A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data |
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Authors: | Nataliia Kussul Andrii Kolotii Sergii Skakun Olena Rakoid Leonid Shumilo |
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Affiliation: | 1. Department of Space Information Technologies and Systems, Space Research Institute NAS Ukraine &2. SSA Ukraine, Kyiv, Ukraine;3. Department of Information Security, National Technical University of Ukraine ‘Igor Sikorsky Kyiv Polytechnic Institute’, Kyiv, Ukraine https://orcid.org/0000-0002-9704-9702;4. Department of Information Security, National Technical University of Ukraine ‘Igor Sikorsky Kyiv Polytechnic Institute’, Kyiv, Ukraine https://orcid.org/0000-0002-6972-4483;5. Department of Geographical Sciences, University of Maryland, College Park, MD, USA;6. NASA Goddard Space Flight Center Code 619, Greenbelt, MD, USA https://orcid.org/0000-0002-9039-0174;7. Department of Ecology and Life Safety, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine;8. Department of Information Security, National Technical University of Ukraine ‘Igor Sikorsky Kyiv Polytechnic Institute’, Kyiv, Ukraine |
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Abstract: | ABSTRACTFor evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1 – ‘Forest area as proportion of total land area’; 15.3.1 – ‘Proportion of land that is degraded over total land area’; and 2.4.1 – ‘Proportion of agricultural area under productive and sustainable agriculture’. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows. |
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Keywords: | ERA-PLANET classification maps Essential Variables crop productivity yield assessment nexus approach |
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