Automated computational delimitation of SST upwelling areas using fuzzy clustering

Nascimento, Susana; Franco, Pedro; Sousa, Fátima; Dias, Joaquim; Neves, F.

doi:10.1016/j.cageo.2011.10.025

Cited by 28 publications

(31 citation statements)

References 19 publications

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“…They are, for example, the histogram-based separation [3], where the bimodality of SST histogram is interpreted to represent two populations of water masses (cold and warm waters), and the neural networks approach [7], which classifies SST images using a feed-forward backpropagation neural network in order to find regions of homogeneous and uniform temperatures. Another class of methods deals directly with the temperature values in SST images by using the fuzzy clustering techniques [5,6,8]. The latter has demonstrated an effectiveness to detect the thermal boundaries in SST images, based on the fact that infrared satellite images are imprecise in nature and the associated frontal boundaries are often related to the smooth transitions between the cold and warm waters [5].…”

Section: Introductionmentioning

confidence: 99%

“…The proliferation and rapid expansion of infrared satellite data has allowed researchers to develop automatic methods for detecting and analyzing the upwelling fronts, in order to deal with the huge amount of satellite data daily collected and analyzed by the oceanographers. The sea surface temperature (SST) satellite images obtained from the Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the NOAA satellite, are frequently used to localise the upwelling boundaries between the cold upwelling waters near the coast and warmer offshore waters [4][5][6]. These thermal boundaries on SST images, are usually associated to the weak or strong horizontal gradient in response to the interaction of a complex set of cold and warm waters.…”

Section: Introductionmentioning

confidence: 99%

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

Tamim

Minaoui

Daoudi

et al. 2014

Advances in Visual Computing

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Abstract. This 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 temperature, 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 developed 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tamim

Minaoui

Daoudi

et al. 2014

Advances in Visual Computing

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“…Finally, the Portuguese shelf is influenced by a complex current system and seasonal upwelling events that affect productivity and summer and winter Sea Surface Temperatures (SSTs) (Fiúza, 1983;Nascimento et al, 2012). This favors the mixing of subtropical warm and northern cold waters in the Portuguese continental shelf.…”

Section: Environmental-biological Relationshipsmentioning

confidence: 99%

Diversity, distribution and ecology of benthic molluscan communities on the Portuguese continental shelf

Martins

Sampaio

Quintino

et al. 2014

Journal of Sea Research

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The diversity, ecology and distribution patterns of the Portuguese continental shelf malacofauna and its relationship with abiotic factors were studied from samples covering the western and the southern coast. A total of 2544 specimens were identified corresponding to 169 taxa, mostly bivalves (62% of the total taxa). Abra alba was the most abundant and the most frequent species. The alpha diversity ranged from one species to 21 spp. 0.1 m −2. The highest abundance and diversity were obtained in coarser sediments. Multivariate analysis based on the abundance data identified five major malacological groups: (a) Angulus pygmaeus and Thracia villosiuscula in the coarser sediments of the western inner and mid shelf; (b) Calyptraea chinensis and Leptochiton cancellatus in the heterogeneous and organically enriched sediments of the southern shelf; (c) Angulus fabula, Spisula subtruncata and Pharus legumen in the near shore exposed fine sands; (d) A. alba in muddy fine sands, mainly in the northwestern shelf and (e) Saccella commutata in the southwestern deeper shelf. The malacofauna could be used as a proxy for the major benthic communities known to occur in this area, except in muddy patches, where molluscs were absent or low abundant. Median grain-size, gravel content, depth and hydrodynamic regime were the environmental factors best related to the malacofauna spatial distribution patterns. This study sets the first record of Astarte borealis, Leptochiton asellus, Mercenaria mercenaria and Montacuta phascolionis in the Portuguese shelf and the most northern limit for Anadara polii, Glycymeris nummaria, and Leptochiton algesirensis along the northwestern shelf. This study also gives new ecological insights for several species, in terms of bathymetric range distribution, as well as habitat type and highlighted the transitional characteristics of the molluscan communities from this particular northeastern Atlantic area where boreal, temperate and subtropical faunas can coexist.

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“…In particular, the Sea Surface Temperature (SST) images obtained from Advanced Very High Resolution Radiometer (AVHRR) are frequently used to detect and study the main thermal upwelling front, separating the cold waters near the coast and warmer offshore waters [14].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the most popular approaches include the use of the neural networks [6] in order to labeling the SST images. Then, a statistical coefficient were used to determine the existence of upwelling, but approaches based on histogram analysis [13] [3] and Fuzzy c-means [14] have also been developed.…”

Section: Introductionmentioning

confidence: 99%

A simple tool for automatic extraction of Moroccan coastal upwelling from Sea Surface Temperature images

Tamim

Minaoui

Daoudi

et al. 2014

2014 9th International Conference on Intelligent Systems: Theories and Applications (SITA-14)

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Automated computational delimitation of SST upwelling areas using fuzzy clustering

Cited by 28 publications

References 19 publications

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

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

Diversity, distribution and ecology of benthic molluscan communities on the Portuguese continental shelf

A simple tool for automatic extraction of Moroccan coastal upwelling from Sea Surface Temperature images

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