Monitoring of the SDG 2.4.1 and 15.3.1 indicators on the CREODIAS platform with using in-situ data

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dc.contributor.authorSylantyev, Sergiy
dc.contributor.authorYailymova, Hanna
dc.contributor.authorShelestov, Andrii
dc.date.accessioned2022-11-07T12:48:34Z
dc.date.available2022-11-07T12:48:34Z
dc.date.issued2022
dc.description.abstractenLand degradation neutrality in Agenda 2030 is the scientific, politic, economic, and social UNCCD conceptual framework in sustainable development in epoch of world economy decarbonization – net zero carbon 2050. For monitoring this LDN process to decision making was proposed SDG 2.4.1. and 15.3.1 indicators on international and national levels. To calculate NDVI index for 8 Ukrainian regions with using CREODIAS platform the Sentinel-1,2 and Landsat-8 mission images and in-situ Ukrainian data was analyzed. Calculation of the NDVI index, which is available from EO data of Landsat 8, Sentinel 1, 2, comes first for different regions of the Ukraine: Chernihiv, Mykolaiv, Dnipropetrovsk, Kherson, Vinnytsia, Zhytomyr, Cherkasy, Sumy. In addition, NDVI is often used in Ukraine as around the world to monitor drought, forecast agricultural production, assist in forecasting fire zones, and desert offensive maps. Farming apps, like Crop Monitoring, integrate NDVI to facilitate crop scouting and give precision to fertilizer application and irrigation, among other field treatment activities, at specific growth stages. NDVI is preferable for global vegetation monitoring since it helps to compensate for changes in lighting conditions, surface slope, exposure, and other external factors. The interpretation NDVI indexes on the examples of October 2021 normalized difference vegetation indexes for different regions of the Ukraine, for example Chernihiv, Mykolaiv, Dnipropetrovsk, Kherson, Vinnytsia, Zhytomyr, Cherkasy, Sumy is underpinned by a conceptual model that perceives land as a socioecological Ukrainian system (a coupled human-natural system); hence, labelling a land unit in Ukraine as degraded requires a synergy of utilitarian (human-driven) and ecological (ecosystem function and structure) in the context of SDG 2.4.1 and 15.3.1 index calculation. Land cover classification systems derived from EO Data from CREODIAS platform and in-situ data are important tools to describe the natural and urban environment of the Ukraine for different science research demands and effective agriculture workflow process organization [1, 2]. The authors acknowledge the funding received by Horizon 2020 e-shape project (Grant Agreement No 820852). REFERENCES 1. Nataliia Kussul, Mykola Lavreniuk, Andrii Kolotii, Sergii Skakun, Olena Rakoid & Leonid Shumilo (2020) A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data, International Journal of Digital Earth, 13:2, 309-321, DOI: 10.1080/17538947.2019.1610807. 2. N. Kussul, A. Shelestov, M. Lavreniuk, I. Butko and S. Skakun, "Deep learning approach for large scale land cover mapping based on remote sensing data fusion," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016, pp. 198-201, doi: 10.1109/IGARSS.2016.7729043.uk
dc.identifier.citationSylantyev, S. Monitoring of the SDG 2.4.1 and 15.3.1 indicators on the CREODIAS platform with using in-situ data / Sylantyev Sergiy, Yailymova Hanna, Shelestov Andrii // Living Planet Symposium, 23-27 May 2022, Bonn, Germany.uk
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/50888
dc.language.isoenuk
dc.titleMonitoring of the SDG 2.4.1 and 15.3.1 indicators on the CREODIAS platform with using in-situ datauk
dc.typeOtheruk

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