Name
Abstract | ||
|---|---|---|
Upwelling filaments are long (≈ 100's km) narrow (O ≈ 10 km) structures in the coastal ocean. They export nutrients and prevent the movement of larvae along the coast. Filaments can be observed in satellite images and in numerical models, but their manual identification and characterization is complex and time consuming. Here we present a Matlab code for a manual method to assist experts in this task, and a code for an automatic filament detection method (AFD) based on image processing and pattern recognition to identify and extract features in output files from a numerical ocean model. AFD was tested with a simulation of northern Chile. AFD had a similar performance in filament detection to that of human experts. AFD provides substantial time savings when analyzing a large number of images from a numerical ocean model. AFD is open source and freely available. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cageo.2018.10.005 | Computers & Geosciences |
Keywords | Field | DocType |
Upwelling filaments,Image processing,Chile,Numerical models,Coastal ocean | Data mining,Satellite,MATLAB,Numerical models,Protein filament,Computer science,Remote sensing,Image processing,Upwelling,Code (cryptography) | Journal |
Volume | ISSN | Citations |
122 | 0098-3004 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| O.E. Artal | 1 | 0 | 0.68 |
| Héctor Hito Sepúlveda | 2 | 25 | 1.52 |
| Domingo Mery | 3 | 466 | 42.09 |
| Christian Pieringer | 4 | 11 | 1.29 |