Multidimensional architecture using a massive and heterogeneous data: Application to drought monitoring

The rapid increase in the number of Earth Observation (EO) systems generates a massive amount of heterogeneous data. It has raised big issues in collecting, preprocessing, storing, and the visualization these data. However, traditional techniques are facing serious challenges when dealing with big EO data dimensions (i.e., Volume, Veracity, Variety, and Velocity), especially in natural hazards management. Therefore, big data techniques and tools attract more attention. In this paper we propose a multidimensional model framework for Big EO data warehousing. This framework includes 3 parts: (1) Data collection and preprocessing, being responsible for collecting data and improving their quality; (2) Data loading and storage, performing the ingestion task which consists of transferring the data from external resources to the Big data platform for storage; and (3) Visualization and interpretation, aiming to provide spatio-temporal analysis. This framework could be useful for decision-makers in monitoring the effects of drought disasters and, consequently, planning the mitigation and remediation measures. Experiments are carried out on drought monitoring in China along the period 2000-2020. The input data include remote sensing data, biophysical data, and climatological data. The results reveal that the proposed framework has a higher retrieval speed and a greater elasticity with different kinds (i.e. spatial, temporal, or spatiotemporal) of requests compared to traditional frameworks, indicating its superiority.(c) 2022 Elsevier B.V. All rights reserved.