Upwelling Detection in SST Images Using Fuzzy Clustering with Adaptive Cluster Merging

International audienceThe current paper explores the applicability of the Fuzzy c-means (FCM) clustering, using an adaptive cluster merging, for the problem of detecting the Moroccan coastal upwelling areas in Sea Surface Temperature (SST) Satellite images. The process is started with the application of FCM clustering method to the SST image with a sufficiently large number of clusters for the purpose of labelling the original SST image, which constitute the input of the proposed approach. Then, the number of clusters is reduced successively by merging clusters that are similar with respect to an adaptive threshold criterion. The algorithm is applied and validated using the visual inspection carried out by an oceanographer over a database of 30 SST images, covering the southern Moroccan atlantic coast of the year 2007. The proposed methodology is shown to be promising and reliable for a majority of images used in this study

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

Absence of the Great Whirl giant ocean vortex abates productivity in the Somali upwelling region

Somali upwelling is the fifth largest upwelling globally with high productivity, attracting tuna migratory species. A key control on the upwelling productivity is its interaction with one of the world’s largest oceanic eddies, the Great Whirl inducing a strong downwelling signal. Here, we use satellite-derived observations to determine the Great Whirl impact on the extent of the upwelling-driven phytoplankton bloom. We find that following decreases in upwelling intensity, productivity has declined by about 10% over the past two decades. The bloom extent has also been diminishing with an abrupt decrease around 2006–2007, coinciding with an abrupt increase in the downwelling effect. Absent or weak Great Whirl leads to the occurrence of smaller anticyclonic eddies with a resulting downwelling stronger than when the Great Whirl is present. We suggest that 2006–2007 abrupt changes in the bloom and downwelling extents’ regimes, are likely driven by Indian Ocean Dipole abrupt shift in 2006

Fuzzy clustering não supervisionado na detecção automática de regiões de upwelling a partir de mapas de temperatura da superfície oceânica

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia InformáticaO afloramento costeiro (upwelling) ao largo da costa de Portugal Continental é um fenómeno bem estudado na literatura oceanográfica. No entanto, existem poucos trabalhos na literatura científica sobre a sua detecção automática, em particular utilizando técnicas de clustering. Algoritmos de agrupamento difuso (fuzzy clustering) têm sido bastante explorados na área de detecção remota e segmentação de imagem, e investigação recente mostrou que essas técnicas conseguem resultados promissores na detecção do upwelling a partir de mapas de temperatura da superfície do oceano, obtidos por imagens de satélite. No trabalho a desenvolver nesta dissertação, propõe-se definir um método que consiga identificar automaticamente a região que define o fenómeno. Como objecto de estudo, foram analisados dois conjuntos independentes de mapas de temperatura, num total de 61 mapas, cobrindo a diversidade de cenários em que o upwelling ocorre. Focando o domínio do problema, foi desenvolvido trabalho de pesquisa bibliográfica ao nível de literatura de referência e estudos mais recentes, principalmente sobre os temas de técnicas de agrupamento, agrupamento difuso e a sua aplicação à segmentação de imagem. Com base num dos algoritmos com mais influência na literatura, o Fuzzy c-means (FCM), foi desenvolvida uma nova abordagem, utilizando o método de inicialização ‘Anomalous Pattern’, que tenta resolver dois problemas base do FCM: a validação do melhor número de clusters e a dependência da inicialização aleatória. Após um estudo das condições de paragem do novo algoritmo, AP-FCM, estabeleceu-se uma parametrização que determina automaticamente um bom número de clusters. Análise aos resultados obtidos mostra que as segmentações geradas são de qualidade elevada, reproduzindo fidedignamente as estruturas presentes nos mapas originais, e que, computacionalmente, o AP-FCM é mais eficiente que o FCM. Foi ainda implementado um outro algoritmo, com base numa técnica de Histogram Thresholding, que, obtendo também boas segmentações, não permite uma parametrização para a definição automática do número de grupos. A partir das segmentações obtidas, foi desenvolvido um módulo de definição de features, a partir das quais se criou um critério composto que permite a identificação automática do cluster que delimita a região de upwelling

Sequential Extraction Thresholding Clustering for Segmentation of Coastal Upwelling on Sea Surface Temperature Images

Coastal upwelling is a process when cold and nutrient-rich water dynamically appears over the surface of the ocean by replacing the warm water. The oceanographers are interested to detect the upwelling regions and corresponding boundaries but to examine the whole process of upwelling they have to work manually on each image, therefore; it increases the workload. The main purpose of this application is to automatically detect the upwelling regions, monitoring environmental changes and the study of fishery resources. The Seed Expanding Clustering algorithm (SEC) (Nascimento et al., 2015) is a thresholding clustering method for automatic detection of upwelling and delineation of its fronts. The self‐tuning thresholding is derived from the clustering criterion and serves as a boundary regularizer of the growing clusters. The SEC algorithm is shown more than 80% of accuracy rate on the unsupervised automatic recognition of the phenomenon. The main contribution of this dissertation is threefold. First, the development of a sequential extraction version of the SEC algorithm with a stop condition that takes advantage of the knowledge domain to select seeds and model extracted features. Second, the development of an explosion control procedure to detect the so-called leakage problem. Third, the development of a fusion scheme of unsupervised clustering validation measures. The experimental comparison of the new iterative version of the SEC algorithm with a new developed iterative version of Adams & Bischof SRG on the unsupervised segmentation of upwelling regions on SST images from different regions of the globe show their competitiveness comparing to other conventional SRG methods