Super‐resolution mapping of the waterline from remotely sensed data

Foody, Giles M.; Muslim, Aidy M.; Atkinson, Peter M.

doi:10.1080/01431160500213292

Cited by 151 publications

(94 citation statements)

References 9 publications

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“…En los últimos años se ha realizado un importante esfuerzo investigador tratando de determinar de forma eficiente la posición de la línea de costa partiendo de imágenes espaciales de resolución media (Foody et al 2005;Muslim et al 2006;Pardo-Pascual et al 2012;Almonacid-Caballer 2014;Sánchez-García et al 2015;García-Rubio et al 2015;Liu et al 2017), permitiendo obtener una enorme cantidad de información con una elevada frecuencia de actualización. El empleo de estos algoritmos permite registrar los cambios en la posición de la línea de costa con una alta frecuencia a lo largo de un año ).…”

Section: Variabilidad De La Línea De Costa a Partir De Imágenes De Saunclassified

Variabilidad De La Línea De Costa a Partir De Imágenes De Satélite Y Su Relación Con La Textura Del Sedimento

Cabezas-Rabadán

Almonacid-Caballer

Pardo-Pascual

et al. 2017

Proceedings - 1st Congress in Geomatics Engineering - CIGeo

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Abstract: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.Key words: Shoreline Detection, Coastline Variability, Beach Sediment, Sediment Texture, Landsat 8, Remote Sensing Resumen: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.Palabras clave: Detección de la línea de costa, Variabilidad de la línea de costa, Sedimento de playa, Textura del sedimento, Landsat 8, Teledetección IntroducciónLas playas son espacios naturales que proporcionan protección frente al oleaje y temporales, representan un importante recurso socioeconómico (Prodger et al. 2016), y actúan como soporte físico para los organismos que allí habitan.Las actuaciones antrópicas en la zona costera generan cambios en el sistema, modifican la distribución y las características del sedimento y en consecuencia alteran la morfología de las playas. La textura del sedimento y la anchura de las playas son factores clave para permitir el mantenimiento de sus funciones. Por tanto, resulta de gran interés disponer de información actualizada de ambos elementos para conocer el estado de la playa y plantear una gestión a...

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Section: Variabilidad De La Línea De Costa a Partir De Imágenes De Saunclassified

Variabilidad De La Línea De Costa a Partir De Imágenes De Satélite Y Su Relación Con La Textura Del Sedimento

Cabezas-Rabadán

Almonacid-Caballer

Pardo-Pascual

et al. 2017

Proceedings - 1st Congress in Geomatics Engineering - CIGeo

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“…Within this context, spectral data have some potential advantages over LIDAR: (1) there exists multispectral datasets that extend back approximately 30 years; (2) spectral data are typically less expensive per km 2 compared with LIDAR; and (3) spectral data are available for more forested areas of the globe. The use of combined hyperspectral and LIDAR data has been shown to increase significantly the accuracy of extraction of biophysical and hybrid variables [23,25], and the idea of combining datasets of different spatial and spectral resolutions is established in the literature [26][27][28]. Such a combination may allow for a stronger definition of the relationship between forest structure and spectra [29,30].…”

Section: Remote Sensing Of Forested Environmentsmentioning

confidence: 99%

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Chávez

Tullis

2013

Remote Sensing

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Coverage and frequency of remotely sensed forest structural information would benefit from single orbital platforms designed to collect sufficient data. We evaluated forest structural information content using single-date Hyperion hyperspectral imagery collected over full-canopy oak-hickory forests in the Ozark National Forest, Arkansas, USA. Hyperion spectral derivatives were used to develop machine learning regression tree rule sets for predicting forest neighborhood percentile heights generated from near-coincident Leica Geosystems ALS50 small footprint light detection and ranging (LIDAR). The most successful spectral predictors of LIDAR-derived forest structure were also tested with basal area measured in situ. Based on the machine learning regression trees developed, Hyperion spectral derivatives were utilized to predict LIDAR forest neighborhood percentile heights with accuracies between 2.1 and 3.7 m RMSE. Understory predictions consistently resulted in the highest accuracy of 2.1 m RMSE. In contrast, hyperspectral prediction of basal area measured in situ was only found to be 6.5 m 2 /ha RMSE when the average basal area across the study area was ~12 m 2 /ha. The results suggest, at a spatial resolution of 30 × 30 m, that orbital hyperspectral imagery alone can provide useful structural information related to vegetation height. Rapidly calibrated biophysical remote sensing techniques will facilitate timely assessment of regional forest conditions.

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“…In particular super-resolution restitution seeks to form a finer spatial resolution image which may then form the focus of interest (Farsiu et al, 2006;Ling et al, 2009) or super-resolution mapping which seeks to map at a sub-pixel scale (Foody et al, 2005). Super-resolution techniques have been shown able to increase the accuracy and realism of key features such as class boundaries (Foody et al, 2005; and provide useful information for ecological research. As with all techniques there are, however, challenges to address (Atkinson, 2009).…”

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confidence: 99%

An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

118

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This article provides an overview of some of the recent research in ecological informatics involving remote sensing and GIS. Attention focuses on a selected range of issues including topics such as the nature of remote sensing data sets, issues of accuracy and uncertainty, data visualization and sharing activities as well as developments in aspects of ecological modeling research. It is shown that considerable advances have been made over recent years and foundations for future research established. BackgroundGeotechnology has been couched as one of the three "mega-technologies" of the 21 st century, along with nanotechnology and biotechnology (Gewin, 2004). On the cusp of the second decade of the 21 st century it is evident that recent developments in the geotechnologies of Geographic Information Science (GIS) and remote sensing have had a substantial impact on ecological research, providing spatial data and associated information to enable the further understanding of ecological systems (Rundell et al., 2009). In a digital society there is sometimes an expectation that information is just a click away and this may apply to spatial information in the domain of ecological informatics. Indeed, it has been in the last five years that developments in and relating to the fields of GIS and remote sensing, such as those seen in internet technologies (e.g., Web 2.0; high performance communication networks; Brunt et al., 2007), sensing technology (e.g., quantum well infrared photodetectors; Krabach, 2000), data standards and interoperability (e.g., OGC® KML 2.2) and spatially explicit modeling (e.g., Osborne et al., 2007), have resulted in the revelation of previously unobservable phenomena and posing of what might be termed second generation of ecological questions. During this time we have also seen more support for the open exchange of data and software and infrastructure for location based services. This paper serves as a review of some of the recent developments in GIS and remote sensing that have been or promises to be of benefit to ecological informatics. The subjects addressed are necessarily selective and limited in scope, drawing on topics of interest to us and, we hope, to you.

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Super‐resolution mapping of the waterline from remotely sensed data

Cited by 151 publications

References 9 publications

Variabilidad De La Línea De Costa a Partir De Imágenes De Satélite Y Su Relación Con La Textura Del Sedimento

Variabilidad De La Línea De Costa a Partir De Imágenes De Satélite Y Su Relación Con La Textura Del Sedimento

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

An overview of recent remote sensing and GIS based research in ecological informatics

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