The Land Degradation Estimation Remote Sensing Methods Using RUE-adjusted NDVI

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dc.contributor.authorShelestov, Andrii
dc.contributor.authorShumilo, Leonid
dc.contributor.authorBilokonska, Yuliia
dc.contributor.authorLavreniuk, Alla
dc.date.accessioned2022-02-07T13:50:36Z
dc.date.available2022-02-07T13:50:36Z
dc.date.issued2021
dc.description.abstractenState of the art methodologies for land degradations assessment accepted by United Nations, Food and Agriculture Organization and other official organizations that work on food security problems are based on the use of satellite data. In this case, the basis for the land degradation maps are vegetation indices, calculated using combinations of multispectral channels of satellite images. Evaluation of the land degradation state and trends is grounded on the analysis of land productivity maps changes over time (land productivity trend), land cover changes and carbon stocks changes. The most common methodology for the land degradation assessment is used for the UN Sustainable Development Goal 15.3.1 “Proportion of land that is degraded over total land area” calculation. This study considers the improvement for the calculation of land productivity / degradation based on the use of means of net primary productivity (NPP). For the NPP calculation we used open databases of satellite products of MODIS with spatial resolution 500 m and Landsat-8 with 30 m spatial resolution in the Google Earth Engine cloud platform. The satellite data for 2015 to 2019 years were used to build land productivity map and determine the areas of land degradation, productive and sustainable land for the territory of Ukraine. The use of NPP improve the land productivity assessment by consideration of agroclimatic conditions. The results were compared with product of Trends.Earth (official QGIS built-in plugin) which calculate land degradation maps by the UN methodology. The total areas of productive, degraded and sustainable land were calculated for the territory of Ukraine for 2015-2019 period.uk
dc.format.pagerangeP. 103-106uk
dc.identifier.citationThe Land Degradation Estimation Remote Sensing Methods Using RUE-adjusted NDVI / Shelestov, A., Shumilo, L., Bilokonska, Y., Lavreniuk, A. // In IEEE EUROCON 2021-19th International Conference on Smart Technologies. – 2021, July. - P. 103-106.uk
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/46197
dc.language.isoenuk
dc.publisherIEEEuk
dc.subjectland degradationuk
dc.subjectland productivityuk
dc.subjectNDVIuk
dc.subjectNPPuk
dc.subjectTrends.Earthuk
dc.subjectSDG 15.3.1uk
dc.titleThe Land Degradation Estimation Remote Sensing Methods Using RUE-adjusted NDVIuk
dc.typeArticleuk

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