Volume II-4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4, 21-27, 2014
https://doi.org/10.5194/isprsannals-II-4-21-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4, 21-27, 2014
https://doi.org/10.5194/isprsannals-II-4-21-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Apr 2014

23 Apr 2014

TEMPORAL ANALYSIS OF ATMOSPHERIC DATA USING OPEN STANDARDS

P. Campalani1, A. Beccati1, S. Mantovani2, and P. Baumann1 P. Campalani et al.
  • 1Center for Advanced Systems Engineering (CASE), Jacobs University Bremen, 28759 Bremen, Germany
  • 2MEEO S.r.l, 44122 Ferrara, Italy

Keywords: Geospatial Web Services, Big Data, OGC, WCS, WCPS

Abstract. The continuous growth of remotely sensed data raises the need for efficient ways of accessing data archives. The classical model of accessing remote sensing (satellite) archives via distribution of large files is increasingly making way for a more dynamic and interactive data service. A challenge, though, is interoperability of such services, in particular when multi-dimensional data and advanced processing are involved. Individually crafted service interfaces typically do not allow substitution and combination of services. Open standards can provide a way forward if they are powerful enough to address both data and processing model.

The OGC Web Coverage Service (WCS) is a modular service suite which provides high-level interface definitions for data access, subsetting, filtering, and processing of spatio-temporal raster data. WCS based service interfaces to data archives deliver data in their original semantics useful for further client-side processing, as opposed to the Web Map Service (WMS) (de la Beaujardière, 2006) which performs a pre-rendering into images only useful for display to humans.

In this paper we present a case study where the OGC coverage data and service model defines the client/server interface for a climate data service. In particular, we show how flexible temporal analysis can be performed efficiently on massive spatio-temporal coverage objects. This service, which is operational on a several Terabyte data holding, has been established as part of the EarthServer initiative focusing on Big Data in the Earth and Planetary sciences.