Obtención de líneas de costa con precisión sub-píxel a partir de imágenes Landsat (TM, ETM + y OLI)

En esta tesis se presenta un método y una serie de herramientas informáticas para obtener automáticamente la posición de líneas de costa partiendo de imágenes registradas por los satélites Landsat (5,7 y 8) con un nivel de precisión cercano a 5,5 m (EMC). Para conseguirlo se han resuelto tres cuestiones: (i) la definición de un algoritmo de extracción automática de la línea de costa a nivel subpíxel, (ii) la georreferenciación de las sucesivas imágenes también a nivel sub-píxel y (iii) la adaptación específica a los distintos tipos de imágenes Landsat. El método desarrollado se sustenta en el análisis de las bandas del infrarojo próximo y medio, en las que existe una diferencia muy marcada en la respuesta radiométrica del agua y el suelo. Una umbralización inicial permite la detección de la línea de costa a nivel píxel. Alrededor de tal línea, se aplica el algoritmo propuesto para alcanzar la precisión sub-píxel. Concretamente, se ajusta una función alrededor de cada píxel de la línea aproximada y, sobre dicha función matemática, se deducen, realizando perfiles transversales, los puntos de máximo gradiente. Finalmente, el promedio de tales puntos define la posición de la línea de costa. En primer lugar se ha evaluado el algoritmo sobre imágenes QuickBird (2,4 m de resolución espacia) definido la posición verdadera de la línea de costa mediante fotointerpretación para servir de referencia. Posteriormente, se han remuestreado esas mismas imágenes a tamaños de píxeles similares a los de las imágenes Landsat, se ha aplicado al algoritmo propuesto y evaluado frente a la línea de referencia. En segundo lugar, se ha propuesto y evaluado un método de georreferenciación basado en la correlación cruzada. Para realizar una evaluación independiente de las líneas de costa, se ha generado un conjunto de imágenes de traslación conocida. Al aplicar el método propuesto y comparar sus resultados con la traslación conocida se ha podido describir el comportamiento de los errores. Los errores observados se acercan a los 0,1 píxeles. Esto implica, al aplicarse sobre imágenes con una resolución igual a la de Landsat (30m/píxel), un error esperable de 2 m. En tercer lugar, se han unificado los procesos de obtención de la línea de costa y de georreferenciación para su aplicación sobre las bandas infrarrojas de Landsat TM/ETM/OLI. Para la validación, se han tomado como referencia ciertas zonas de costa que no han sufrido variaciones en el tiempo de estudio. Se ha demostrado que la reflectancia de las zonas terrestres próximas a la costa afecta a la posición de la línea de costa que obtiene el algoritmo. Este comportamiento ha podido ser descrito estadísticamente. De esta manera, en función de qué sensor y banda se empleen, es posible corregir la línea de costa y llevarla a su posición definitiva. Tomando el total de líneas de costa analizadas se obtiene un error medio cuadrático de 5,5 m. Una vez establecido el nivel de precisión que se consigue con la metodología propuesta en la tesis se abordan dos aplicaciones específicas: (i) un estudio sobre el impacto de una serie de temporales costeros sobre un amplio segmento de playas arenosas (100 km) y los procesos de recuperación de esas playas y (ii) un estudio de la tendencia a medio plazo (casi treinta años) de un segmento costero (14 km). Estos dos estudios han permitido mostrar la utilidad de las líneas de costa así obtenidas, evidenciando que aportan una nueva fuente de información para los estudios de la dinámica de las playas. Si bien la metodología presenta algunas limitaciones, resulta claro que también resuelve otras que son propias del resto del fuentes de datos disponibles para los estudios de la dinámica costera.Almonacid Caballer, J. (2014). Obtención de líneas de costa con precisión sub-píxel a partir de imágenes Landsat (TM, ETM + y OLI) [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48462TESISPremios Extraordinarios de tesis doctorale

Evaluating Fourier Cross-Correlation Sub-Pixel Registration in Landsat Images

[EN] Multi-temporal analysis is one of the main applications of remote sensing, and Landsat imagery has been one of the main resources for many years. However, the moderate spatial resolution (30 m) restricts their use for high precision applications. In this paper, we simulate Landsat scenes to evaluate, by means of an exhaustive number of tests, a subpixel registration process based on phase correlation and the upsampling of the Fourier transform. From a high resolution image (0.5 m), two sets of 121 synthetic images of fixed translations are created to simulate Landsat scenes (30 m). In this sense, the use of the point spread function (PSF) of the Landsat TM (Thematic Mapper) sensor in the downsampling process improves the results compared to those obtained by simple averaging. In the process of obtaining sub-pixel accuracy by upsampling the cross correlation matrix by a certain factor, the limit of improvement is achieved at 0.1 pixels. We show that image size affects the cross correlation results, but for images equal or larger than 100 x 100 pixels similar accuracies are expected. The large dataset used in the tests allows us to describe the intra-pixel distribution of the errors obtained in the registration process and how they follow a waveform instead of random/stochastic behavior. The amplitude of this waveform, representing the highest expected error, is estimated at 1.88 m. Finally, a validation test is performed over a set of sub-pixel shorelines obtained from actual Landsat-5 TM, Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI (Operation Land Imager) scenes. The evaluation of the shoreline accuracy with respect to permanent seawalls, before and after the registration, shows the importance of the registering process and serves as a non-synthetic validation test that reinforce previous results.This study has been supported by a research project from the Spanish Ministry of Economy and Competitiveness (CGL2015-69906-R).Almonacid Caballer, J.; Pardo Pascual, JE.; Ruiz Fernández, LÁ. (2017). Evaluating Fourier Cross-Correlation Sub-Pixel Registration in Landsat Images. Remote Sensing. 9(10). https://doi.org/10.3390/rs9101051S91

Detecting problematic beach widths for the recreational function along the Gulf of Valencia (Spain) from Landsat 8 subpixel shorelines

[EN] This work shows a continuous and regional monitoring of the beach width and how to link it with the recreational function of these spaces. Shorelines automatically derived from Landsat 8 satellite were employed for this purpose, covering up to 83 dates (2013¿2016) and 150¿km of beaches. The study included the microtidal beaches of the Gulf of Valencia, a strongly developed coast with intensive use in the Western Mediterranean. Beach widths were defined in alongshore coastal segments of 80-m length. Annual mean width and annual percentiles appeared as representative statistics of the beach state and the most unfavorable widths occurred throughout the year. Considering these statistical descriptors, beach segments were classified according to their adequacy to sustain a recreational function. The integration of descriptors of the beach width and use of the beach data on a regional scale offers a holistic approach to identify potentially problematic segments, crucial information for coastal managers.This study integrates findings and results obtained within the framework of the FPU15/04501 granted by the Spanish Ministry of Education, Culture and Sports to Carlos Cabezas Rabadan, and by the funds of the research project RESETOCOAST (CGL2015-69906-R) supported by the Ministry of Economy, Industry, and Competitiveness. Authors acknowledge the USGS for providing free access to the Landsat imagery.Cabezas-Rabadán, C.; Pardo Pascual, JE.; Almonacid-Caballer, J.; Rodilla, M. (2019). Detecting problematic beach widths for the recreational function along the Gulf of Valencia (Spain) from Landsat 8 subpixel shorelines. Applied Geography. 110:1-13. https://doi.org/10.1016/j.apgeog.2019.102047S11311

A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level

[EN] This paper presents a new methodological process for detecting the instantaneous land-water border at sub-pixel level from mid-resolution satellite images (30 m/pixel) that are freely available worldwide. The new method is based on using an iterative procedure to compute Laplacian roots of a polynomial surface that represents the radiometric response of a set of pixels. The method uses a first approximation of the shoreline at pixel level (initial pixels) and selects a set of neighbouring pixels to be part of the analysis window. This adaptive window collects those stencils in which the maximum radiometric variations are found by using the information given by divided differences. Therefore, the land-water surface is computed by a piecewise interpolating polynomial that models the strong radiometric changes between both interfaces. The assessment is tested on two coastal areas to analyse how their inherent differences may affect the method. A total of 17 Landsat 7 and 8 images (L7 and L8) were used to extract the shorelines and compare them against other highly accurate lines that act as references. Accurate quantitative coastal data from the satellite images is obtained with a mean horizontal error of 4.38 +/- 5.66 m and 1.79 +/- 2.78 m, respectively, for L7 and L8. Prior methodologies to reach the sub-pixel shoreline are analysed and the results verify the solvency of the one proposed.This study is part of the PhD dissertation of E. Sanchez-Garcia, which was supported by a grant from the Spanish Ministry of Education, Culture and Sports (I + D + i 2013-2016). The authors also appreciate the financial support provided by the Spanish Ministry of Economy and Competitiveness (CGL2015-69906-R)Sánchez-García, E.; Balaguer-Beser, Á.; Almonacid-Caballer, J.; Pardo Pascual, JE. (2019). A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level. Remote Sensing. 11(16):1-28. https://doi.org/10.3390/rs11161880S1281116Szmytkiewicz, M., Biegowski, J., Kaczmarek, L. M., Okrój, T., Ostrowski, R., Pruszak, Z., … Skaja, M. (2000). Coastline changes nearby harbour structures: comparative analysis of one-line models versus field data. Coastal Engineering, 40(2), 119-139. doi:10.1016/s0378-3839(00)00008-9Furmańczyk, K., Andrzejewski, P., Benedyczak, R., Bugajny, N., Cieszyński, Ł., Dudzińska-Nowak, J., … Zawiślak, T. (2014). Recording of selected effects and hazards caused by current and expected storm events in the Baltic Sea coastal zone. Journal of Coastal Research, 70, 338-342. doi:10.2112/si70-057.1Deng, J., Harff, J., Zhang, W., Schneider, R., Dudzińska-Nowak, J., Giza, A., … Furmańczyk, K. (2017). The Dynamic Equilibrium Shore Model for the Reconstruction and Future Projection of Coastal Morphodynamics. Coastal Research Library, 87-106. doi:10.1007/978-3-319-49894-2_6Paprotny, D., Andrzejewski, P., Terefenko, P., & Furmańczyk, K. (2014). Application of Empirical Wave Run-Up Formulas to the Polish Baltic Sea Coast. PLoS ONE, 9(8), e105437. doi:10.1371/journal.pone.0105437Roelvink, D., Reniers, A., van Dongeren, A., van Thiel de Vries, J., McCall, R., & Lescinski, J. (2009). Modelling storm impacts on beaches, dunes and barrier islands. Coastal Engineering, 56(11-12), 1133-1152. doi:10.1016/j.coastaleng.2009.08.006Kostrzewski, A., Zwoliński, Z., Winowski, M., Tylkowski, J., & Samołyk, M. (2015). Cliff top recession rate and cliff hazards for the sea coast of Wolin Island (Southern Baltic). Baltica, 28(2), 109-120. doi:10.5200/baltica.2015.28.10Terefenko, P., Zelaya Wziątek, D., Dalyot, S., Boski, T., & Pinheiro Lima-Filho, F. (2018). A High-Precision LiDAR-Based Method for Surveying and Classifying Coastal Notches. ISPRS International Journal of Geo-Information, 7(8), 295. doi:10.3390/ijgi7080295Terefenko, P., Paprotny, D., Giza, A., Morales-Nápoles, O., Kubicki, A., & Walczakiewicz, S. (2019). Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis. Remote Sensing, 11(7), 843. doi:10.3390/rs11070843Kolander, R., Morche, D., & Bimböse, M. (2013). Quantification of moraine cliff coast erosion on Wolin Island (Baltic Sea, northwest Poland). Baltica, 26(1), 37-44. doi:10.5200/baltica.2013.26.04Moore, L. J., Ruggiero, P., & List, J. H. (2006). Comparing Mean High Water and High Water Line Shorelines: Should Proxy-Datum Offsets be Incorporated into Shoreline Change Analysis? Journal of Coastal Research, 224, 894-905. doi:10.2112/04-0401.1Davidson, M., Van Koningsveld, M., de Kruif, A., Rawson, J., Holman, R., Lamberti, A., … Aarninkhof, S. (2007). The CoastView project: Developing video-derived Coastal State Indicators in support of coastal zone management. Coastal Engineering, 54(6-7), 463-475. doi:10.1016/j.coastaleng.2007.01.007Aarninkhof, S. G. ., Turner, I. L., Dronkers, T. D. ., Caljouw, M., & Nipius, L. (2003). A video-based technique for mapping intertidal beach bathymetry. Coastal Engineering, 49(4), 275-289. doi:10.1016/s0378-3839(03)00064-4Andriolo, U., Sánchez-García, E., & Taborda, R. (2019). Operational Use of Surfcam Online Streaming Images for Coastal Morphodynamic Studies. Remote Sensing, 11(1), 78. doi:10.3390/rs11010078Sánchez-García, E., Balaguer-Beser, A., & Pardo-Pascual, J. E. (2017). C-Pro: A coastal projector monitoring system using terrestrial photogrammetry with a geometric horizon constraint. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 255-273. doi:10.1016/j.isprsjprs.2017.03.023Holman, R. A., & Stanley, J. (2007). The history and technical capabilities of Argus. Coastal Engineering, 54(6-7), 477-491. doi:10.1016/j.coastaleng.2007.01.003Sagar, S., Roberts, D., Bala, B., & Lymburner, L. (2017). Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations. Remote Sensing of Environment, 195, 153-169. doi:10.1016/j.rse.2017.04.009Luijendijk, A., Hagenaars, G., Ranasinghe, R., Baart, F., Donchyts, G., & Aarninkhof, S. (2018). The State of the World’s Beaches. Scientific Reports, 8(1). doi:10.1038/s41598-018-24630-6Li, J., & Roy, D. (2017). A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote Sensing, 9(9), 902. doi:10.3390/rs9090902Boak, E. H., & Turner, I. L. (2005). Shoreline Definition and Detection: A Review. Journal of Coastal Research, 214, 688-703. doi:10.2112/03-0071.1Gens, R. (2010). Remote sensing of coastlines: detection, extraction and monitoring. International Journal of Remote Sensing, 31(7), 1819-1836. doi:10.1080/01431160902926673Liu, H., Wang, L., Sherman, D. J., Wu, Q., & Su, H. (2011). Algorithmic Foundation and Software Tools for Extracting Shoreline Features from Remote Sensing Imagery and LiDAR Data. Journal of Geographic Information System, 03(02), 99-119. doi:10.4236/jgis.2011.32007Pardo-Pascual, J. E., Almonacid-Caballer, J., Ruiz, L. A., & Palomar-Vázquez, J. (2012). Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision. Remote Sensing of Environment, 123, 1-11. doi:10.1016/j.rse.2012.02.024Pardo-Pascual, J. E., Almonacid-Caballer, J., Ruiz, L. A., Palomar-Vázquez, J., & Rodrigo-Alemany, R. (2014). Evaluation of storm impact on sandy beaches of the Gulf of Valencia using Landsat imagery series. Geomorphology, 214, 388-401. doi:10.1016/j.geomorph.2014.02.020Almonacid-Caballer, J., Sánchez-García, E., Pardo-Pascual, J. E., Balaguer-Beser, A. A., & Palomar-Vázquez, J. (2016). Evaluation of annual mean shoreline position deduced from Landsat imagery as a mid-term coastal evolution indicator. Marine Geology, 372, 79-88. doi:10.1016/j.margeo.2015.12.015Sánchez-García, E., Pardo-Pascual, J. E., Balaguer-Beser, A., & Almonacid-Caballer, J. (2015). ANALYSIS OF THE SHORELINE POSITION EXTRACTED FROM LANDSAT TM AND ETM+ IMAGERY. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3, 991-998. doi:10.5194/isprsarchives-xl-7-w3-991-2015Pardo-Pascual, J., Sánchez-García, E., Almonacid-Caballer, J., Palomar-Vázquez, J., Priego de los Santos, E., Fernández-Sarría, A., & Balaguer-Beser, Á. (2018). Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery. Remote Sensing, 10(2), 326. doi:10.3390/rs10020326Almonacid-Caballer, J., Pardo-Pascual, J., & Ruiz, L. (2017). Evaluating Fourier Cross-Correlation Sub-Pixel Registration in Landsat Images. Remote Sensing, 9(10), 1051. doi:10.3390/rs9101051Liu, Q., Trinder, J., & Turner, I. (2016). A COMPARISON OF SUB-PIXEL MAPPING METHODS FOR COASTAL AREAS. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, III-7, 67-74. doi:10.5194/isprsannals-iii-7-67-2016Liu, Y., Wang, X., Ling, F., Xu, S., & Wang, C. (2017). Analysis of Coastline Extraction from Landsat-8 OLI Imagery. Water, 9(11), 816. doi:10.3390/w9110816Liu, Q., Trinder, J., & Turner, I. L. (2017). Automatic super-resolution shoreline change monitoring using Landsat archival data: a case study at Narrabeen–Collaroy Beach, Australia. Journal of Applied Remote Sensing, 11(1), 016036. doi:10.1117/1.jrs.11.016036Cipolletti, M. P., Delrieux, C. A., Perillo, G. M. E., & Cintia Piccolo, M. (2012). Superresolution border segmentation and measurement in remote sensing images. Computers & Geosciences, 40, 87-96. doi:10.1016/j.cageo.2011.07.015Liu, H., & Jezek, K. C. (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. doi:10.1080/0143116031000139890Hermosilla, T., Bermejo, E., Balaguer, A., & Ruiz, L. A. (2008). Non-linear fourth-order image interpolation for subpixel edge detection and localization. Image and Vision Computing, 26(9), 1240-1248. doi:10.1016/j.imavis.2008.02.012Harten, A., Engquist, B., Osher, S., & Chakravarthy, S. R. (1987). Uniformly high order accurate essentially non-oscillatory schemes, III. Journal of Computational Physics, 71(2), 231-303. doi:10.1016/0021-9991(87)90031-3Shu, C.-W., & Osher, S. (1988). Efficient implementation of essentially non-oscillatory shock-capturing schemes. Journal of Computational Physics, 77(2), 439-471. doi:10.1016/0021-9991(88)90177-5Capilla, M. T., & Balaguer-Beser, A. (2013). A new well-balanced non-oscillatory central scheme for the shallow water equations on rectangular meshes. Journal of Computational and Applied Mathematics, 252, 62-74. doi:10.1016/j.cam.2013.01.014Balaguer, Á., & Conde, C. (2005). Fourth-Order Nonoscillatory Upwind and Central Schemes for Hyperbolic Conservation Laws. SIAM Journal on Numerical Analysis, 43(2), 455-473. doi:10.1137/s0036142903437106Xu, N. (2018). Detecting Coastline Change with All Available Landsat Data over 1986–2015: A Case Study for the State of Texas, USA. Atmosphere, 9(3), 107. doi:10.3390/atmos9030107Balaguer, A., Conde, C., López, J. A., & Martínez, V. (2001). A finite volume method with a modified ENO scheme using a Hermite interpolation to solve advection diffusion equations. International Journal for Numerical Methods in Engineering, 50(10), 2339-2371. doi:10.1002/nme.123Press, W. H., & Teukolsky, S. A. (1990). Savitzky-Golay Smoothing Filters. Computers in Physics, 4(6), 669. doi:10.1063/1.482296

Coastline variability from satellite imagery and its relation with sediment texture

[EN] Beaches are natural environments of great interest for our society. They go through remarkable changes run by key factors that are interconnected according to the literature. A better understanding of these parameters, such as sediment texture and shoreline variability, would be of a great interest for coastal monitoring and planning. Shorelines of all Landsat 8 (OLI) images available over the course of one year have been obtained for determining the variability that has occurred in different Valencian beaches. Likewise, the relation between shoreline variability and sediment texture has been evaluated, showing that beaches with higher variability over the year have smaller sediment texture, which is also related with gentle slopes, and vice versa. The methodology allows obtaining the shoreline variability, a key parameter of beach morphodynamics, in a semiautomatic way. The variability allows developing a gross estimate of beach texture.[ES] Las playas son entornos naturales de enorme interés para nuestra sociedad. Estos espacios están sometidos a grandes cambios regidos por factores clave fuertemente interrelacionados según la literatura. Un mayor conocimiento de estos parámetros, como la textura del sedimento o la variabilidad de la línea de costa de las playas resultaría de gran interés para la monitorización y gestión de la costa. Se han obtenido las líneas de costa de todas las escenas Landsat 8 (OLI) disponibles a lo largo de un año para, a partir de ellas, determinar la variabilidad de diferentes playas valencianas. Asimismo, se ha evaluado la relación existente entre dicha variabilidad y la textura del sedimento de las playas, mostrando que las playas con mayores cambios en la línea de costa a lo largo del año son aquellas con un tamaño de grano menor, asociado a pendientes más suaves y viceversa. La metodología seguida incluye la obtención de forma semiautomática de la variabilidad de la línea de costa, un parámetro clave de la morfodinámica costera, y a partir de ella la estimación a grandes rasgos de la textura del sedimento de las playas.Este proyecto ha sido realizado con la ayuda de la beca FPU15/04501 concedida por el MECD a C. Cabezas, así como con el proyecto RESETOCOAST (CGL2015- 69906-R) del programa Retos del MINECO. Los autores agradecen el apoyo de la Oficina Técnica DevesaAlbufera.Cabezas-Rabadán, C.; Almonacid-Caballer, J.; Pardo-Pascual, JE.; Soriano-González, J. (2017). Variabilidad de la línea de costa a partir de imágenes de satélite y su relación con la textura del sedimento. En Primer Congreso en Ingeniería Geomática. Libro de actas. Editorial Universitat Politècnica de València. 153-161. https://doi.org/10.4995/CIGeo2017.2017.6628OCS15316

Analysis of the shoreline position extracted from Landsat TM and ETM+ imagery

[EN] A statistical analysis of the results obtained by the tool SELI (Shoreline Extraction from Landsat Imagery) is made in order to characterise the medium and long term period changes occurring on beaches. The analysis is based on the hypothesis that intra-annual shifts of coastline positions hover around an average position, which would be significant when trying to set these medium and long term trends. Fluctuations around this average are understood as the effect of short-term changes -variations related to sea level, wave run-up, and the immediate morphological beach profile settings of the incident waves- whilst the alterations of the average position will obey changes relating to the global sedimentary harmony of the analysed beach segment. The goal of this study is to assess the validity of extracted Landsat shorelines knowing whether the intrinsic error could alter the position of the computed mean annual shoreline or if it is balanced out between the successive averaged images. Two periods are stablished for the temporal analysis in the area according to the availability of other data taken from high precision sources. Statistical tests performed to compare samples (Landsat versus high accuracy) indicate that the two sources of data provide similar information regarding annual means; coastal behaviour and dynamics, thereby verifying Landsat shorelines as useful data for evolutionary studies.This work is part of the PhD of the first author which is supported by the “Ministerio de Educación, Cultura y Deporte” of Spain (state program in I+D+i 2013-2016)Sánchez García, E.; Pardo Pascual, JE.; Balaguer Beser, ÁA.; Almonacid Caballer, J. (2015). Analysis of the shoreline position extracted from Landsat TM and ETM+ imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XL-7/W3:991-998. doi:10.5194/isprsarchives-XL-7-W3-991-2015S991998XL-7/W

Monitoring the response of mediterranean beaches to storms and anthropogenic actions using Landsat imagery

[EN] Large-scale and continuous monitoring of morphological changes on beaches is of great interest for coastal management. Shoreline positions were extracted with the system SHOREX on multiple dates on three beaches of the Gulf of Valencia from Landsat 5, 7 and 8 images from the period 1984-2014. These data made it possible to analyze the evolution of the beaches over three decades, as well as their short-term changes. In this way, the capacity of the shorelines to represent the response of the beaches to coastal storms and anthropogenic actions was evaluated. The shorelines obtained from SHOREX show great potential for monitoring and surveillance of the state of the beaches, while the analysis of their changes provides key information on the nature of the beaches.[ES] La monitorización a gran escala y de forma continua de los cambios morfológicos en playas presenta un gran interés para la gestión costera. La posición de la línea de costa ha sido definida en tres playas del golfo de Valencia en múltiples fechas durante el periodo 1984-2014 partiendo de las imágenes Landsat 5, 7 y 8 y el sistema para la extracción de líneas de costa SHOREX. Estos datos han permitido analizar la evolución de las playas durante tres décadas, así como sus cambios a corto plazo. De este modo, se ha evaluado la capacidad de las líneas para representar la respuesta de las playas ante fenómenos de temporales costeros y actuaciones antrópicas en el medio costero. Las líneas obtenidas de SHOREX muestran un gran potencial para el seguimiento y la vigilancia del estado de las playas, a la vez que el análisis de sus cambios suministra información clave de la naturaleza de las playas.Este trabajo se ha beneficiado del contrato de investigación FPU15 otorgado por el Ministerio de educación, ciencia y deporte al primer autor, así como por fondos del proyecto RESETOCOAST (CGL2015-69906-R) del Programa Retos-2015 del Ministerio de Economía, Industria y Competitividad.Cabezas-Rabadán, C.; Pardo Pascual, JE.; Almonacid-Caballer, J.; Palomar-Vázquez, J.; Fernández-Sarría, A. (2019). Monitorización de la respuesta de playas mediterráneas a temporales y actuaciones antrópicas mediante imágenes Landsat. GeoFocus. Revista Internacional de Ciencia y Tecnología de la Información Geográfica. (23):119-139. https://doi.org/10.21138/GF.640S1191392

Assessing the accuracy of automatically extracted shorelines on microtidal beaches from Landsat 7, Landsat 8 and Sentinel-2 imagery

[EN] This paper evaluates the accuracy of shoreline positions obtained from the infrared (IR) bands of Landsat 7, Landsat 8, and Sentinel-2 imagery on natural beaches. A workflow for sub-pixel shoreline extraction, already tested on seawalls, is used. The present work analyzes the behavior of that workflow and resultant shorelines on a micro-tidal (<20 cm) sandy beach and makes a comparison with other more accurate sets of shorelines. These other sets were obtained using differential GNSS surveys and terrestrial photogrammetry techniques through the C-Pro monitoring system. 21 sub-pixel shorelines and their respective high-precision lines served for the evaluation. The results prove that NIR bands can easily confuse the shoreline with whitewater, whereas SWIR bands are more reliable in this respect. Moreover, it verifies that shorelines obtained from bands 11 and 12 of Sentinel-2 are very similar to those obtained with bands 6 and 7 of Landsat 8 (-0.75 +/- 2.5 m; negative sign indicates landward bias). The variability of the brightness in the terrestrial zone influences shoreline detection: brighter zones cause a small landward bias. A relation between the swell and shoreline accuracy is found, mainly identified in images obtained from Landsat 8 and Sentinel-2. On natural beaches, the mean shoreline error varies with the type of image used. After analyzing the whole set of shorelines detected from Landsat 7, we conclude that the mean horizontal error is 4.63 m (+/- 6.55 m) and 5.50 m (+/- 4.86 m), respectively, for high and low gain images. For the Landsat 8 and Sentinel-2 shorelines, the mean error reaches 3.06 m (+/- 5.79 m).The authors appreciate the financial support provided by the Spanish Ministry of Economy and Competitiveness in the framework of project CGL2015-69906-R. This study is part of the Ph.D. dissertation of the second author, which is supported by a grant from the Spanish Ministry of Education, Culture and Sports (I+D+i 2013-2016). The authors are extremely grateful to different reviewers and editors of this work because their observations and suggestions have improved the final article a lot.Pardo Pascual, JE.; Sánchez García, E.; Almonacid-Caballer, J.; Palomar-Vázquez, J.; Priego De Los Santos, E.; Fernández-Sarría, A.; Balaguer-Beser, Á. (2018). Assessing the accuracy of automatically extracted shorelines on microtidal beaches from Landsat 7, Landsat 8 and Sentinel-2 imagery. Remote Sensing. 10(2). https://doi.org/10.3390/rs10020326S10

Evaluation of storm impact on sandy beaches of the Gulf of Valencia using Landsat imagery series

Supplementary data associated with this article can be found in the online version, at http://dx.doi.org/10.1016/j.geomorph.2014.02.020. These data include Google maps of the most important areas described in this article.The impact of storms on sandy beaches and the subsequent recovery process is described from an analysis of the shoreline positions obtained from Landsat 5 TM and Landsat 7 ETM + imagery. Shoreline extraction is based on an algorithm previously proposed by the authors that enables a positioning accuracy of 5 m root mean square error (RMSE). The impact of six storms registered over a period of seven months (between November 2001 and May 2002) and the beach recovery processes until December 2002 across a 100 km segment of the Gulf of Valencia on the Spanish Mediterranean coast were analysed by comparing 12 shoreline positions. The multiple shoreline positions obtained from Landsat images provide very useful information for describing the impact of storms and the recovery process across large segments of microtidal coast. This enables the identification of differences not only in the magnitude of change produced by a particular event but also in the cumulative effect associated with several storm events, and in the study of how the beach recovery process takes place. The results show a high level of spatial variability. Beaches with steep slopes experienced fewer changes than shallow slopes. The existence of well developed foredunes in some areas minimised the reduction in the beach width after the storms. Coastal orientation was another important factor in explaining storm impact and the recovery process. This factor affects not only the way the waves interact with the beaches but also the sediment longshore transport: beach regeneration is slower when the transport of sediments is limited by artificial infrastructures (groins, jetties, ports) or natural sediment traps (headlands). The main limitations of using the proposed methodology to obtain the shoreline position from Landsat images are related to: (i) the precision in the shoreline detection; (ii) the nature of the indicator obtained, that is, the water/land interface; and (iii) the registration instant defined by the image acquisition time. However, the high frequency of the data acquisition and the possibility to cover large coastal areas bring a new perspective that enriches other methods and tools used by coastal scientists.The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion in the framework of the Projects CGL2009-14220-C02-01 and CGL2010-19591. We also thank the Direccion General de Costas in Valencia for making available the data for the tests and analysis. Finally, we would like to thank the useful suggestions provided by the anonymous referees and the assigned editor, which enabled us to improve the quality of this paper.Pardo Pascual, JE.; Almonacid Caballer, J.; Ruiz Fernández, LÁ.; Palomar-Vázquez, J.; Rodrigo-Alemany, R. (2014). Evaluation of storm impact on sandy beaches of the Gulf of Valencia using Landsat imagery series. Geomorphology. 214:388-401. doi:10.1016/j.geomorph.2014.02.020S38840121

Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision

A high precision geometric method for automated shoreline detection from Landsat TM and ETM+ imagery is presented. The methodology is based on the application of an algorithm that ensures accurate image geometric registration and the use of a new algorithm for sub-pixel shoreline extraction, both at the sub-pixel level. The analysis of the initial errors shows the influence that differences in reflectance of land cover types have over shoreline detection, allowing us to create a model to substantially reduce these errors. Three correction models were defined according to the type of gain used in the acquisition of the original Landsat images. Error assessment tests were applied on three artificially stabilised coastal segments that have a constant and well-defined land-water boundary. A testing set of 45 images (28 TM, 10 ETM high-gain and 7 ETM low-gain) was used. The mean error obtained in shoreline location ranges from 1.22 to 1.63. m, and the RMSE from 4.69 to 5.47. m. Since the errors follow a normal distribution, then the maximum error at a given probability can be estimated. The results confirm that the use of Landsat imagery for detection of instantaneous coastlines yields accuracy comparable to high-resolution techniques, showing the potential of Landsat TM and ETM images in those applications where the instantaneous lines are a good geomorphological descriptor. © 2012 Elsevier Inc.The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion and the Spanish Plan E in the framework of the Projects CGL2009-14220-C02-01 and CGL2010-19591.Pardo Pascual, JE.; Almonacid Caballer, J.; Ruiz Fernández, LÁ.; Palomar-Vázquez, J. (2012). Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision. Remote Sensing of Environment. 123:1-11. doi:10.1016/j.rse.2012.02.024S11112