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Hadoop Paradigm for Satellite Environmental Big Data Processing

Author

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  • Badr-Eddine Boudriki Semlali

    (LIST Lab, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Morocco)

  • Chaker El Amrani

    (LIST Lab, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Morocco)

  • Guadalupe Ortiz

    (UCASE Research Group, Escuela Superior de Ingeniería, University of Cadiz, Spain)

Abstract

The important growth of industrial, transport, and agriculture activities, has not led only to the air quality and climate changes issues, but also to the increase of the potential natural disasters. The emission of harmful gases, particularly: the Vertical Column Density (VCD) of CO, SO2 and NOx, is one of the major factors causing the aforementioned environmental problems. Our research aims to contribute finding solution to this hazardous phenomenon, by using remote sensing (RS) techniques to monitor air quality which may help decision makers. However, RS data is not easy to manage, because of their huge amount, high complexity, variety, and velocity, Thus, our manuscript explains the different aspects of the used satellite data. Furthermore, this article has proven that RS data could be regarded as big data. Accordingly, we have adopted the Hadoop big data architecture and explained how to process efficiently RS environmental data.

Suggested Citation

  • Badr-Eddine Boudriki Semlali & Chaker El Amrani & Guadalupe Ortiz, 2020. "Hadoop Paradigm for Satellite Environmental Big Data Processing," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 11(1), pages 23-47, January.
    • Handle: RePEc:igg:jaeis0:v:11:y:2020:i:1:p:23-47
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    Cited by:

    1. Ania Cravero & Ana Bustamante & Marlene Negrier & Patricio Galeas, 2022. "Agricultural Big Data Architectures in the Context of Climate Change: A Systematic Literature Review," Sustainability, MDPI, vol. 14(13), pages 1-26, June.

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