Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images
(Elsevier, 2013-01) Balaguer Beser, Ángel Antonio; Ruiz Fernández, Luis Ángel; Hermosilla, T.; Recio Recio, Jorge Abel; Dpto. de Matemática Aplicada; Dpto. de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Grupo de Cartografía Geoambiental y Teledetección; Ministerio de Ciencia e Innovación
he benchmark problem proposed in this paper is to identify regions in aerial or satellite images with geometric patterns and describe the geometric properties of the constituent elements of the pattern and their spatial distribution. This is a relevant topic in image analysis processes where spatial regular patterns are studied. This paper first presents two approaches based on multi-directional semivariograms for reducing the processing time required to compute omnidirectional semivariograms. A set of parameters for describing the structure of a semivariogram, introduced by Balaguer et al. (2010), is extracted from an experimental semivariogram and analysed to quantify the heterogeneity of the distribution of elements (trees) with periodic patterns in images of orchards. An assessment is made using four image datasets. The first dataset is composed of synthetic images that simulate regularly spaced tree crops and real images, and is used to evaluate the influence that the orientation of elements (regularly spaced trees) in the objects (crop plots) has in the descriptive parameter values. This dataset is also used to compare different semivariogram computational approaches. The other three are also composed of synthetic images and are used to evaluate the semivariogram parameters under different spatial heterogeneity conditions, and are generated by varying patterns and tree characteristics, i.e., existence or absence of faults, regular/irregular distributions, and size of the elements. Finally, the proposed methodology is applied to real aerial orthoimages of orchard plots.
Photogrammetry and image processing techniques for beach monitoring
(2020-11-27) Sánchez-García, Elena; Balaguer Beser, Ángel Antonio; Pardo-Pascual, Josep Eliseu; Dpto. de Matemática Aplicada; Dpto. de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Grupo de Cartografía Geoambiental y Teledetección; Ministerio de Educación, Cultura y Deporte; Ministerio de Economía y Competitividad
[EN] The land-water boundary varies according to the sea level and the shape of a beach profile that is continuously modelled by incident waves. Attempting to model the response of a landscape as geomorphologically volatile as beaches requires multiple precise measurements to recognize responses to the actions of various geomorphic agents. It is therefore essential to have monitoring systems capable of systematically recording the shoreline accurately and effectively. New methods and tools are required to efficiently capture, characterize, and analyze information – and so obtain geomorphologically significant indicators. This is the aim of the doctoral thesis, focusing on the development of tools and procedures for coastal monitoring using satellite images and terrestrial photographs. The work brings satellite image processing and photogrammetric solutions to scientists, engineers, and coastal managers by providing results that demonstrate the usefulness of these viable and lowcost techniques. Existing and freely accessible public information (satellite images, video-derived data, or crowdsourced photographs) can be converted into high quality data for monitoring morphological changes on beaches and thus help achieve a sustainable management of coastal resources.
ÁLGEBRA LINEAL
(Editorial Universitat Politècnica de València, 2012-10-18) Marín Molina, Josefa; Balaguer Beser, Ángel Antonio; Felipe Román, María José; Capilla Romá, Maria Teresa; Dpto. de Matemática Aplicada; Escuela Técnica Superior de Ingeniería del Diseño; Dpto. de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Instituto Universitario de Matemática Pura y Aplicada; Escuela Técnica Superior de Ingeniería Industrial; Instituto Universitario de Seguridad Industrial, Radiofísica y Medioambiental; Grupo de Cartografía Geoambiental y Teledetección
Este libro proporciona un marco general para cualquiera que desee introducirse en el estudio del Álgebra lineal. El principal objetivo del texto es presentar de forma sencilla y práctica un material con definiciones, propiedades, ejemplos y aplicaciones de los conceptos estudiados, trabajándose tanto la componente matricial como la componente geométrica, ambas propias de cualquiercurso de álgebra impartido en titulaciones cienífico-técnicas. El texto está formado por once temas agrupados en tres bloques temáticos: Cálculo matricial, Espacio vectorial euclídeo y Espacio afín euclídeo. Cada uno de los temas que componen el texto está estructurado en tres partes: una primera parte expositiva con los conceptos teóricos y ejercicios; una segunda parte con un breve listado de comandos útiles del programa Mathematica y una tercera parte con una serie de problemas propuestos.
A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level
(MDPI AG, 2019-08-12) Sánchez-García, Elena; Balaguer Beser, Ángel Antonio; Almonacid Caballer, Jaime; Pardo Pascual, Josep Eliseu; Dpto. de Matemática Aplicada; Dpto. de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Grupo de Cartografía Geoambiental y Teledetección; Ministerio de Economía y Competitividad
[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.
A well-balanced high-resolution shape-preserving central scheme to solve one-dimensional sediment transport equations
(ELSEVIER SCI LTD, 2012) Capilla Romá, Maria Teresa; Balaguer Beser, Ángel Antonio; Dpto. de Matemática Aplicada; Escuela Técnica Superior de Ingeniería del Diseño; Dpto. de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Instituto Universitario de Seguridad Industrial, Radiofísica y Medioambiental; Grupo de Cartografía Geoambiental y Teledetección; Ministerio de Ciencia e Innovación; Universitat Politècnica de València
[EN] We present a well-balanced high-resolution non-oscillatory central finite volume scheme for solving the shallow water equations with a non-flat bottom topology. Time integration is obtained following a Runge¿Kutta procedure, coupled with its natural continuous extension. We use a central scheme with a point value reconstruction algorithm based on average or flux values, which satisfies the monotonicity preserving property. We apply a special treatment for the source term spatial integration, which preserves the time and space accuracy and it results in a well-balanced scheme. Several one-dimensional test cases are used to verify the behaviour and non-oscillatory properties of our scheme.
Evaluation of annual mean shoreline position deduced from Landsat imagery as a mid-term coastal evolution indicator
(Elsevier, 2016-02-01) Almonacid Caballer, Jaime; Sánchez García, Elena; Pardo Pascual, Josep Eliseu; Balaguer Beser, Ángel Antonio; Palomar Vázquez, Jesús Manuel; Dpto. de Matemática Aplicada; Dpto. de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Grupo de Cartografía Geoambiental y Teledetección; Ministerio de Educación, Cultura y Deporte
[EN] The shoreline is a useful indicator of mid-term coastal evolution. Every shoreline is affected by instantaneous sealevel, the length of the run-up, and beach profile changes. In this work, annual mean shorelines are evaluated in a manner that avoids these effects by averaging the instantaneous shoreline positions registered during the same year. A set of 270 shorelines obtained from Landsat imagery between 2000 and 2014, using the method described in Pardo-Pascual et al. (2012), have been used. It has been shown that the use of annual mean shorelines enables the same rate of change to be obtained as when using all the shorelines, but that the data is simpler to manage and more useful when visualising local changes. It has also been shown that annual mean shorelines largely remove the short-term variability, and are therefore useful for analysing mid-term trend quantifications. In addition, we propose a methodology for annual mean shorelines, obtained from Landsat imagery, that minimises the effects of sea-level variation on the shoreline positions. Both shorelines – instantaneous and mean annual – appear to be about 4 or 5 m seaward from those obtained using more precise sources.
Using street based metrics to characterize urban typologies
(Elsevier, 2014-03) Hermosilla, T.; Palomar Vázquez, Jesús Manuel; Balaguer Beser, Ángel Antonio; Balsa Barreiro, José; Ruiz Fernández, Luis Ángel; Dpto. de Matemática Aplicada; Dpto. de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Grupo de Cartografía Geoambiental y Teledetección; Ministerio de Ciencia e Innovación
[EN] Urban spatial structures reflect local particularities produced during the development of a city. High spatial resolution imagery and LiDAR data are currently used to derive numerical attributes to describe in detail intra-urban structures and morphologies. Urban block boundaries have been frequently used to define the units for extracting metrics from remotely sensed data. In this paper, we propose to complement these metrics with a set of novel descriptors of the streets surrounding the urban blocks under consideration. These metrics numerically describe geometrical properties in addition to other distinctive aspects, such as presence and properties of vegetation and the relationship between the streets and buildings. For this purpose, we also introduce a methodology for partitioning the street area related to an urban block into polygons from which the street urban metrics are derived. We achieve the assessment of these metrics through application of a one-way ANOVA procedure, the winnowing technique, and a decision tree classifier. Our results suggest that street metrics, and particularly those describing the street geometry, are suitable for enhancing the discrimination of complex urban typologies and help to reduce the confusion between certain typologies. The overall classification accuracy increased from 72.7% to 81.1% after the addition street of descriptors. The results of this study demonstrate the usefulness of these metrics for describing street properties and complementing information derived from urban blocks to improve the description of urban areas. Street metrics are of particular use for the characterization of urban typologies and to study the dynamics of cities.
Description and validation of a new set of object-based temporal geostatistical features for land-use/land-cover change detection
(Elsevier, 2016-11) Gil Yepes, José Luis; Ruiz Fernández, Luis Ángel; Recio Recio, Jorge Abel; Balaguer Beser, Ángel Antonio; Hermosilla Gómez, Txomin; Dpto. de Matemática Aplicada; Dpto. de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Grupo de Cartografía Geoambiental y Teledetección; Generalitat Valenciana; Comunidad Autónoma de la Región de Murcia
[EN] A new set of temporal features derived from codispersion and cross-semivariogram geostatistical functions is proposed, described, extracted, and evaluated for object-based land-use/land-cover change detection using high resolution images. Five features were extracted from the codispersion function and another six from the cross-semivariogram. The set of features describes the temporal behaviour of the internal structure of the image objects defined in a cadastral database. The set of extracted features was combined with spectral information and a feature selection study was performed using forward stepwise discriminant analysis, principal component analysis, as well as correlation and feature interpretation analysis. The temporal feature set was validated using high resolution aerial images from an agricultural area located in south-east Spain, in order to solve a tree crop change detection problem. Direct classification using decision tree classifier was used as change detection method. Different classifications were performed comparing various feature group combinations in order to obtain the most suitable features for this study. Results showed that the new sets of cross-semivariogram and codispersion features provided high global accuracy classification results (83.55% and 85.71% respectively), showing high potential for detecting changes related to the internal structure of agricultural tree crop parcels. A significant increase in accuracy value was obtained when combining features from both groups with spectral information (94.59%).
Non-linear fourth-order image interpolation for subpixel edge detection and localization
(Elsevier, 2008-09-01) Hermosilla, T.; Bermejo, E.; Balaguer Beser, Ángel Antonio; Ruiz Fernández, Luis Ángel; Dpto. de Matemática Aplicada; Dpto. de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Grupo de Cartografía Geoambiental y Teledetección; Ministerio de Educación y Ciencia
[EN] A fourth-order non-linear interpolation procedure based on the ENO (Essentially Non-Oscillatory) methodology is presented and evaluated, with the purpose of increasing the geometric accuracy of edge detection in digital images. Two possible cases are considered one that considers that each pixel of the image represents a point value, the other that the pixel is an average value of a function. After image interpolation to obtain a finer grid of pixels, the Canny edge detection algorithm is applied, with the objective of improving the localization and geometry of the edges at a subpixel level. The results are compared with other schemes based on fourth order two-dimensional interpolation methods, such as a centered scheme based on a cubic convolution, a fourth order non-centered lineal scheme and a centered cubic convolution based on local gradient features. The evaluation is performed using visual and analytical techniques applied over aerial and satellite images, analyzing the positional errors of the detected edges, as well as the errors due to changes in scale and orientation. In addition to the subpixel edge detection, the quality of the interpolated images is tested. We conclude that the proposed methodology based on ENO interpolation improves the detection of edges in images as compared to other fourth-order methods
Assessing the accuracy of automatically extracted shorelines on microtidal beaches from Landsat 7, Landsat 8 and Sentinel-2 imagery
(MDPI AG, 2018) Pardo Pascual, Josep Eliseu; Sánchez García, Elena; Almonacid Caballer, Jaime; Palomar Vázquez, Jesús Manuel; Priego de los Santos, Jose Enrique; Fernández Sarriá, Alfonso; Balaguer Beser, Ángel Antonio; Dpto. de Matemática Aplicada; Dpto. de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Instituto Universitario de Restauración del Patrimonio; Grupo de Cartografía Geoambiental y Teledetección; Ministerio de Educación; Ministerio de Economía, Industria y Competitividad
[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).