La pêche du vivaneau rouge en Guyane. Un bilan de l'exploitation sous le régime vénézuélien, des techniques de capture à adapter et à développer

En Guyane française , le vivaneau rouge (Lutjanus purpureus) est capturé par 3 flottilles, les ligneurs vénézuéliens, les caseyeurs antillais et les chalutiers crevettiers guyanais. Pour les crevettiers, il s'agit d'une capture accessoire inévitable, mais qui ne semble pas sans conséquences, puisque, si l'on tient compte de l'effectif total de la flottille, c'est 1 million à 1.5 millions de juvéniles qui sont pêchés (et souvent rejetés à la mer) annuellement par les crevettiers. Pour les ligneurs vénézuéliens qui pêchent 1200 tonnes, les individus de petite taille sont devenus prépondérants dans leurs captures. Ainsi dans la gamme de taille 20-30cm (125-425 grammes), on est passé, entre 1990 et 1998, de 37 000 poissons débarqués (6% de la capture) à 616 500 poissons (56% de la capture). La taille moyenne du vivaneau rouge débarqué est passée de 45 à 35 cm et son poids moyen de 1600 grammes à 700 grammes. Pour les caseyeurs, seuls deux armements (un du Larivot, l'autre du Robert), nous ont fourni quelques renseignements sur les activités et les débarquements de leurs navires. Les premières observations montrent que la composition de leurs captures en vivaneaux rouges ressemble à celle des ligneurs avec une tendance vers les petites tailles. Cependant cette tendance n'est pas aussi systématique que veulent bien le dire les détracteurs de la nasse à poissons. Leurs débarquements sont composés en nombre, pour moitié, de "vivaneaux tête ronde" (Rhomboplites aurorubens). On notera également que les caseyeurs rentabilisent leurs captures accessoires de mérous sur le marché antillais, alors que les ligneurs les rapatrient vers le Venezuela. Il existe une troisième espèce de vivaneau, le vivaneau rayé, Lutjanus synagris, capturé surtout par les chalutiers.

Detection of mesoscale thermal fronts from 4km data using smoothing techniques: Gradient-based fronts classification and basin scale application

In order to optimize frontal detection in sea surface temperature fields at 4 km resolution, a combined statistical and expert-based approach is applied to test different spatial smoothing of the data prior to the detection process. Fronts are usually detected at 1 km resolution using the histogram-based, single image edge detection (SIED) algorithm developed by Cayula and Cornillon in 1992, with a standard preliminary smoothing using a median filter and a 3 × 3 pixel kernel. Here, detections are performed in three study regions (off Morocco, the Mozambique Channel, and north-western Australia) and across the Indian Ocean basin using the combination of multiple windows (CMW) method developed by Nieto, Demarcq and McClatchie in 2012 which improves on the original Cayula and Cornillon algorithm. Detections at 4 km and 1 km of resolution are compared. Fronts are divided in two intensity classes (“weak” and “strong”) according to their thermal gradient. A preliminary smoothing is applied prior to the detection using different convolutions: three type of filters (median, average and Gaussian) combined with four kernel sizes (3 × 3, 5 × 5, 7 × 7, and 9 × 9 pixels) and three detection window sizes (16 × 16, 24 × 24 and 32 × 32 pixels) to test the effect of these smoothing combinations on reducing the background noise of the data and therefore on improving the frontal detection. The performance of the combinations on 4 km data are evaluated using two criteria: detection efficiency and front length. We find that the optimal combination of preliminary smoothing parameters in enhancing detection efficiency and preserving front length includes a median filter, a 16 × 16 pixel window size, and a 5 × 5 pixel kernel for strong fronts and a 7 × 7 pixel kernel for weak fronts. Results show an improvement in detection performance (from largest to smallest window size) of 71% for strong fronts and 120% for weak fronts. Despite the small window used (16 × 16 pixels), the length of the fronts has been preserved relative to that found with 1 km data. This optimal preliminary smoothing and the CMW detection algorithm on 4 km sea surface temperature data are then used to describe the spatial distribution of the monthly frequencies of occurrence for both strong and weak fronts across the Indian Ocean basin. In general strong fronts are observed in coastal areas whereas weak fronts, with some seasonal exceptions, are mainly located in the open ocean. This study shows that adequate noise reduction done by a preliminary smoothing of the data considerably improves the frontal detection efficiency as well as the global quality of the results. Consequently, the use of 4 km data enables frontal detections similar to 1 km data (using a standard median 3 × 3 convolution) in terms of detectability, length and location. This method, using 4 km data is easily applicable to large regions or at the global scale with far less constraints of data manipulation and processing time relative to 1 km data.

Reconstructability of 3-Dimensional Upper Ocean Circulation from SWOT Sea Surface Height Measurements

Utilizing the framework of effective surface quasi-geostrophic (eSQG) theory, we explored the potential of reconstructing the 3D upper ocean circulation structures, including the balanced vertical velocity (w) field, from high-resolution sea surface height (SSH) data of the planned SWOT satellite mission. Specifically, we utilized the 1/30°, submesoscale-resolving, OFES model output and subjected it through the SWOT simulator that generates the along-swath SSH data with expected measurement errors. Focusing on the Kuroshio Extension region in the North Pacific where regional Rossby numbers range from 0.22 to 0.32, we found that the eSQG dynamics constitutes an effective framework for reconstructing the 3D upper ocean circulation field. Using the modeled SSH data as input, the eSQG-reconstructed relative vorticity (ζ) and w fields are found to reach a correlation of 0.7–0.9 and 0.6–0.7, respectively, in the 1,000m upper ocean when compared to the original model output. Degradation due to the SWOT sampling and measurement errors in the input SSH data for the ζ and w reconstructions is found to be moderate, 5–25% for the 3D ζ field and 15-35% for the 3D w field. There exists a tendency for this degradation ratio to decrease in regions where the regional eddy variability (or Rossby number) increases.