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

Abstract

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.

An Efficient Detection of Moroccan Coastal Upwelling Based on Fusion of Chlorophyll-a and Sea Surface Temperature Images With a New Validation Index

“…Other studies focused on the problem of the upwelling identification from the SST and Chl-a images. Tamim et al [9]- [12] proposed a clustering-based approach to identify the upwelling areas from the SST images. Recently, El Aouni et al [7] improved the earlier method by introducing a simple normalization to allow these methods to work properly on the other part of the system.…”

Physical and Biological Satellite Observations of the Northwest African Upwelling: Spatial Extent and Dynamics

“…Various techniques have been proposed for the subject of the upwelling detection and delimitation for different upwelling ecosystems. However, none of the previous work has successfully been able to delineate the central part of the Canary Upwelling Ecosystem from the SST images [12]- [14], [24], [25]. The proposed method is capable of extracting upwelling regions from the SST and CHL a images.…”

Convolutional neural networks for coastal upwelling monitoring along the Atlantic Coast of Morocco

“…Their approach starts by segmenting SST images into two classes, upwelling and non upwelling based on a clustering algorithm, then uses a region growing algorithm to extract the upwelling regions. Similarly, the same philosophy is used in Tamim et al (2014c), but this time based on the use of advanced images processing techniques. Authors in Tamim et al (2014b), applied the FCM algorithm on SST images which a large number of clusters, then reduces the latter successively by merging similar clusters that respect a certain adaptive threshold criterion.…”