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

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

An Efficient Tool for Automatic Delimitation of Moroccan Coastal Upwelling Using SST Images

International audienceAn unsupervised classification method is developed for the coarse segmentation of Moroccan coastal upwelling using the Sea Surface Temperature (SST) satellite images. The algorithm is started with the generation of c-partitioned labeled image using Otsu's method for the purpose of finding regions of homogenous temperatures. Then two well-known validity indices are used to select the c-partition which best reproduce the shape of upwelling area. A region-growing algorithm is developed that is used to remove the noisy structures in the offshore waters not belonging to the upwelling area. The algorithm is used to provide a seasonal variability of upwelling activity in the southern Moroccan Atlantic coast using 70 SST images of the years 2007 and 2008. The performance of the proposed methodology has been validated by an oceanographer, showing its effectiveness for automatic delimitation of Moroccan upwelling region

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

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

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

A SIMPLE AND EFFICIENT APPROACH FOR COARSE SEGMENTATION OF MOROCCAN COASTAL UPWELLING

International audienceIn this work, we aim to develop a simple and fast algorithm using conventional methods in images segmentation for the automatic detection and extraction of upwelling areas, in the coastal region of Morocco, from the sea surface temperature (SST) satellite images. Our approach is based on the evalua- tion and comparison between two unsupervised classification methods, Otsu and Fuzzy C-means, and explores the appli- cability of these methods to our classification problem. The latter consists in coarse detection of the main thermal front that separates coastal cold upwelling waters from the remain- ing ocean waters. The algorithm has been applied and val- idated by an oceanographer over a database of 66 SST im- ages corresponding to southern Moroccan coastal upwelling of the years 2004, 2005, 2007 and 2009. The results indicate that the proposed algorithm revealed is promising and reli- able on different upwelling scenarios and for a wide variety of oceanographic conditions

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

Detection of Moroccan Coastal Upwelling in SST images using the Expectation-Maximization

International audienceThis paper proposes an unsupervised algorithm for automatic detection and segmentation of upwelling region in Moroccan Atlantic coast using the Sea Surface Temperature (SST) satellite images. This has been done by exploring the Expectation-Maximization algorithm. The good number of clus- ters that best reproduces the shape of upwelling areas is selected by using the two popular Davies-Bouldin and Dunn indices. Area opening technique is developed that is used to remove and discarded the residuals noise in offshore waters not belonging to the upwelling region. The complete system has been validated by an oceanographer using a database of 30 SST images of the year 2007, demonstrating its capability and robustness for precise detection of Moroccan coastal upwelling

Forecasts of desert locust presence in Morocco coupling remote sensing imagery and field surveys

International audienceWith the objective of improving preventive management of desert locust, an operational system was developed to help in the planning of field surveys in Morocco. This operational system produce regularly some presence probability maps of solitarious or transiens desert locust. The spatial resolution is 25km over the Moroccan territory and the temporal horizon of the forecasts are 40 days. The forecasts are based on statistical models coupling historical data of field surveys with several layers of remote sensing imagery. These images are proxy of environmental variables important for desert locust: temperature, rainfall and vegetation availability. The statistical coupling was realised with random forest models. These models were assessed with a splitting of the data to evaluate the forecast errors and validate the approach. An automatic process was also developed to transform new remote sensing imagery into probability maps in order to operationalize the system. As the system has been running for over 3 years, another level of evaluation can be presented: the correspondence between the forecasts of probability of locust presence and the actual observations of field survey teams of the national anti-locust centre of Morocco since 2015