Abstract
Abstract. Earth observation data of large part of the world is available at different temporal, spectral and spatial resolution. These data can be termed as big data as they fulfil the criteria of 3 Vs of big data: Volume, Velocity and Variety. The size of image in archives are multiple petabyte size, the size is growing continuously and the data have varied resolution and usages. These big data have variety of applications including climate change study, forestry application, agricultural application and urban planning. However, these big data also possess challenge of data storage, management and high computational requirement for processing. The solution to this computational and data management requirements is database system with distributed storage and parallel computation.In this study SciDB, an array-based database is used to store, manage and process multitemporal satellite imagery. The major aim of this study is to develop SciDB based scalable solution to store and perform time series analysis on multi-temporal satellite imagery. Total 148 scene of landsat image of 10 years period between 2006 and 2016 were stored as SciDB array. The data was then retrieved, processed and visualized. This study provides solution for storage of big RS data and also provides workflow for time series analysis of remote sensing data no matter how large is the size.
Highlights
Laney (2001) defined big data as data characterized by the 3Vs: Volume, Velocity, and Variety
Two languages are available in SciDB: Array Query Language (AQL) and Array Functional Language (AFL)
AQL is SQLlike query language whereas AFL is a functional language for SciDB
Summary
Laney (2001) defined big data as data characterized by the 3Vs: Volume, Velocity, and Variety. National imagery archives are storing terabytes of data every day and total stored imagery volume will grow to the order of Exabyte (OGC, 1999) This data is increasing at an exceptionally fast rate with the advent of the new sensor with varied spectral, spatial and temporal resolutions. These remote sensing data of large part of the world is big wealth to model the earth. Other application area includes forestry, urban planning, land management, food security. These Big Remote Sensing (RS) data poses the significant challenge of management, processing, and interpretation (Ma, et al, 2015). There is still big challenge in managing these data and fulfil high computation requirement to process them
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