Abstract
This chapter focuses on how to monitor marine spills using powerful tools such as remote sensing and Geographic Information Systems (GIS). On the one hand, remote sensing has been widely used as one of the main ways to periodically monitor large areas, as it allows to obtain data under poor weather conditions and in spite of darkness. We particularly center upon a sensor called “Advanced Synthetic Aperture Radar” (ASAR), which is part of the Envisat payload. On the other hand, GIS have emerged in recent years as a set of standards for data organization and representation that allow themanagement of geographic data.We provide a detailed description of the design and implementation of a tool that provides an integrated framework for the detection and localization of marine spills using remote sensing, GIS, and cloud computing. Cloud computing is used because of the enormous amount of data to be processed and the need of communication between users.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
La tragedia del prestige, evolución de la marea negra (2009), http://www.lavozdegalicia.es/albumes/index.jsp?album=20021121133541
Geoserver user’s manual: Cql tutorial (2010), http://docs.geoserver.org/1.7.x/user/tutorials/cql
2004 IEEE International: Algorithms for oil spill detection in Radarsat and ENVISAT SAR images. In: Geoscience and Remote Sensing Symposium (2004)
Baumann, P.: Ogc wcs 2.0 interface standard - core. Tech. rep., Open Geospatial Consortium (2010)
Brekke, C., Solberg, A.J.S.: Oil spill detection by satellite remote sensing. Remote Sensing of Environment 95(1), 1–13 (2005)
Brittain, J., Darwin, I.F.: Tomcat: The Definitive Guide, 2nd edn. O’Reilly Media (2008)
Daubechies, I.: Ten Lectures on Wavelets. SIAM: Society for Industrial and Applied Mathematics (1992)
ESA: Asar product handbook. European Spatial Agency (2007)
Heffelfinger, D.: Java EE 5 Development using GlassFish Application Server, 1st edn. Packt Publishing (2007)
Herring, J.R.: Opengis implementation standard for geographic information - simple feature access - part 2: Sql option. Tech. rep., Open Geospatial Consortium (2010)
Indregard, M., Solberg, A., Clayton, P.: D2-report on benchmarking oil spill recognition approaches and best practice. Tech. Rep. Archive No. 04-10225-A-Doc, Con- tract No: EVK2-CT-2003-00177, European Comission (2004)
Opengis, J.B.: web map server implementation specification. Tech. rep., Open Geospatial Consortium (2006)
Kennedy, M., Kopp, S.: Understanding Map Projections. Esri Press (2001)
Klawonn, F., Höppner, F.: What Is Fuzzy about Fuzzy Clustering? Understanding and Improving the Concept of the Fuzzifier. In: R. Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 254–264. Springer, Heidelberg (2003)
Kothuri, A., Godfrind, A., Beinat, E.: Pro Oracle Spatial for Oracle Database 11g, 2nd edn. Apress (2007)
Kresse, W., Fadaie, K.: Iso standards for geographic information. Springer (2004)
Lee, J.: Speckle analysis and smoothing of synthetic aperture radar images. Computer Graphics and Image Processing 17, 24–32 (1981)
Lin, J., Dyer, C.: Data-Intensive Text Processing with MapReduce, 2nd edn. Morgan and Claypool Publishers (2010)
Lupp, M.: Styled layer descriptor profile of the web map service implementation specification. Tech. rep., Open Geospatial Consortium (2007)
Nebert, D., Whiteside, A., Panagiotis, P.: Opengis catalogue services specification. Tech. rep., Open Geospatial Consortium (2007)
Nuñez, J., Llacer, J.: Astronomical image segmentation by selforganizing neural networks and wavelets. Neural Networks (16), 411–417 (2003)
Obe, R.O., Hsu, L.S.: PostGIS in Action. Manning Publications Co. (2011)
Panagiotis, P., Vretanos, A.: Opengis web feature service 2.0 interface standard. Tech. rep., Open Geospatial Consortium (2010)
Understanding ArcSDE: ArcGIS 9. ESRI Press (2004)
Eddins, W.R., Crosslin, R.L., Sutherland, D.E.: Digital Image Processing, pp. 201–208, 443–457. Prentice Hall (1992)
Seventh IEEE International Symposium on Cluster Computing and the Grid: Bridging the High Performance Computing Gap: the OurGrid Experience. IEEE Computer Society (2007)
Sotomayor, B., Childers, L.: Globus Toolkit 4: Programming Java Services. Morgan Kaufman, Elsevier (2006)
White, T.: Hadoop: The Definitive Guide, 2nd edn. O’Really Media|Yahoo Press (2010)
Zhang, D.Q., Chen, S.C.: A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine 32 (2004)
Zhang, H., Berg, A., Maire, M., Malik, J.: Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Berlin Heidelberg
About this chapter
Cite this chapter
Fustes, D., Cantorna, D., Dafonte, C., Iglesias, A., Arcay, B. (2012). Applications of Cloud Computing and GIS for Ocean Monitoring through Remote Sensing. In: Mukhopadhyay, S. (eds) Smart Sensing Technology for Agriculture and Environmental Monitoring. Lecture Notes in Electrical Engineering, vol 146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27638-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-27638-5_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27637-8
Online ISBN: 978-3-642-27638-5
eBook Packages: EngineeringEngineering (R0)