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Remote Sens. 2015, 7(4), 3986-4001; doi:10.3390/rs70403986

A Novel Technique for Time-Centric Analysis of Massive Remotely-Sensed Datasets

National Snow and Ice Data Center, University of Colorado, Boulder, CO 80309, USA
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Academic Editors: Chandra Giri and Prasad S. Thenkabail
Received: 30 December 2014 / Revised: 14 March 2015 / Accepted: 23 March 2015 / Published: 2 April 2015
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Abstract

Analyzing massive remotely-sensed datasets presents formidable challenges. The volume of satellite imagery collected often outpaces analytical capabilities, however thorough analyses of complete datasets may provide new insights into processes that would otherwise be unseen. In this study we present a novel, object-oriented approach to storing, retrieving, and analyzing large remotely-sensed datasets. The objective is to provide a new structure for scalable storage and rapid, Internet-based analysis of climatology data. The concept of a “data rod” is introduced, a conceptual data object that organizes time-series information into a temporally-oriented vertical column at any given location. To demonstrate one possible use, we ingest 25 years of Greenland imagery into a series of pure-object databases, then retrieve and analyze the data. The results provide a basis for evaluating the database performance and scientific analysis capabilities. The project succeeds in demonstrating the effectiveness of the prototype database architecture and analysis approach, not because new scientific information is discovered, but because quality control issues are revealed in the source data that had gone undetected for years. View Full-Text
Keywords: object-oriented; database; temporal analysis; Greenland; AVHRR; albedo object-oriented; database; temporal analysis; Greenland; AVHRR; albedo
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Grant, G.E.; Gallaher, D.W. A Novel Technique for Time-Centric Analysis of Massive Remotely-Sensed Datasets. Remote Sens. 2015, 7, 3986-4001.

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