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Roa-Pascuali L., Demarcq Hervé, Nieblas A. E. (2015). Detection of mesoscale thermal fronts from 4 km data using smoothing techniques : gradient-based fronts classification and basin scale application. Remote Sensing of Environment, 164, 225-237. ISSN 0034-4257

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Lien direct chez l'éditeur doi:10.1016/j.rse.2015.03.030

Detection of mesoscale thermal fronts from 4 km data using smoothing techniques : gradient-based fronts classification and basin scale application
Année de publication2015
Type de documentArticle référencé dans le Web of Science WOS:000356554600019
AuteursRoa-Pascuali L., Demarcq Hervé, Nieblas A. E.
SourceRemote Sensing of Environment, 2015, 164, p. 225-237. ISSN 0034-4257
RésuméIn 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 x 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 resolution are compared. Fronts are divided into 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 x 3, 5 x 5, 7 x 7, and 9 x 9 pixels) and three detection window sizes (16 x 16,24 x 24 and 32 x 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 x 16 pixel window size, and a 5 x 5 pixel kernel for strong fronts and a 7 x 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 x 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 achieved 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 x 3 convolution) in terms of detectability, length and location. This method is easily applicable to large regions or at the global scale, with far less constraints of data manipulation and processing time using 4 km data relative to 1 km data.
Plan de classementTélédétection [126] ; Limnologie physique / Océanographie physique [032] ; Sciences fondamentales / Techniques d'analyse et de recherche [020]
LocalisationFonds IRD [F B010064731]
Identifiant IRDfdi:010064731
Lien permanenthttp://www.documentation.ird.fr/hor/fdi:010064731

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