reflectance values from red, green and near infrared wavelengths show a more limited variation anc smaller spectral intensities over the water than over the land, as shown in the examples in Figure 1. The smallest values, around 50 to 100, correspond to water, and the higher values, around 150-250, belong tc land. The shoreline has been identified as the boundary between the groups, at 70 m in the cases shown ir Figure 1A-B. The sharpness of the change in intensities from sea to land means that the shoreline can be identified with an accuracy of a pixel, and this has been confirmed by tests which show that shoreline location is insensitive to the convergence threshold used in the clustering algorithm.
Figure 1. Spectral profiles on cross shore sections between shallow water and land showing the spectral intensities from the three bands (Green, Red, Near Infrared) for locations where the largest offset between the ground and satellite-derived shoreline occurs (A) and where the offset is less than 0.5 m (B). = Sto =e UL oe TS ES ee Oe ee ee ee ee es ea ee ee lm el el ee The reflectance values from red, green and near infrared wavelengths show a more limited variation anc smaller spectral intensities over the water than over the land, as shown in the examples in Figure 1. The smallest values, around 50 to 100, correspond to water, and the higher values, around 150-250, belong tc land. The shoreline has been identified as the boundary between the groups, at 70 m in the cases shown ir Figure 1A-B. The sharpness of the change in intensities from sea to land means that the shoreline can be identified with an accuracy of a pixel, and this has been confirmed by tests which show that shoreline location is insensitive to the convergence threshold used in the clustering algorithm.
Over the 8 km shoreline stretch measured, the ground shoreline and the satellite-derived shoreline differ from each other by a mean of -0.19 m and a standard deviation of 4 m. The mean and the standard variation are much smaller than the pixel size, showing that in fact the satellite-derived shoreline is close to the edge of the run up maximum. Figure 2 shows an example of the fitting of both shorelines. The agreement over most of the beach stretch is well within the pixel size. Figure 2. Ground shoreline and satellite-derived shoreline superimposed on the image from 20" of September 2008. The upper image shows close up from the lower image. The black dots represent the ground shoreline and the red line the satellite-derived shoreline.
Over the 8 km shoreline stretch measured, the ground shoreline and the satellite-derived shoreline differ from each other by a mean of -0.19 m and a standard deviation of 4 m. The mean and the standard variation are much smaller than the pixel size, showing that in fact the satellite-derived shoreline is close to the edge of the run up maximum. Figure 2 shows an example of the fitting of both shorelines. The agreement over most of the beach stretch is well within the pixel size. Figure 2. Ground shoreline and satellite-derived shoreline superimposed on the image from 20" of September 2008. The upper image shows close up from the lower image. The black dots represent the ground shoreline and the red line the satellite-derived shoreline.
Figure 3. Predicted tide in meters in the horizontal axis and corrected height to a standard atmospheric pressure along the beach surveyed in Progreso, Mexico on September 9" 2008. ea <—)e eeee eeeeeeeeeeeeeeeee e eeeee The DGPS measurements of the shoreline began at the eastern side of the pier and then moved to the we stern side and during this period the tide level was falling, with a predicted drop of around 19 cm between t he start and end of the measurement period. To see whether this drop is detectable we have computed separ ate means and standard deviations of the differences between the measured and satellite-derived shorelines for the eastern and the western sides of the pier. For the eastern side, the resulting mean is +2.3 m with a sta ndard deviation of 2.1 m whilst to the west the mean is -4.2 m with a standard deviation of 2.8 m. A positiv e mean indicates that the DGPS shoreline is landward of the satellite shoreline, so the drop in the mean fro m east to west is qualitatively consistent with a drop in sea level over the measurement period. However th e magnitude of the difference is much greater than expected on the basis of the predicted drop in tide level; a 19 cm drop on a beach slope between 0.05 and 0.3 gives a difference of only 0.6 — 3.8 m, whilst the obser ved difference from east to west is 6.5 m. Interestingly however the measured vertical elevations of the DG PS survey also provide some independent evidence that the actual drop in mean water level was considerab ly larger than the tide prediction. Figure 3 shows a plot of the DGPS vertical elevation, corrected for atmos pheric pressure variations, versus the predicted tide level. The rapid variations may represent fluctuations i n the swash line but there is a longer term slope which fits a linear trend with a high correlation (r°=0.8). H owever the slope of this trend is about three times greater than expected, suggesting a drop of sea level of a round 40 cm; with atmospheric pressure effects included this increases to around 50 cm. The equivalent hor izontal shoreline excursion would be between 1.6 and 10 m, thus spanning the observed difference between the means to the east and west of the pier.
Figure 3. Predicted tide in meters in the horizontal axis and corrected height to a standard atmospheric pressure along the beach surveyed in Progreso, Mexico on September 9" 2008. ea <—)e eeee eeeeeeeeeeeeeeeee e eeeee The DGPS measurements of the shoreline began at the eastern side of the pier and then moved to the we stern side and during this period the tide level was falling, with a predicted drop of around 19 cm between t he start and end of the measurement period. To see whether this drop is detectable we have computed separ ate means and standard deviations of the differences between the measured and satellite-derived shorelines for the eastern and the western sides of the pier. For the eastern side, the resulting mean is +2.3 m with a sta ndard deviation of 2.1 m whilst to the west the mean is -4.2 m with a standard deviation of 2.8 m. A positiv e mean indicates that the DGPS shoreline is landward of the satellite shoreline, so the drop in the mean fro m east to west is qualitatively consistent with a drop in sea level over the measurement period. However th e magnitude of the difference is much greater than expected on the basis of the predicted drop in tide level; a 19 cm drop on a beach slope between 0.05 and 0.3 gives a difference of only 0.6 — 3.8 m, whilst the obser ved difference from east to west is 6.5 m. Interestingly however the measured vertical elevations of the DG PS survey also provide some independent evidence that the actual drop in mean water level was considerab ly larger than the tide prediction. Figure 3 shows a plot of the DGPS vertical elevation, corrected for atmos pheric pressure variations, versus the predicted tide level. The rapid variations may represent fluctuations i n the swash line but there is a longer term slope which fits a linear trend with a high correlation (r°=0.8). H owever the slope of this trend is about three times greater than expected, suggesting a drop of sea level of a round 40 cm; with atmospheric pressure effects included this increases to around 50 cm. The equivalent hor izontal shoreline excursion would be between 1.6 and 10 m, thus spanning the observed difference between the means to the east and west of the pier.
Figure 4A-B shows the three different satellite-derived shorelines overlaid on the 2008 satellite image. There are differences of more than 40 m between 2004 and 2008 shoreline. It is possible to appreciate that along the whole beach the oldest shoreline is consistently seaward of the most recent one and the 2006 shoreline shows that the landward movement has been progressive over this period. Figure 4. The upper images show a close up within the A and B squares shown in the lower image, showing three satellite-derived shorelines from 2004, 2006 and 2008.
Figure 4A-B shows the three different satellite-derived shorelines overlaid on the 2008 satellite image. There are differences of more than 40 m between 2004 and 2008 shoreline. It is possible to appreciate that along the whole beach the oldest shoreline is consistently seaward of the most recent one and the 2006 shoreline shows that the landward movement has been progressive over this period. Figure 4. The upper images show a close up within the A and B squares shown in the lower image, showing three satellite-derived shorelines from 2004, 2006 and 2008.
Nevertheless, between 2006 and 2008 several tropical storms and hurricanes had taken place close to the Progreso beaches. During the 2006 Atlantic Hurricane Season 10 tropical cyclones occurred (NHC, 2006). The tropical storm Alberto was particularly close to Yucatan beaches. During the 2007 Atlantic Hurricane season, 17 tropical cyclones occurred, with two tropical cyclones in close proximity, Olga and Erin, and Hurricane Dean crossed the Peninsula in August (NHC, 2007). In 2008 10 tropical cyclones occurred before the satellite acquisition, Hurricane Dolly crossed very close to the Progreso beaches (NHC, 2008). All these tropical cyclones events were likely to have had an impact on the wave conditions and on the Yucatan beaches.
Nevertheless, between 2006 and 2008 several tropical storms and hurricanes had taken place close to the Progreso beaches. During the 2006 Atlantic Hurricane Season 10 tropical cyclones occurred (NHC, 2006). The tropical storm Alberto was particularly close to Yucatan beaches. During the 2007 Atlantic Hurricane season, 17 tropical cyclones occurred, with two tropical cyclones in close proximity, Olga and Erin, and Hurricane Dean crossed the Peninsula in August (NHC, 2007). In 2008 10 tropical cyclones occurred before the satellite acquisition, Hurricane Dolly crossed very close to the Progreso beaches (NHC, 2008). All these tropical cyclones events were likely to have had an impact on the wave conditions and on the Yucatan beaches.

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References (20)

  1. Addo, K. A., Walkden, M. & Mills, J. P. , 2008. Detection, measurement and prediction of shoreline recession in Accra, Ghana. ISPRS Journal of Photogrammetry & Remote Sensing, 63: 543-558.
  2. Atlantic Oceanographic and Meteorological Laboratory-National Oceanic and Atmospheric Administration (AOML- NOAA). Hurricane Research Division. http://www.aoml.noaa.gov/hrd/tcfaq/E11.html.
  3. Alvarez, E., Rubio, R. & H, Ricalde, 2007. Beach restoration with geotextile tubes as submerged breakwaters in Yucatan, Mexico. Geotextiles and Geomembranes, 25: 233-241.
  4. Berg, R. 2009. National Hurricane Center. http://www.nhc.noaa.gov/2008atlan.shtml.
  5. Capurro-Filograsso, L., J. Herrera Silveira, J. Euan Avila, I. Mariño Tapia, C. Enriquez Ortiz, V. Castillo, J. Acosta, 2006., Coastal Evolution of the Western Littoral of the Yucatan Peninsula-Erosion and Water Quality. Final report. CONACYT and World Bank funded research project.
  6. Chu, Z. X., X. G. Sun, S. K. Zhai, K.H. Xu, 2006. Changing pattern of accretion/erosion of the modern Yellow River (Huanghe) subaerial delta, China: Based on remote sensing images. Marine Geology, 227:13-30.
  7. De Wolf, P., D. Fransaer, J. Van Sieleghem, R. Houthuys, 1994. Morphological Trends of the Belgian Coast shown by 10 Years of Remote-Sensing based Surveying. Coastal Zone Proceedings. 245-257, NO, Louisiana.
  8. Dinesh Kumar, P. K., G. Gopinath, C. M. Laluraj, P. Seralathan, D. Mitra, 2007. Change Detection Studies of Sagar Island, India, using Indian Remote Sensing Satellite 1C Linear Imaging Self-Scan Sensor III Data. Journal of Coastal Research, 23(6):1498-1502.
  9. Dyer, K.R., 1986. Coastal and Estuarine Sediment Dynamics. Wiley -Interscience.
  10. Lafon, V., Froidefond, J. M., Lahet, F. & P. Castaing, 2002. SPOT shallow water bathymetry of a moderately turbid tidal inlet based on field measurments. Remote Sensing of Environment, 81: 136-148.
  11. Lafon, V., D. De Melo Apoluceno, H. Dupuis, D. Michel, H. Howa, J. M. Froidefond, 2004. Morphodynamics of nearshore rhythmic sandbars in a mixed-energy environment (SW France): Mapping beach changes using visible satellite imagery. Estuarine, Coastal and Shelf Science, 61:289-299.
  12. Liu, H. & K.C.Jezek., 2004. Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods. International Journal of Remote Sensing, 25(5): 937-958.
  13. Magaña-Chuc, R. E., 2006., Procesos Dinámicos de Erosión -Acreción en las costas de Chuburná Chelem, Yucatán. Instituto Tecnológico de Campeche. CINVESTAV-IPN.
  14. Meyer-Arendt, K. J., 1993. Shoreline Changes along the North Yucatán Coast. Proceedings of the Eight Symposium on Coastal and Ocean Management Coastlines of the Gulf of Mexico, 1993:103-117.
  15. Morel, A., 1974. Optical properties of pure water and pure sea water. in Jerlov, N.G. and Steeman-Nielsen, E. eds. Optical Aspects of Oceanography. Academic Press.
  16. National Hurricane Center, (NHC)., 2005. 2005 Atlantic Hurricane Season. http://www.nhc.noaa.gov/2005atlan.shtml National Hurricane Center, (NHC)., 2006. 2006 Atlantic Hurricane Season. http://www.nhc.noaa.gov/2006atlan.shtml National Hurricane Center, (NHC)., 2007. 2007 Atlantic Hurricane Season. http://www.nhc.noaa.gov/2007atlan.shtml National Hurricane Center, (NHC)., 2008. 2008 Atlantic Hurricane Season. http://www.nhc.noaa.gov/2008atlan.shtml Programa de Ordenamiento Ecológico del Territorio Costero del estado de Yucatán (POETCY) in press. Semarnat, Merida, Yucatan, Mexico.
  17. Provost, J.-N., C.Collet, Rostaing, P., Pérez, P. & P. Bouthemy, 2004. Hierarchical Markovian segmentation of multispectral images for the reconstruction of water depths. Computer Vision and Image Understanding, 93:155- 174.
  18. Pugh, D., 2004. Changing Sea Levels: Effects of Tides, Weather and Climate. Cambridge: Cambridge University Press.
  19. Theodoridis, S. and K. Koutroumbas., 2006. Pattern recognition. Third ed. Elsevier.
  20. White, K. & H. M. El Asmar, 1999. Monitoring changing position of coastlines using Thematic Mapper Imagery, an example from the Nile Delta. Geomorphology, 29:93-105.

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Paulo Salles

Estuaries and Coasts, 2019

Shoreline change is an important morphological feature used to identify impacts on coastal processes caused by coastal infrastructure or natural conditions (e.g., storms or sea level rise), providing insightful information for coastal and shoreline management. While shoreline measurements are not always available, remote sensing data can provide shoreline position in scarce data areas, as well as in large and inaccessible regions. Nevertheless, few studies have used multi-temporal remote sensing data to study gradual changes for time frames of less than 10 years due to the low spatial resolution for detecting small changes. In this study, we explore the use of image data fusion from the Satellite Pour l'Observation de la Terre-5 (SPOT-5), taking into account the accuracy of shoreline mapping to detect changes in both disturbed and undisturbed coasts during 10 years in the northwestern Yucatan coast. Our results show that using image data fusion in SPOT-5 images is a feasible method to detect shoreline change trends in a relatively short time frame. Based on the proposed method, we were able to identify the factors leading to shoreline trends. This methodology proves useful for shoreline management and is appropriate for the planning of future developments in areas for which data are scarce.

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Assessment of the Impact of Anthropogenic Evolution and Natural Processes on Shoreline Dynamics Using Multi-Temporal Satellite Images and Statistical Analysis
Ammar Abulibdeh

Water

This research aims to examine changes in the eastern part of Qatar’s shoreline from 1982 to 2018 by means of satellite imagery. Five different time periods, namely 1982, 1992, 2002, 2013, and 2018, were analysed to determine shoreline movements and shoreline variations. Techniques such as maximum likelihood classification, the normalised difference vegetation index, and tasselled cap transformation were utilised to extract the shoreline data. Linear regression rate statistics were used to quantify the rate of shoreline variations. The results indicate that the majority of shoreline accretion is a result of human activities such as coastal construction, land reclamation, and building artificial islands, which are associated with the high economic activity over the past two decades. Significant changes were observed in Lusail City, The Pearl, and Hamad International Airport (HIA). Natural sediment accumulation was also observed in Al Wakra and on the southern side of HIA. In general, ...

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

Water

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...

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