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Kinakh, V., Bun, R., & Danylo, O. (2018). Geoinformation Technology for Analysis and Visualisation of High Spatial Resolution Greenhouse Gas Emissions Data Using a Cloud Platform. In: Advances in Intelligent Systems and Computing II. Eds. Shakhovska, N. & Stepashko, V., pp. 217-229 Cham, Switzerland: Springer International. ISBN 978-3-319-70581-1 10.1007/978-3-319-70581-1_15.
Full text not available from this repository.Abstract
The geoinformation technology for spatial analysis and visualisation of greenhouse gas (GHG) emissions is proposed using Google Earth Engine cloud technology as a key component of interaction with high-resolution spatial data. This technology includes a website for spatial analysis and visualisation of vector data, as well as an interactive site for deeper analysis of raster data on GHG emissions. We use high-resolution vector data of emissions at the level of point, line and areal emission sources, which are converted into raster emission data. Emissions can be analysed within user-created polygons including calculation of the total, specific, maximum or average emission magnitudes. There is also the possibility to fix and select pixels containing a certain interval of emission magnitudes. Using Python’s Google Earth Engine module, we have created a website where users can clip raster data from hand-drawn polygons that can be saved on Google Drive. We have also used Python modules (Matplotlib, Pandas, Numpy) for statistical analysis of raster data and histogram construction. Geoinformation technology includes many sectors and categories of human activity included in national inventory reports on GHG emissions, such as those regarding the burning of fossil fuels for power and heat production, within the industrial, agricultural, construction, residential, institutional and waste sectors, as well reports addressing emissions caused by chemical processes. Implementation of the proposed technology is presented using high spatial resolution greenhouse gas emissions data from Poland.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | Geoinformation technology; Greenhouse gas; Emission data; High resolution; Google Earth Engine; Spatial analysis; Raster map; Histogram; Pandas; Matplotlib |
| Research Programs: | Ecosystems Services and Management (ESM) |
| Depositing User: | Romeo Molina |
| Date Deposited: | 29 Nov 2017 09:48 |
| Last Modified: | 27 Aug 2021 17:42 |
| URI: | https://pure.iiasa.ac.at/14986 |
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