TY - JOUR

T1 - О ВОЗМОЖНОСТЯХ ИНТЕГРАЦИИ ОБЛАЧНЫХ ИНФРАСТРУКТУР ПРОСТРАНСТВЕННЫХ ДАННЫХ И УНИВЕРСАЛЬНЫХ НАСТОЛЬНЫХ ГЕОГРАФИЧЕСКИХ ИНФОРМАЦИОННЫХ СИСТЕМ НА ПРИМЕРЕ GOOGLE EARTH ENGINE И QGIS

AU - Panidi, Evgeny A.

AU - Rykin, Ivan S.

N1 - Publisher Copyright: © 2020 Lomonosov Moscow State University. All rights reserved.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Google Earth Engine

KW - QGIS

KW - Remote sensing data processing

UR - http://www.scopus.com/inward/record.url?scp=85093821918&partnerID=8YFLogxK

U2 - 10.35595/2414-9179-2020-1-26-421-433

DO - 10.35595/2414-9179-2020-1-26-421-433

M3 - статья в журнале по материалам конференции

AN - SCOPUS:85093821918

VL - 26

SP - 421

EP - 433

JO - ИНТЕРКАРТО/ИНТЕРГИС

JF - ИНТЕРКАРТО/ИНТЕРГИС

SN - 2414-9179

T2 - 2020 International Conference on GI Support of Sustainable Development of Territories

Y2 - 28 September 2020 through 29 September 2020

ER -