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Open AccessTechnical Note

A Generic Framework for Using Multi-Dimensional Earth Observation Data in GIS

Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA
Author to whom correspondence should be addressed.
Academic Editors: Markus Neteler, Ioannis Gitas and Prasad S. Thenkabail
Remote Sens. 2016, 8(5), 382;
Received: 11 March 2016 / Revised: 20 April 2016 / Accepted: 28 April 2016 / Published: 4 May 2016
PDF [16160 KB, uploaded 4 May 2016]


Earth Observation (EO) data are critical for many Geographic Information System (GIS)-based decision support systems to provide factual information. However, it is challenging for GIS to understand traditional EO data formats (e.g., Hierarchical Data Format (HDF)) given the different contents and formats in the two domains. To address this gap between EO data and GIS, the barriers and strategies of integrating various types of EO data with GIS are explored, especially with the popular Geospatial Data Abstraction Library (GDAL) that is used by many GISs to access EO data. The research investigates four key technical aspects: (i) designing a generic plug-in framework for consuming different types of EO data; (ii) implementing the framework to fix the errors in GIS when using GDAL to understand EO data; and (iii) developing extension for commercial and open source GIS (i.e., ArcGIS and QGIS) to demonstrate the usability of the proposed framework and its implementation in GDAL. A series of EO data products collected from NASA’s Atmospheric Scientific Data Center (ASDC) are used in the tests and the results prove the proposed framework is efficient to solve different problems in interpreting EO data without compromising their original content. View Full-Text
Keywords: Earth Observation data; data access; GDAL; GIS; plugin framework; QGIS Earth Observation data; data access; GDAL; GIS; plugin framework; QGIS

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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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Jiang, Y.; Sun, M.; Yang, C. A Generic Framework for Using Multi-Dimensional Earth Observation Data in GIS. Remote Sens. 2016, 8, 382.

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