Scholarly record
SATELLITE REMOTE SENSING OF SEAS AND OCEANS: THE CLOUD PARADIGM
Abstract
The research investigates the possibilities of using cloud technologies to supply the life cycle of satellite remote sensing of seas and oceans by using the examples of implementing a number of research projects. The need to control the condition of seas and oceans necessitates the timely receipt and complex processing of large amounts of satellite remote sensing data. Currently, these processes can be performed using cloud technologies. One case of implementing such technologies is the Google Earth Engine (GEE) cloud platform. The functionality of this platform provides its users with a new range of capabilities that significantly expand the availability of space data processing and analyzing for remote sensing of seas and oceans. The main tasks of the research are the analysis of the GEE functionality and the satellite remote sensing data archives available within this platform for studying the marine environment, the experimental implementation of a number of geospatial data processing scenarios for sea and ocean monitoring using the GEE according to the typical research projects cases, and finally the assessment of the results obtained with the cloud platform. As a result of the research, the experience of working in the GEE cloud geoinformational infrastructure was accumulated and analyzed in the following focuses: searching of the archived space images by complicated adaptive criteria, customizable on-line marine imagery visualization, object interpretation with semantic features assigning, a multi-user database of selected objects and their significant parameters creation and utilization, calculating the maps of the number of relevant satellite observations for specified water areas, uploading data to a local resource, etc. The processing of significant amounts of optical and radar imagery within a number of various cases allowed us to evaluate the effectiveness of the cloud infrastructure used in comparison with conventional methods of obtaining and processing satellite remote sensing data. Due to GEE the need of third-party software is minimized and local hardware resources are released, the volume of data uploaded is reduced up to 90%, as well as the time spent on preliminary and thematic processing of space images is reduced by approximately 4 and more times.
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References12
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