Sustainable Development Goal 2.4.1 for Ukraine Based on Geospatial Data

Hanna Yailymova, Bohdan Yailymov, Yevhen Mazur, Nataliia Kussul and Andrii Shelestov

References:

1. Global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development. [Online]. Available: https://unstats.un.org/sdgs/indicators/indicators-list/.
2. G. B. Van Halderen, “Big data for the SDGs: country examples in compiling SDG indicators using non-traditional data sources,” Working paper, United Nations, 2021. [Online]. Available: https://hdl.handle.net/20.500.12870/3442.
3. J. Chen, X. Cao, S. Peng, and H. Ren, “Analysis and applications of GlobeLand30: a review,” ISPRS International Journal of Geo-Information, 2017, vol. 6(8), p. 230, doi: 10.3390/ijgi6080230.
4. M. Cao, L. Chang, S. Ma, Z. Zhao, K. Wu, X. Hu, and M. Chen, “Multi-Scenario Simulation of Land Use for Sustainable Development Goals,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, vol. 15, pp. 2119-2127, doi: 10.1109/JSTARS.2022.3152904.
5. J. D. Gil, P. Reidsma, K. Giller, L.Todman, A. Whitmore, and M. van Ittersum, “Sustainable development goal 2: Improved targets and indicators for agriculture and food security,” Ambio, 2019, vol. 48(7), pp. 685-698, doi: 10.1007/s13280-018-1101-4.
6. L. Han, L. Lu, J. Lu, X. Liu, S. Zhang, K. Luo, and Q. Li, “Assessing Spatiotemporal Changes of SDG Indicators at the Neighborhood Level in Guilin, China: A Geospatial Big Data Approach,” Remote Sensing, 2022, vol. 14(19), p. 4985, doi: 10.3390/rs14194985.
7. M. Brussel, M. Zuidgeest, K. Pfeffer, and M. van Maarseveen, “Access or accessibility? A critique of the urban transport SDG indicator,” ISPRS International Journal of Geo-Information, 2019, vol. 8(2), p. 67, doi: 10.3390/ijgi8020067.
8. N. Kussul, M. Lavreniuk, A. Kolotii, S. Skakun, O. Rakoid, and L. Shumilo, “A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data,” International Journal of Digital Earth, 2019, vol. 13:2, pp. 309-321, doi: 10.1080/17538947.2019.1610807.
9. K. Deininger, D.A. Ali, and et al., “Quantifying War-Induced Crop Losses in Ukraine in Near Real Time to Strengthen Local and Global Food Security”, Food Policy, vol. 115, Feb. 2023, p. 102418, doi: 10.1016/j.foodpol.2023.102418.
10. L. Shumilo, S. Drozd, and et al., “Mathematical Models and Informational Technologies of Crop Yield Forecasting in Cloud Environment,” Ilchenko, M., Uryvsky, L., Globa, L. (eds) Progress in Advanced Information and Communication Technology and Systems. MCiT 2021. Lecture Notes in Networks and Systems, vol 548. Springer, Cham. pp. 143-164, doi: 10.1007/978-3-031-16368-5_7.
11. SDG Indicators Metadata repository. [Online]. Available: https://unstats.un.org/sdgs/metadata/.
12. SDG 2.4.1 indicator metadata. [Online]. Available: https://unstats.un.org/sdgs/metadata/files/Metadata-02-04-01.pdf.
13. N. Kussul, L. Shumilo, H. Yailymova, A. Shelestov, and T. Krasilnikova, “Complex method for land degradation estimation,” 2nd International Scientific Conference on Environmental Sustainability in Natural Resources Management, 01 November 2022, Riga, Latvia, vol. 1126, no. 1, p. 012032, doi: 10.1088/1755-1315/1126/1/012032.
14. A. Shelestov, M. Lavreniuk, V. Vasiliev, and et al., “Cloud Approach to Automated Crop Classification Using Sentinel-1 Imagery,” IEEE Transactions on Big Data, 2020, vol. 6, no. 3, pp. 572-582, doi: 10.1109/TBDATA.2019.2940237.
15. C. V. Van Diepen, J. V. Wolf, H. Van Keulen, and C. Rappoldt, “WOFOST: a simulation model of crop production,” Soil use and management, 1989, vol. 5(1), pp. 16-24, doi: 10.1111/j.1475-2743.1989.tb00755.x.
16. European Soil Database. [Online]. Available: https://esdac.jrc.ec.europa.eu/content/sinfo-esdb-data-adapted-mars-crop-yield-forecasting-system#tabs-0-description=1.
17. T. Zhang, W. S. Chandler, J. M. Hoell, D. Westberg, C. H. Whitlock, and P. W. Stackhouse, “A global perspective on renewable energy resources: NASA’s prediction of worldwide energy resources (power) project,” In Proceedings of ISES World Congress 2007, vol. I–vol. V, pp. 2636-2640. Springer, Berlin, Heidelberg, doi: 10.1007/978-3-540-75997-3_532.
18. L. Zhichkina, V. Nosov, K. Zhichkin, H. Aydinov, V. Zhenzhebir, and V. Kudryavtsev, “Satellite monitoring systems in forestry,” Journal of Physics: Conference Series 2020, vol. 1515, no. 3, p. 032043. IOP Publishing.
19. Y.Tian and et. all, “Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment,” Remote Sensing of Environment, 2002, vol. 83(3), pp. 414-430, doi: 10.1016/S0034-4257(02)00047-0.
20. M. H. Easdale and et all., “Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring,” Ecological Indicators, 2019, vol. 107, 105545, doi: 10.1016/j.ecolind.2019.105545.
21. N. Kussul, K. Deininger, L. Shumilo, M. Lavreniuk, D. Ayalew Ali., and O. Nivievskyi, “Biophysical Impact of Sunflower Crop Rotation on Agricultural Fields,” Sustainability. 2022. no. 14(7):3965, pp. 125-132, doi: 10.3390/su14073965.