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Adopting the Hadoop Architecture to Process Satellite Pollution Big Data
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2019
Year
Earth ObservationEnvironmental MonitoringEngineeringBig Data AnalyticsSatellite DataTerrestrial SensingBig Data ModelData ScienceAtmospheric ScienceBig Data ArchitectureData IntegrationHadoop ArchitectureData ManagementRenewable Energy MonitoringGeographyEarth Observation DataBig Data AcquisitionCloud ComputingRemote SensingAir PollutionRemote Sensing SensorBig Data
The important growth of industrial, transport and agriculture activities, has not led only to the Air Quality (AQ) and climate changes issues, but also to the increase of the potential natural disasters. The emission of harmful gases, in particular, the vertical column density of CO, SO2, and NOx is one of the major factors causing the aforementioned environmental problems. Our research aims to contribute to finding a solution to this hazardous phenomenon, by using Remote Sensing (RS) techniques to monitor AQ with the aim of helping decision-makers. However, RS data are not easy to manage, because of their huge size, high complexity, variety, and velocity. This is why, our manuscript explains the different aspects of the used satellite data, proving that satellite data could be regarded as Big Data (BD). Accordingly, we have proposed a Hadoop BD architecture and explained how to use it to process RS environmental data efficiently.