Spatial data assimilation with a service-based GIS infrastructure for mapping and analysis of E. Huxleyi blooms in arctic seas

E. Kazakov; D. Kondrik; D. Pozdnyakov

doi:10.1117/12.2325127

6 August 2018 Spatial data assimilation with a service-based GIS infrastructure for mapping and analysis of E. Huxleyi blooms in arctic seas

E. Kazakov, D. Kondrik, D. Pozdnyakov

Author Affiliations +

Proceedings Volume 10773, Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018); 107730S (2018) https://doi.org/10.1117/12.2325127
Event: Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018), 2018, Paphos, Cyprus

Abstract

A coccolithophore E. huxleyi is one of the most significant sources of inorganic carbon in the world oceans. Forming vast bloom areas this species can affect the carbon balance in the atmosphere-ocean system, and thus interfere with climate and marine ecology. We obtained from 6 seas located at high latitudes a 19-year time series (1998-2016) of spaceborne data on this phenomenon as well as data on the phenomenon-affecting oceanographic and atmospheric variables. To efficiently concatenate and eventually analyze versatile data of huge size on the aforementioned blooms, a special GIS infrastructure (GISI) is developed. It is built on the principles of a service-based architecture with microservices. The GISI includes both a server application that controls information flows and automated data processing. Microservices with the RESTful architecture for data access and three types of interfaces for researchers are at the base of GISI. Researchers working with the GIS use both a dedicated web client for searching and downloading the required data, a desktop client developed as an extension for an open source desktop GIS QGIS and a Python library developed for the implementation of methods of interaction with the server. Another part of GISI is a virtual-machine based environment for user-side data processing. The use of such a system allows to improve the bloom identification, to map the variations in bloom location, extent, and its inherent properties as well as to perform time series analyses.

Citation Download Citation

E. Kazakov, D. Kondrik, and D. Pozdnyakov "Spatial data assimilation with a service-based GIS infrastructure for mapping and analysis of E. Huxleyi blooms in arctic seas", Proc. SPIE 10773, Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018), 107730S (6 August 2018); https://doi.org/10.1117/12.2325127

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KEYWORDS

Geographic information systems

Data processing

Associative arrays

Data storage

Human-machine interfaces

Statistical analysis

Water

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