Abstract
Abstract. Earth observation system, which can realize global coverage in three dimensions, has provided massive data from earth observations at global scale for Earth System Science (ESS) and global change researches. It is named SDOG-based ESSG that the Spheroid Degenerated Octree Grid (SDOG) was chosen as an initial grid to develop an Earth System Spatial Grid (ESSG), which provides storage strategy and management framework based on triple (C, T, A) and SDOG grid coding for massive data. The objection of this paper is to provide an effective spatial data organization and indexing method, which organizes triple units into block and encodes for each triples block and converts multi-source global scale datasets into triples block so as to manage massive raster data at global scale.
Highlights
With the continuous development of earth observation technologies, spatial data is available at a global scale[1]
Spheroid Degenerated Octree Grid (SDOG) is a good implementation of the Earth System Spatial Grid (ESSG)[6] which has been adopted as the reference framework of global dataset in GEO working plan in 2012-2015[7]
How to enhance the efficiency of data accessing is a key issue for global spatial data management by SDOG-based ESSG
Summary
With the continuous development of earth observation technologies, spatial data is available at a global scale[1]. How to organize and manage the global-scale spatial data effectively has been a hotspot in ESS. SDOG[5] is a newly developed GSG in the intrinsic space of the Earth (i.e., spheroid) without using any map projections, and is able to represent global-scale spatial data. The number of triples for a global dataset will be raised dramatically as the grid resolution increasing which will beyond the ability of data management in present software and hardware environments, especially for the data retrieval and the I/O. How to enhance the efficiency of data accessing is a key issue for global spatial data management by SDOG-based ESSG. This paper is intent to manage the global-scale dataset using SDOG triples model which contains blocks division and hierarchical index
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