A web-based support system for biometeorological research.

Data are the fundamental building blocks to conduct scientific studies that seek to understand natural phenomena in space and time. The notion of data processing is ubiquitous and nearly operates in any project that requires gaining insight from the data. The increasing availability of information sources, data formats and download services offered to the users, makes it difficult to reuse or exploit the potential of those new resources in multiple scientific fields. In this paper, we present a spatial extract-transform-load (spatial-ETL) approach for downloading atmospheric datasets in order to produce new biometeorological indices and expose them publicly for reuse in research studies. The technologies and processes involved in our work are clearly defined in a context where the GDAL library and the Python programming language are key elements for the development and implementation of the geoprocessing tools. Since the National Oceanic and Atmospheric Administration (NOAA) is the source of information, the ETL process is executed each time this service publishes an updated atmospheric prediction model, thus obtaining different forecasts for spatial and temporal analyses. As a result, we present a web application intended for downloading these newly created datasets after processing, and visualising interactive web maps with the outcomes resulting from a number of geoprocessing tasks. We also elaborate on all functions and technologies used for the design of those processes, with emphasis on the optimisation of the resources as implemented in cloud services.

A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes.

In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model, we compare the results achieved with a second-order polynomial model, which is the most widely used regression model for characterization purposes. We applied the methodology on a set of raw images of rock art scenes collected with two different Single Lens Reflex (SLR) cameras. A leave-one-out cross-validation (LOOCV) procedure was used to assess the predictive performance of the models in terms of CIE XYZ residuals and Δ E a b * color differences. Values of less than 3 CIELAB units were achieved for Δ E a b * . The output sRGB characterized images show that both regression models are suitable for practical applications in cultural heritage documentation. However, the results show that colorimetric characterization based on the Gaussian process provides significantly better results, with lower values for residuals and Δ E a b * . We also analyzed the induced noise into the output image after applying the camera characterization. As the noise depends on the specific camera, proper camera selection is essential for the photogrammetric work.

Low-Cost Smartphone-Based Photogrammetry for the Analysis of Cranial Deformation in Infants.

BACKGROUND: Cranial deformation, including deformational plagiocephaly, brachycephaly, and craniosynostosis, is a condition that affects a large number of infants. Despite its prevalence, there are no standards for the systematic evaluation of the cranial deformation. Usually, the deformation is measured manually by the use of calipers. Experts, however, do not agree on the suitability of these measurements to correctly represent the deformation. Other methodologies for evaluation include 3-dimensional (3D) photography and radiologic scanners. These techniques require either patient's sedation and ionizing radiation or high investment. The aim of this study is to develop a novel, low-cost, and minimally invasive methodology to correctly evaluate the cranial deformation using 3D imagery. METHODS: A smart phone was used to record a slow motion video sequence on 5 different patients. Then, the videos were processed to create accurate 3D models of the patients' head, and the results were compared with the measurements obtained by the manual caliper. RESULTS: The correspondence between the manual and the photogrammetric 3D model measurements was high as far as head marks are available, with differences of 2 mm ± 0.9 mm; without marks, measurement results differed up to 20 mm. CONCLUSIONS: Smartphone-based photogrammetry is a low-cost, highly useful methodology to evaluate cranial deformation. This technique provides a much larger quantity of information than linear measurements with a similar accuracy as far as head marks exist. In addition, a new approach for the evaluation is pointed out: the comparison between the head 3D model and an ideal head, represented by a 3-axis ellipsoid.

Grey level and noise evaluation of a Foveon X3 image sensor: a statistical and experimental approach.

Radiometric values on digital imagery are affected by several sources of uncertainty. A practical, comprehensive and flexible procedure to analyze the radiometric values and the uncertainty effects due to the camera sensor system is described in this paper. The procedure is performed on the grey level output signal using image raw units with digital numbers ranging from 0 to 2(12)-1. The procedure is entirely based on statistical and experimental techniques. Design of Experiments (DoE) for Linear Models (LM) are derived to analyze the radiometric values and estimate the uncertainty. The presented linear model integrates all the individual sensor noise sources in one global component and characterizes the radiometric values and the uncertainty effects according to the influential factors such as the scene reflectance, wavelength range and time. The experiments are carried out under laboratory conditions to minimize the rest of uncertainty sources that might affect the radiometric values. It is confirmed the flexibility of the procedure to model and characterize the radiometric values, as well as to determine the behaviour of two phenomena when dealing with image sensors: the noise of a single image and the stability (trend and noise) of a sequence of images.

Relación estadística entre metales pesados y propiedades de suelos de cultivos regados con aguas residuales no depuradas / Statiscal relation between heavy metals and properties of soils irrigated with waste water

Los suelos de la zona arrocera situada al sureste de la Ciudad de Valencia, España, son irrigados con aguas procedentes de la Acequia de Favara, mediante derivación del Río Turia, en cuyo cauce se vierte aguas residuales industriales y urbanas. En estos suelos se determinó por métodos analíticos convencionales la concentración de metales pesados totales (Cd, Cr, Cu, Fe, Mo, Mn, Ni Pb y Zn) y se estudió su relación con diversas propiedades edáficas mediante un análisis estadístico multivariante. Los resultados analíticos indican que los suelos poseen una elevada capacidad de intercambio catiónico, alto contenido en materia orgánica y pH alcalino. El contenido medio de metales pesados totales presentó la secuencia Fe>Mn>Zn>Cu>Cr>Pb>Ni>>Mo>Cd. El estudio estadístico reportó una manifiesta correlación entre la arcilla, limo, materia, orgánica, pH, CE, Fe y Mn, y la concentración de metales pesados totales presentes. La concentración total media de Zn, Pb, Cr, NI, Mo y Cu en los suelos, y la de Cd, Cr, Cu, Pb, Mo, Mn y Ni en las aguas de riego superan los límites señalados en su evaluación, por lo que en ambos casos se manifiestan riesgos potenciales de contaminación por fitotoxicidad al cultivo del arroz.