Teaching satellite oceanography

Presentamos en este trabajo una serie de consideraciones acerca de la importancia que posee la enseñanza de la Oceanografía por Satélite en diferentes contextos tanto profesionales como académicos. La enseñanza de esta técnica debería ser un requisito universal para todos los científicos y profesionales involucrados en la investigación y aplicaciones comerciales o de gestión del medio marino, independientemente de cuál sea su formación básica. Por supuesto, los requerimientos, programas y metodología a emplear dependerán del contexto, universitario o no, de estas enseñanzas.In this work an analysis about the importance of teaching Satellite Oceanography is presented in order to obtain the major benefit in Oceanography and marine applications from this kind of data. The training of this technique must be an universal requirement for all the researchers and scientifics related with the ocean environment. Specific requirements and subjects are presented for different backgrounds and categories of oceanographers, remote sensing and comercial and protection marine specialist and students of marine science

Reduction of Irrelevant Features in Oceanic Satellite Images by means of Bayesian Networks

This paper describes the use of Bayesian networks for the reduction of irrelevant features [1,2] in the recognition of oceanic structures in satellite images. Bayesian networks are used to validate the symbolic knowledge -provided by neuro symbolic or HLKPs (High Level Knowledge Processors) nets- and the numeric knowledge. This provides an automatic interpretation of images. The main objective of this work is the construction of an automatic recognition system for processing AVHRR (Advanced Very High Resolution Radiometer) images from NOAA (National Oceanographic and Atmospheric Administration) satellites to detect and locate oceanic phenomena of interest like upwellings, eddies and island wakes. With this aim, this paper reports on a methodology of knowledge selection and validation. In knowledge selection, filter measures are used. For knowledge validation, Bayesian networks (Naïve Bayes, TAN and KDB) are evaluated

Procedimiento de interpretación automática de imágenes para la cuantificación de marcadores tumorales nucleares

Número de publicación: 2 481 347 Número de solicitud: 201232033Procedimiento de interpretación automática de imágenes para la cuantificación de marcadores tumorales nucleares donde, partiendo de imágenes tintadas previamente por procedimientos conocidos, se aplican los siguientes pasos: cambio de espacio de color RGB a otro espacio en el que uno de sus canales represente la intensidad luminosa del píxel, extracción del canal correspondiente al valor de intensidad, filtrado de la imagen, ecualización del histograma para aumento del contraste, segmentación basada en regiones y en detección de contornos, suma de las imágenes obtenidas de la segmentación basada en regiones y de la segmentación basada en contornos para obtener una máscara que se aplica a la imagen original para obtener el área de interés compuesta sólo de los núcleos y por último, clasificación de los píxeles. La invención propuesta mejora el flujo de trabajo, la ergonomía, confort y productividad de los expertos en diagnóstico.Universidad de Almerí

MODIS Sensor Capability to Burned Area Mapping—Assessment of Performance and Improvements Provided by the Latest Standard Products in Boreal Regions

This paper presents an accuracy assessment of the main global scale Burned Area (BA) products, derived from daily images of the Moderate-Resolution Imaging Spectroradiometer (MODIS) Fire_CCI 5.1 and MCD64A1 C6, as well as the previous versions of both products (Fire_CCI 4.1 and MCD45A1 C5). The exercise was conducted on the boreal region of Alaska during the period 2000–2017. All the BA polygons registered by the Alaska Fire Service were used as reference data. Both new versions doubled the annual BA estimate compared to the previous versions (66% for Fire_CCI 5.1 versus 35% for v4.1, and 63% for MCD64A1 C6 versus 28% for C5), reducing the omission error (OE) by almost one half (39% versus 67% for Fire_CCI and 48% versus 74% for MCD) and slightly increasing the commission error (CE) (7.5% versus 7% for Fire_CCI and 18% versus 7% for MCD). The Fire_CCI 5.1 product (CE = 7.5%, OE = 39%) presented the best results in terms of positional accuracy with respect to MCD64A1 C6 (CE = 18%, OE = 48%). These results suggest that Fire_CCI 5.1 could be suitable for those users who employ BA standard products in geoinformatics analysis techniques for wildfire management, especially in Boreal regions. The Pareto boundary analysis, performed on an annual basis, showed that there is still a potential theoretical capacity to improve the MODIS sensor-based BA algorithms

OBIA System for Identifying Mesoscale Oceanic Structures in SeaWiFS and MODIS-Aqua Images

The ocean covers over 70% of the surface of our planet and plays a key role in the global climate. Most ocean circulation is mesoscale (scales of 50–500 km and 10–100 days), and the energy in mesoscale circulation is at least one order of magnitude greater than general circulation; therefore, the study of mesoscale oceanic structures (MOS) is crucial to ocean dynamics, making it especially useful for analyzing global changes. The detection of MOS, such as upwellings or eddies, from satellites images is significant for marine environmental studies and coastal resource management. In this paper, we present an object-based image analysis (OBIA) system which segments and classifies regions contained in sea-viewing field-of-view sensor (SeaWiFS) and Moderate Resolution Imaging Spectro-radiometer (MODIS)-Aqua sensor satellite images into MOS. After color clustering and hierarchical data format (HDF) file processing, the OBIA system segments images and extracts image descriptors, producing primary regions. Then, it merges regions, recalculating image descriptors for MOS identification and definition. First, regions are labeled by a human-expert, who identifies MOS: upwellings, eddies, cool, and warm eddies. Labeled regions are then classified by learning algorithms (i.e., decision tree, Bayesian network, artificial neural network, genetic algorithm, and near neighbor algorithm) from selected features. Finally, the OBIA system enables images to be queried from the user interface and retrieved by means of fuzzy descriptors and oceanic structures. We tested our system with images from the Canary Islands and the North West African coast

A location-based approach to the classification of mesoscale oceanic structures in SeaWiFS and Aqua-MODIS images of Northwest Africa

This study presents a different approach to the classification of Mesoscale Oceanic Structures (MOS) present in the Northwest African area, based on their location. The main improvement stems from the partition of this area in four large zones perfectly differentiated by their morphological characteristics, with attention to seafloor topography and coastal relief. This decomposition makes it easier to recognize structures under adverse conditions, basically the presence of clouds partly hiding them. This is observed particularly well in upwellings, which are usually very large structures with a different morphology and genesis in each zone. This approach not only improves the classification of the upwellings, but also makes it possible to analyse changes in the MOS over time, thereby improving the prediction of its morphological evolution. To identify and label the MOS classified in the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Aqua-MODIS (Moderate Resolution Imaging Spectroradiometer) chlorophyll-a and temperature images, we used a tool specifically designed by our group for this purpose and which has again shown its validity in this new proposal

Cálculo de velocidades oceánicas superficiales en el área del afloramiento del NW de Africa mediante imágenes del sensor A VHRR

En el presente estudio, una serie de pares de imágenes consecutivas del sensor Advance Very High Resolution Radiometer separadas entre si 24 horas son utilizadas con el objetivo de deducir velocidades de flujo superficial en el área del afloramiento del NW de África. El método utilizado es el método de las correlaciones cruzadas bidimensionales entre imágenes de satélite sucesivas, que representan el movimiento de las estructuras observadas. Los resultados de aplicar este método son analizados y discutidos