Abstract
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.
Highlights
Earth Observing System (EOS) produces big remote sensing data, which record and capture long-term facts of land surface, solid earth, atmosphere and oceans
National Aeronautics and Space Administration (NASA) funded to the development of a NASA Hierarchical Data Format (HDF)-EOS Web Geographic Information System (GIS) Software Suite (NWGISS) to provide standards-based access and services to NASA Earth Observation (EO) data for the GIS community according to Open Geospatial Consortium specifications [17,18,19]
It focuses on upgrading the delivery of EO data for consumption in GIS by solving data interpretation issues
Summary
Earth Observing System (EOS) produces big remote sensing data, which record and capture long-term facts of land surface, solid earth, atmosphere and oceans. Some GIS software vendors worked on fixing these problems by developing specific versions of GDAL to assimilate more EO data. An XML-based plug-in framework is proposed to address the problem of using GDAL to access. Unlike traditional EOS geospatial tools that only consume partial EO data, the proposed framework enables GIS to support more EO data. The HDF4/HDF5 data drivers in GDAL (version 2.0.0) are improved to address the above limitations and to enhance the capability of processing multi-dimensional variables at the level of source code.
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