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.

Is sustainable development a motor or a constraint for the professionalization of the pearl oyster industry in tahiti?

This article reports the results of a survey of the pearl oyster industry in French Polynesia territory. Its purpose is to examine the perceptions of the priorities for the development of this industry towards sustainable development. These perceptions were apprehended by a survey of pearl oyster farmers and other stakeholders of the sector (management authorities, scientists). After describing the methodological protocol of these investigations, it comes to confront the priorities chosen by professionals (i.e. pearl farmers) concerning sustainable development, with the perceptions of others stakeholders in the sector. Secondly it comes to build a typology of the priorities of pearl farmers concerning sustainable development. This analysis enables the assessment of the degree of convergence within the sector, which is the base material for defining a shared action plan at the territory scale. This is the first study compiling data of surveys of various professionals and stakeholders of the pearl farming industry in such a large area in French Polynesia.

Projet DMX2 - Volet Croissance

Cette étude correspond au volet « croissance » du projet ANCRE-DMX2. Six espèces ciblées parmi les principales espèces démersales débarquées par la petite pêche réunionnaise ont été étudiées afin d’établir leur modèle de croissance, dont certaines pour la première fois. Ce travail présente les résultats de la croissance, acquis à partir de pièces calcifiées (otolithe, écaille, opercule). Des tests préalables des différentes techniques (observation in toto, otolithe brûlé, otolithe cassé-brûlé, coupe fine) sur les principales pièces calcifiées (otolithes, écailles et opercules) ont été menés, afin d’appliquer la méthode de traitement la mieux adaptée à chaque espèce. Les paramètres morphométriques de l’otolithe ont été mesurés (Poids : Ow, Longueur : OLong, Largeur : Olarg, Surface : Osurf). Pour chaque individu des 6 espèces de grands fonds retenues, l’appartenance à un groupe d’âge a été estimée pour établir les premiers modèles de croissance. Les paramètres de croissance (L¥ et k) ont été estimés selon le modèle de Von Bertalanffy (1938). L’indice de performance de croissance (F ; Pauly & Munro 1984) est utilisé pour comparer la croissance entre les différentes populations selon les zones géographiques, d’une part, d'une même espèce, et d’autre part, entre plusieurs espèces.