An Automated Structure for Acquiring and Processing Sentinel-1 Data and its Applicability for Coastal Studies

Abstract:

Article Preview

The present work develops an automated structure to search, download and pre process Synthetic Aperture RADAR imagery acquired by the Sentinel-1 mission. A Python script defines the area and time of interest, performs the search for relevant products and download them using the Application Program Interfaces Hub of the Sentinels Scientific Data Hub. The preprocessing steps are performed using the Graph Processing Tool, which allows for a batch-mode execution of the Sentinel-1 Toolbox operators. Both steps are combined in a Bash program that performs the entire chain daily, without the need of users interaction. We demonstrate applicabilities of the processed data for coastal studies of the Rio Grande do Sul state. The structure is built aiming to 1) increase the usage of the open-access Sentinel-1 SAR data by reducing the processing time and 2) further develop automated detection systems for targets such as oil spills, ships and flooded areas.

Info:

Periodical:

Edited by:

Antonio F. Miguel, Luiz Alberto Oliveira Rocha and Prof. Andreas Öchsner

Pages:

122-131

Citation:

J. Costi et al., "An Automated Structure for Acquiring and Processing Sentinel-1 Data and its Applicability for Coastal Studies", Defect and Diffusion Forum, Vol. 372, pp. 122-131, 2016

Online since:

March 2017

Export:

Price:

$38.00

* - Corresponding Author

[1] H. K. Lotze, H. S. Lenihan, B. J. Bourque, R. H. Bradbury, G. Cooke, M. C. Kay, S. M. Kidwell, M. X. Kirby, C. H. Peterson, and J. B. C. Jackson, Depletion, Degredation, and Recovery Potential of Estuaries and Coastal Seas, Science. 312(2014).

[2] G. McGranahan, D. Balk, and B. Anderson, The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones, Environ. Urban. 19(1) (2007) 17–37.

DOI: https://doi.org/10.1177/0956247807076960

[3] F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave remote sensing: Active and passive. Volume 3 - From theory to applications, Artech House, Norwood, (1986).

[4] L. J. Tomazelli, J. A. Villwock, S. R. Dillenburg, F. A. Bachi, and B. A. Dehnhardt, Significance of Present-Day Coastal Erosion and Marine Transgression, Rio Grande do Sul, Southern Brazil, An. Acad. Bras. Ciencias. 70(2) (1998) 220–229.

[5] L. J. Tomazelli, S. R. Dillenburg, and J. A. Villwock, Late Quaternary Geological History of Rio Grande do Sul Coastal Plain, Southern Brazil, Rev. Bras. Geociências. 30(3) (2000) 474–476.

[6] O. Möller, P. Castaing, J. -Cl. Salomon, and P. Lazure, The Influence of Local and Non-Local Forcing Effects on the Subtidal Circulation of Patos Lagoon, Estuaries. 24(2) (2001) 297–311.

DOI: https://doi.org/10.2307/1352953

[7] W. C. Marques, E. H. L. Fernandes, and L. A. O. Rocha, Straining and advection contributions to the mixing process in the Patos Lagoon estuary, Brazil, J. Geophys. Res. Ocean., vol. 116(3, (2011).

DOI: https://doi.org/10.1029/2010jc006524

[8] P. Potin, ESA Sentinel 1 handbook, European Space Agency technical note. (2013).

[9] A. H. S. Solberg, C. Brekke, and P. O. Husøy, "Oil spill detection in Radarsat and Envisat SAR images, IEEE Trans. Geosci. Remote Sens. 45(3) (2007) 746–754.

DOI: https://doi.org/10.1109/tgrs.2006.887019

[10] D. J. Crisp, The State-of-the-Art in Ship Detection in Synthetic Aperture Radar Imagery Intell., Surveillance and Reconnaissance Div., Inf. Sci. Lab., Def., Sci. Technol. Org., Edinburgh, Australia. (2004).

[11] J. Canny, A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intelligence. 8(6) (1986) 679–698.

DOI: https://doi.org/10.1109/tpami.1986.4767851

[12] R. F. Duarte, Monitoramento das áreas úmidas e inundadas adjacentes ao Canal São Gonçalo com uma série de imagens ERS-1/2 SAR e Envisat ASAR adquiridas entre 1992 e 2007. Master thesis. Post graduation programme in Geography, Federal University of Rio Grande, Rio Grande, Rio Grande do Sul, Brazil. (2013).

DOI: https://doi.org/10.1590/1516-3180.2015.0241030516