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The paper describes briefly content and results of experiments produced to test possibilities and effectiveness of integration and common use of the Google Earth Engine public cloud geospatial computing platform and QGIS desktop geographic information system. The experiments were focused on probation of Google Earth Engine data unloading and visualizing using QGIS graphical user interface instead of standard Web-browser-based visualizing. Final goal of the experiments was to formalize the principles of architecture of the specialized QGIS module developed by authors. The module is planned as a tool for vegetation index time-series mapping and analysis aimed on estimation of the growing season parameters (i.e., time frames, length, etc.) with 1-day time resolution.The project context is formed by long-going research collaboration devoted to the investigation of interdependencies in dynamics and change of climate parameters and parameters of vegetation cover. In earlier studies, authors detected that analysis of quantitative parameters of the changing climate in northern regions have to be conducted for spring, summer and autumn growing seasons separately, as these periods are characterized by significant differences in plant vegetating conditions. However, due to the sparseness of ground observation network in northern regions of Russia (which are discovered as the area of interest by the authors), the issue of detailed estimation of the spatial distribution and differentiation of growing season framing dates and other parameters becomes almost unresolvable. Vegetation indexes mapping and analysis can be applied to solve this problem, but implementation of cloud computing facilities is needed in the case of 1-day time resolution of initial satellite imagery used to compute vegetation indexes, due to the huge size of processed data. In such a context authors touch the issue of integration of the cloud platform computational power with the desktop GIS analysis diversity.

intercarto. intergis

Toward the capabilities of integration of the cloud-based spatial data infrastructures and universal desktop geographic information systems, case study of Google Earth Engine and QGIS