Extraction of Knowledge Rules for the Retrieval of Mesoscale Oceanic Structures in Ocean Satellite Images

The processing of ocean satellite images has as goal the detection of phenomena related with ocean dynamics. In this context, Mesoscale Oceanic Structures (MOS) play an essential role. In this chapter we will present the tool developed in our group in order to extract knowledge rules for the retrieval of MOS in ocean satellite images. We will describe the implementation of the tool: the workflow associated with the tool, the user interface, the class structure, and the database of the tool. Additionally, the experimental results obtained with the tool in terms of fuzzy knowledge rules as well as labeled structures with these rules are shown. These results have been obtained with the tool analyzing chlorophyll and temperature images of the Canary Islands and North West African coast captured by the SeaWiFS and MODIS-Aqua sensors

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

MEC: A Mesoscale events classifier for oceanographic imagery

The observation of the sea through remote sensing technologies plays a fundamentalan role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT’s Metop and NASA’s Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula.info:eu-repo/semantics/publishedVersio

Automatic Detection of Moroccan Coastal Upwelling Zones using Sea Surface Temperature Images

International audienceAn efficient unsupervised method is developed for automatic segmentation of the area covered by upwelling waters in the coastal ocean of Morocco using the Sea Surface Temperature (SST) satellite images. The proposed approach first uses the two popular unsupervised clustering techniques, k-means and fuzzy c-means (FCM), to provide different possible classifications to each SST image. Then several cluster validity indices are combined in order to determine the optimal number of clusters, followed by a cluster fusion scheme, which merges consecutive clusters to produce a first segmentation of upwelling area. The region-growing algorithm is then used to filter noisy residuals and to extract the final upwelling region. The performance of our algorithm is compared to a popular algorithm used to detect upwelling regions and is validated by an oceanographer over a database of 92 SST images covering each week of the years 2006 and 2007. The results show that our proposed method outperforms the latter algorithm, in terms of segmentation accuracy and computational efficiency

A Simple Tool for Automatic Extraction of Moroccan Coastal Upwelling from Sea Surface Temperature Images

International audienceThis work aims at automatically identify and ex- tract the region covered by the upwelling waters in the costal ocean of Morocco using the well known region-growing segmen- tation algorithm. The later consists in coarse segmentation of upwelling area which characterized by cold and usually nutrient- rich water near the coast. The complete system has been validated by an oceanographer over a database of 30 Sea Surface Tem- perature (SST) satellite images of the year 2007 obtained from Advanced Very High Resolution Radiometer (AVHRR) sensor onboard NOAA-18 satellite serie, demonstrating its capability and effectiveness to reproduce the shape of upwelling area

On Detectability of Moroccan Coastal Upwelling in Sea Surface Temperature Satellite Images

International audienceThis work aims at automatically identify the upwelling areas in coastal ocean of Morocco using the Sea Surface Temperature (SST) satellite images. This has been done by using the fuzzy clustering technique. The proposed approach is started with the application of Gustafson-Kessel clustering algorithm in order to detect groups in each SST image with homogenous and non-overlapping temper-ature, resulting in a c-partitioned labeled image. Cluster validity indices are used to select the c-partition that best reproduces the shape of upwelling areas. An area opening technique is developed that is used to filter out the residuals noise and fine structures in offshore waters not belonging to the upwelling regions. The de-veloped algorithm is applied and adjusted over a database of 70 SST images from years 2007 and 2008, covering the southern part of Moroccan atlantic coast. The system was evaluated by an oceanographer and provided acceptable results for a wide variety of oceanographic conditions

Detection of Moroccan Coastal Upwelling Fronts in SST Images using the Microcanonical Multiscale Formalism

International audienceNonlinear signal processing using the Microcanonical Multiscale Formalism (MMF) is used to the problem of detecting and extracting the upwelling fronts in coastal region of Morocco using Sea Sur- face Temperature (SST) satellite images. The algorithm makes use of the Singularity Exponents (SE), computed in a microcanonical framework, to detect and analyze the critical transitions in oceano- graphic satellite data. The objective of the proposed study is to develop a helpful preprocessor that transforms SST images into clean and simple line drawing of upwelling fronts as an input to a subse- quent step in the analysis of SST images of the ocean. The method is validated by an oceanographer and it is shown to be superior to that of an automatic algorithm commonly used to locate edges in satellite oceanographic images. The proposed approach is applied over a collection of 92 SST images, covering the southern Moroccan Atlantic coast of the years 2006 and 2007. The results indicate that the approach is promising and reliable for a wide variety of oceanographic conditions

Detection of Moroccan coastal upwelling using sea surface chlorophyll concentration

International audienceThe aim of this work is to automatically identify and extract the upwelling area in the coastal ocean of Morocco using the satellite observation of chlorophyll concentration. The algorithm starts by the application of FCM algorithm for the purpose of finding regions of homogeneous concentration of the chlorophyll, resulting in c-partitioned labeled images. A region­ growing algorithm is then used to filter out the noisy structures in the offshore waters not belonging to the upwelling regions. The proposed methodology has been validated by an oceanographer and tested over a database of 166 weekly Sea Surface chlorophyll data. The region of interst cover the southern part of Moroccan atlantic coast spanning from the years 2007 to 2012

Nadzor raspodjele uljnih mrlja u hrvatskom dijelu Jadranskog mora: potrebe i mogućnosti

A set of spaceborne synthetic aperture radar (SAR) images and geographic information system (GIS) can significantly contribute to monitoring and identification of oil spills floating on the sea surface. Initially, the GIS has been proven as an excellent management tool for resources assessment, oil spill response and planning, and damage assessment, but the possibilities of GIS mapping also integrate geographical, remote sensing, oil & gas production/infrastructure data and slick signatures detected by SAR. Data from different sources such as nautical charts, geo-databases, ground truth and remote sensing data combined in GIS reveal offshore/onshore oil sources, and estimate the intensity and evolution of oil pollution. SAR and GIS together can significantly improve identification and classification of oil spills, leading to the product - oil spill distribution maps. This approach, applied successfully in different water basins, can also be applied to oil spill monitoring/ mapping in Croatian waters of the Adriatic Sea – it can contribute to understanding the spatio-temporal distribution of oil spills in the Adriatic Sea and be an ideal tool for an oil spill monitoring system. In the framework of the Croatian initiative towards regional cooperation in the Adriatic Sea, such an approach represents a good national opportunity. In this paper, the properties of SAR imagery, as the most reliable source of oil spill information, are described. The possibilities of a combined SAR-GIS approach to oil spill monitoring and GIS databases as a management tools for the protection of the Adriatic Sea are discussed.Kombinacija SAR (synthetic aperture radar) satelitskih snimaka i geografskog informacijskog sustava (GIS) može znatno doprinjeti pračenju i prepoznavanju tankog filma ulja koji pliva na površini mora. U početku, GIS se pokazao odličnim sredstvom u gospodarenju resursa i planiranju zaštite od uljnih mrlja, ali mogućnosti GIS-a postoje i u integriranju geografskih informacija o infrastrukturi za preradu/transport nafte sa satelitskim informacijama od SAR snimaka. Podaci različitih izvora kao što su nautičke mape, geografske baze podataka, mjerenja in-situ te podaci daljinskih mjerenja u kombinaciji sa GIS-om mogu otkriti obalne i morske izvore ulja i odrediti intenzitet i razvoj uljnog zagađenja. SAR i GIS zajedno mogu znatno poboljšati identifikaciju i klasifikaciju uljnog zagađenja, dovodeći do produkta – mapa raspodjele uljnih mrlja na Jadranu. Ovaj pristup, uspješno primijenjen u različitim morskim bazenima može se primijeniti i za monitoring i mapiranje u hrvatskim obalnim vodama na Jadranu, a također može doprinjeti razumijevanju prostorno vremenske raspodjele uljnih mrlja na Jadranu te biti idealan alat nacionalnog sustava monitoriga. U okviru hrvatske inicijative prema regionalnoj suradnji na Jadranu ovaj pristup može predstavljati dodatnu mogućnost. U ovom su radu opisane osobine SAR snimaka, kao najpouzdanijeg izvora informacija o uljnim mrljama. Raspravljene su i mogućnosti kombiniranog SAR-GIS pristupa nadzoru uljnih mrlja te baza podataka u GIS-u kao sredstva korisnog u zaštiti Jadrana

Making sense of light: the use of optical spectroscopy techniques in plant sciences and agriculture

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.info:eu-repo/semantics/publishedVersio