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.

Macro-geographical differences influenced by family-based expression on cultured pearl grade, shape and colour in the black-lip "pearl oyster" Pinctada margaritifera: a preliminary bi-local case study in French Polynesia

In French Polynesia, the aquaculture of P. margaritifera is carried out in numerous grow-out sites, located over three archipelagos (Gambier, Society and Tuamotu). To evaluate the impact of macro-geographical effects of these growing sites on pearl quality traits, five hatcheries produced families were used as homogeneous donor oysters in an experimental graft. The molluscs were then reared in two commercial locations: Tahaa island (Society) and Rangiroa atoll (Tuamotu). At harvest, eight pearl quality traits were recorded and compared: surface defects, lustre, grade, circles, shape categories, darkness level, body and secondary colour and visual colour categories. Overall inter-site comparison revealed that: 1) all traits were affected by grow-out location except for lustre and round shape, and 2) a higher mean rate of valuable pearls was produced in Rangiroa. Indeed, for pearl grade, Rangiroa showed twice as many A-B and less reject samples than Tahaa. This was related to the number of surface defects (grade component): in Rangiroa, twice as many pearls had no defects and less pearls had up to 10 defects. Concerning pearl shape, more circled and baroque pearls were found in Tahaa (+10%). For colour variation, 10% more pearls have an attractive green overtone in Rangiroa than in Tahaa, where more grey bodycolor were harvested. Lustre does not seem to be affected by these two culture site (except at a family scale). This is the first time P. margaritifera donor family have been shown to vary in the quality of pearls they produce depending on their grow-out location.