The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 553–557, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-553-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 553–557, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-553-2020

  25 Aug 2020

25 Aug 2020

CLOUD-DESKTOP REMOTE SENSING DATA MANAGEMENT TO ENSURE TIME SERIES ANALYSIS, INTEGRATION OF QGIS AND GOOGLE EARTH ENGINE

E. Panidi1, I. Rykin1, P. Kikin2, and A. Kolesnikov3 E. Panidi et al.
  • 1Department of Cartography and Geoinformatics, Institute of Earth Sciences, Saint Petersburg State University, St. Petersburg, Russia
  • 2Peter the Great St. Petersburg Polytechnic University (SPbPU), St. Petersburg, Russia
  • 3Siberian State University of Geosystems and Technologies, Novosibirsk, Russia

Keywords: Google Earth Engine, QGIS, Remote Sensing Data Processing

Abstract. Our context research is conducted to investigate the possibility of common application of the remote sensing and ground-based monitoring data to detection and observation of the dynamics and change in climate and vegetation cover parameters. We applied the analysis of the annual graphs of Normalized Difference Water Index to estimate the length and time frames of growing seasons. Basing on previously gained results, we concluded that we can use the Index-based monitoring of growing season parameters as a relevant technique. We are working on automation of computations that can be applied to processing satellite imagery, computing Normalized Difference Water Index time series (in the forms of maps and annual graphs), and estimation of growing season parameters. As currently used data amounts are big (or up-to-big) geospatial data, we use the Google Earth Engine platform to process initial datasets. Our currently described experimental work incorporates investigation of the possibilities for integration of cloud computing data storage and processing with client-side data representation in universal desktop GISs. To ensure our study needs we developed a prototype of a QGIS plugin capable to run processing in GEE and represent results in QGIS.