BOOKS OF THE NATIONAL ACADEMY OF SCIENCES OF UKRAINE

Monitoring of sustainable development goals using satellite data

Science & Technology

Authors:

Oleh Fedorov (Олег Федоров)— Director of Space Research Institute NASU-SSAU, Doctor of Sciences, professor, corresponding member of NASU; Space Research Institute NASU-SSAU.

http://www.scopus.com/authid/detail.url?authorId= 12780133600

https://scholar.google.com.ua/citations?hl=uk&user=9K95f2wAAAAJ

https://www.researchgate.net/profile/Oleg-Fedorov-2

https://orcid.org/0000-0002-0245-6509

 

Nataliia Kussul (Наталія Куссуль) — Head of Department “Mathematical Modeling and Data Analysis” of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Leading Scientist of Space Research Institute NASU-SSAU, Doctor of Sciences, professor; National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Space Research Institute NASU-SSAU.

http://www.scopus.com/authid/detail.url?authorId=6602485938

https://scholar.google.com.ua/citations?user=e3TWBuwAAAAJ&hl=ru

https://www.researchgate.net/profile/Nataliia_Kussul2

https://orcid.org/0000-0002-9704-9702

 

Andrii Shelestov (Андрій Шелестов) — professor of “Mathematical Modeling and Data Analysis” Department of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Leading Scientist of Space Research Institute NASU-SSAU, Doctor of Sciences, professor; National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Space Research Institute NASU-SSAU.

http://www.scopus.com/authid/detail.url?authorId=6507365226

http://scholar.google.com.ua/citations?user=tqoQKZAAAAAJewed

https://www.researchgate.net/profile/Andrey_Shelestov

https://orcid.org/0000-0001-9256-4097

 

Reviewers:

corresponding member of National Academy of Sciences of Ukraine, Doctor of Sciences, professor Vyacheslav Gubarev, Chief of Department at Space Research Institute of NASU-SSAU; Space Research Institute of NASU-SSAU, Ukraine, Kyiv 03187, Gushkova prosp. 40, building 4/1.

https://www.scopus.com/authid/detail.uri?authorId=7004361810

https://scholar.google.com/citations?hl=ru&user=9TuVpkcAAAAJ

https://orcid.org/0000-0001-6284-1866

 

corresponding member of National Academy of Sciences of Ukraine, Doctor of Sciences, professor, Deputy Director of Institute of Applied System Analysis (IASA) of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Nataliia Pankratova; Institute for Applied System Analysis (IASA), National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine, Kyiv 03056, Peremohy, 37-А

https://www.scopus.com/authid/detail.uri?authorId=7004316033

https://scholar.google.com/citations?hl=ru&user=REDB7xAAAAAJ

https://orcid.org/0000-0002-6372-5813

Affiliation:

Project: Scientific book

Year: 2023

Publisher: PH "Naukova Dumka"

Pages: 164

DOI:

https://doi.org/10.15407/978-966-00-1865-5

ISBN: 978-966-00-1865-5

Language: Ukrainian

How to Cite:

Fedorov, O., Kussul, N., Shelestov, A. (2023) Monitoring of sustainable development goals using satellite data. Kyiv, Naukova Dumka. 164 p. [in Ukrainian].

Abstract:

The monograph dedicates to the results of development and implementation by Ukrainian scientists of information technologies and services for estimation indicators of sustainable development based on the use of satellite Earth observation data. The work is aimed at solving current socio-economic problems, in particular, achieving the goals of sustainable development formulated by the United Nations, monitoring global climate changes, and catastrophic events. The methodology for determining indicators of sustainable development, information technologies for their calculating based on satellite data and corresponding geospatial products using modern cloud technologies is proposed. In particular, the implementation in Ukraine of the ideology of the newly created international system of systems of Earth observations GEOSS and Copernicus program is presented. The gained experience allows proposing the ideology of the Ukrainian segment of GEOSS – UkrGEO information system, which involves the integration and exploitation of many new satellite datasets, as well as the significant modernization of national statistical and geospatial systems in the context of digitalization of the economy and Industry 4.0.

For specialists in the field of space information technologies, satellite Earth observations, geoinformation systems, environmental safety, sustainable development, as well as graduate students and students of relevant specializations.

Keywords:

Sustainable development goals SDG, GEOSS, UkrGEO, Earth observation, satellite data, information technology, geospatial service, Industry 4.0.

References:

For section 1 

  1. United Nations (2015). Transforming our world: The 2030 Agenda for Sustainable Development. Resolution adopted by the General Assembly on 25 September 2015 (A/RES/70/1). https://www.un.org/en/development/desa/population/migration/generalassembly/docs/globalcompact/A_RES_70_1_E.pdf
  2. Scott, G. & Rajabifard, A. (2017). Sustainable development and geospatial information: a strategic framework for integrating a global policy agenda into national geospatial capabilities. Geo-spatial Information Science, 20(2), 59-76. https://www.tandfonline.com/doi/full/10.1080/10095020.2017.1325594
  3. United Nations (2002). Report of the World Summit on Sustainable Development. Johannesburg Declaration on Sustainable Development (A/CONF.199/20*). https://documents-dds-ny.un.org/doc/UNDOC/GEN/N02/636/93/PDF/N0263693.pdf?OpenElement
  4. Thépaut, J.-N. (2017). European Contribution to the Monitoring of Essential Climate Variables from Space.  [Conference presentation]. Copernicus Climate Change Service (C3S). https://www.ecmwf.int/sites/default/files/elibrary/2017/17860-european-contribution-monitoring-essential-climate-variables-space.pdf
  5. United Nations (2017). Cape Town Global Action Plan for Sustainable Development Data. General Assembly in July 2017 (RES/71/313). http://unstats.un.org/sdgs/hlg/Cape-Town-Global-Action-Plan
  6. United Nations (2017). Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators. 48th session. Department of Economic and Social Development. https://unstats.un.org/unsd/statcom/49th-session/documents/2018-2-SDG-IAEG-R.pdf
  7. Earth Observations in Support of the 2030 Agenda for Sustainable Development (2017). http://www.earthobservations.org/documents/publications/201703_geo_eo_for_2030_agenda.pdf
  8. Fedorov O.P., Samoilenko L.I., Kolos L.M. & Pidhorodetska L.V. (2019). Problems of using satellite data to the assessment of sustainable development goals of Ukraine. Space Science and Technology, 25(3), 40-56. https://doi.org/10.15407/knit2019.03.040 [in Ukrainian].
  9. Hoff, H. (2011). Understanding the Nexus. Background Paper for the Bonn 2011 Conference: The Water, Energy and Food Security Nexus. Stockholm Environment Institute, Stockholm. https://mediamanager.sei.org/documents/Publications/SEI-Paper-Hoff-UnderstandingTheNexus-2011.pdf
  10. Allouche, J., Middleton, C. & Gyawali, D. (2014). Nexus Nirvana or Nexus Nullity? A dynamic approach to security and sustainability in the water-energy-food nexus. STEPS Working Paper 63. Sussex, UK: Social, Technological and Environmental Pathways to Sustainability (STEPS) Center. https://steps-centre.org/wp-content/uploads/Water-and-the-Nexus.pdf
  11. Biggs, E.M., et al. (2015). Sustainable Development and the Water-Energy-Food Nexus: A Perspective on Livelihoods. Environmental Science & Policy Journal, (54), 389-397. https://doi.org/10.1016/j.envsci.2015.08.002
  12. Eftelioglu, E., Jiang, Z., Tang, X., Shekhar, S. (2017). The Nexus of Food, Energy, and Water Resources: Visions and Challenges in Spatial Computing. In Griffith, D., Chun, Y., Dean, D. (eds) Advances in Geocomputation. Advances in Geographic Information Science (pp. 5-20). Springer, Cham. https://doi.org/10.1007/978-3-319-22786-3_2
  13. Simmons, A., Fellous, J., Ramaswamy, V. et al. (2016). Observation and integrated Earth-system science: A roadmap for 2016-2025. Advances in Space Research, 57(10),  2037-2103. https://doi.org/10.1016/j.asr.2016.03.008
  14. Nativi, S., Mazzetti, P., Santoro, M. & Ochiai, O. (2015). Big Data challenges in building the Global Earth Observation System of Systems. Environmental Modelling and Software, (68), 1-26. https://doi.org/10.1016/j.envsoft.2015.01.017
  15. UNU-FLORES Nexus Observatory. The Nexus Approach. https://flores.unu.edu/en/research/nexus
  16. Paton, B.Ye. (Ed.). (2012). National paradigm of sustainable development of Ukraine. State Institution «Institute of Economies of Nature Use and Sustainable Development of the National Academy of Sciences of Ukraine» [in Ukrainian].
  17. Paton, B.Ye. (Ed.). (2016). National paradigm of sustainable development of Ukraine. (2 edition, revised and supplemented). State Institution «Institute of Economies of Nature Use and Sustainable Development of the National Academy of Sciences of Ukraine» [in Ukrainian].
  18. The World Bank Group. The World Development Indicators [Data Set]. http://data.worldbank.org/indicator
  19. Eurostat Sustainable Development Indicators. http://epp.eurostat.ec.europa.eu/portal/page/portal/sdi/indicators
  20. 20. OECD Key Environmental Indicators (2008). OECD, Paris. (P. 36). http://oecd.org/env/indicators../37551205.pdf
  21. 21. Zghurovskyi, M.Z. (Ed.). (2009). Sustainable development of regions in Ukraine. National Technical Univercity of Ukraine Igor Sikorskij Kyiv Politechnic Institute. http://www.activity.wdc.org.ua/ukraine/Isd_ukr-2400dpi-10.pdf [in Ukrainian].
  22. Sebestyén, V., Czvetkó, T. & Abonyi, J. (2021). The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking. Frontiers Environmental Science, (9). Article 619092. https://doi.org/10.3389/fenvs.2021.619092
  23. Meadows, D.H., Meadows, D.L., Randers, J., & Behrens W.W. III. (1972). The Limits to Growth: A report for the Club of Rome’s project on the predicament of mankind. Universe Books.
  24. Meadows, D., Randers, Y. & Meadows, D. (2018). The Limits to Growth. The 30-Year Update. Pabulum. [in Ukrainian].
  25. Makston, H. & Randers, Y. (2017). In search of prosperity. Managing economic development to reduce unemployment, inequality and climate change. Report to the Club of Rome. Pabulum. [in Ukrainian].
  26. Nair, Ch. (2020). The state of sustainable development. The future governance of the economy and society. Nash format [in Ukrainian].
  27. Swain, R.B. & Yang-Wallentin, F. (2020). Achieving sustainable development goals: predicaments and strategies. International Journal of Sustainable Development & World Ecology, 27(2), 96-106. https://doi.org/10.1080/13504509.2019.1692316
  28. Polumiienko, S.K. & Rybakov, L.O. (2015). Indicative analysis of sustainability of technological development (S.O. Dovhii, Ed.). Logos [in Ukrainian].
  29. Horda, S.Ie. (2018). Methods of resource-theoretic game analysis of regional development processes [Master’s thesis dissertation, Tech. Sci] [in Ukrainian].
  30. Polumiienko, S.K., Stryzhak, O.Ie. & Trofymchuk, O.M. (2016). Resource-ontological approach to assessing the level of national development. Mathematical modeling in economics, (3-4), 7-26 [in Ukrainian].
  31. Time to revise the Sustainable Development Goals (03 July 2020). Nature, 583, 331-332 https://www.nature.com/articles/d41586-020-02002-3
  32. Bali Swain, R. & Yang-Wallentin, F. (2020). Achieving sustainable development goals: predicaments and strategies. International Journal of Sustainable Development & World Ecology, 27(2), 96-106. https://www.tandfonline.com/doi/pdf/10.1080/13504509.2019.1692316?needAccess=true
  33. Shepherd, Keith, Hubbard, Douglas,  Fenton, Norman,  Claxton, Karl,  Luedeling, Eike &  de Leeuw, Jan (08 July 2015). Policy: Development goals should enable decision-making. Nature, 523, 152-154. https://www.nature.com/news/policy-development-goals-should-enable-decision-making-1.17915
  34. Blair, G.S, Henrys, P., Leeson, A., Watkins, J., Eastoe, E., Jarvis, S. & Young, P.J. (2019). Data Science of the Natural Environment: A Research Roadmap. Frontiers Environmental Science, 7, Article 121.
  35. Ferreira, B., Iten, M. & Silva, R.G. (2020). Monitoring sustainable development by means of earth observation data and machine learning: a review. Environmental Sciences Europe, 32, Article 120. https://doi.org/10.1186/s12302-020-00397-4
  36. United Nations (5 december 2017). Earth observations for official statistics: Satellite imagery and geospatial data task team report. https://unstats.un.org/bigdata/task-teams/earth-observation/UNGWG_Satellite_Task_Team_Report_WhiteCover.pdf
  37. International Organization for Standardization. (2011). Geographic information – Metadata –Part 1: Fundamentals (DIS). (ISO Standart № 19115-1:2011, ISO/TC211). Geneva, Switzerland.
  38. International Organization for Standardization. (2006). Geographic information – Data quality measures. (ISO Standart № 19138:2006. ISO/TC211).Geneva, Switzerland.
  39. International Organization for Standardization. (2011). Geographic information – Data quality (DIS). (ISO Standart № 19157: 2011. ISO/TC211). Geneva, Switzerland.
  40. Yang, X., Blower, J.D., Bastin, L., Lush, V., Zabala, A., Masó, J., Cornford, D., Díaz, P. & Lumsden, J. (2013). An integrated view of data quality in Earth observation. Philosofical Transactions of the Royal Society A. 371: 20120072. http://dx.doi.org/10.1098/rsta.2012.0072
  41. Tam, S.-M. & Clarke, F. (2015). Big Data, Official Statistics and Some Initiatives by the Australian Bureau of Statistics. International Statistical Review. 83(3), 436-448. https://doi.org/10.1111/insr.12105

 

 For section 2 

  1. Cowie, A.L., Orr, B.J., Sanchez, V. M.C., Chasek, P., Crossman, N.D., Erlewein, A., Louwagie, G., et al. (2018). Land in Balance: The Scientific Conceptual Framework for Land Degradation Neutrality. Environmental Science & Policy, (79), 25-35.
  2. Kussul, N., Lavreniuk, M., Skakun, S. & Shelestov, A. (2017b). Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778-782. https://doi.org/10.1109/LGRS.2017.2681128
  3. Kingma, D.P. & Ba, J. Adam (2014). A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980.
  4. Skakun, S., Kussul, N., Shelestov, A. & Kussul, O. (2015). The Use of Satellite Data for Agriculture Drought Risk Quantification in Ukraine. Geomatics. Natural Hazards and Risk, 7(3), 901-917. https://doi.org/10.1080/19475705.2015.1016555
  5. Kussul, N., Lemoine, G., Gallego, F.J., Skakun, S., Lavreniuk, M. & Shelestov, A. (2016). Parcel-based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 2500-2508. http://dx.doi.org/10.1109/JSTARS.2016.2560141
  6. Kussul, N., Lavreniuk, M., Shelestov, A. & Skakun, S. (2018). Crop Inventory at Regional Scale in Ukraine: Developing in Season and End of Season Crop Maps with Multi-temporal Optical and SAR Satellite Imagery. European Journal of Remote Sensing, 51(1),  627-636.https://doi.org/10.1080/22797254.2018.1454265
  1. Ghazaryan, G., Dubovyk, O., Löw, F., Lavreniuk, M., Kolotii, A., Schellberg, J. & Kussul, N. (2018). A Rule-based Approach for Crop Identification Using Multi-temporal and Multi-sensor Phenological Metrics. European Journal of Remote Sensing, 51(1), 511-524. https://doi.org/10.1080/22797254.2018.1455540
  2. Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., & Skakun, S. (2017b). Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-temporal Satellite Imagery for Crop Mapping. Frontiers in Earth Science, (5), 17. https://doi.org/10.3389/feart.2017.00017
  3. Skakun, S., Kussul, N., Shelestov, A., Lavreniuk, M., & Kussul, O. (2016). Efficiency Assessment of Multitemporal C-Band Radarsat-2 Intensity and Landsat-8 Surface Reflectance Satellite Imagery for Crop Classification in Ukraine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8), 3712-3719. http://dx.doi.org/10.1109/JSTARS.2015.2454297
  4. Waldner, F., Abelleyra, D.De, Verón, S.R., Zhang, M., Wu, B., Plotnikov, D., Bartalev, S., et al. (2016). Towards a Set of Agrosystem-specific Cropland Mapping Methods to Address the Global Cropland Diversity. International Journal of Remote Sensing, 37(14), 3196-3231.
  5. Kussul, N., Kolotii, A., Shelestov, A., Yailymov, B., & Lavreniuk, M. (2017a). Land Degradation Estimation from Global and National Satellite based Datasets within UN Program. In 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), (1), 383-386. http://dx.doi.org/10.1109/IDAACS.2017.8095109
  6. Gitelson, A.A. (2011). Remote Sensing Estimation of Crop Biophysical Characteristics at Various Scales. Chapter 15 in Hyperspectral Remote Sensing of Vegetation (Thenkabail, P.S., Lyon, J.G., Huete, A., Eds). (pp. 329-358). Boca Raton, FL: CRC Press.
  7. Monteith, J.L. (1972). Solar Radiation and Productivity in Tropical Ecosystems. The Journal of Applied Ecology, 9(3), 747-766.  https://doi.org/10.2307/2401901
  8. Bandaru, V., West, T.O., Ricciuto, D.M. & Izaurralde, R.C. (2013). Estimating Crop Net Primary Production Using National Inventory Data and MODIS-derived Parameters. ISPRS Journal of Photogrammetry and Remote Sensing, (80), 61-71.  https://doi.org/10.1016/j.isprsjprs.2013.03.005
  9. Prince, S.D. & Goward, S.N. (1995). Global Primary Production: A Remote Sensing Approach. Journal of Biogeography, 22(4–5), 815–835.  https://doi.org/10.2307/2845983
  10. He, M., Kimball, J.S., Maneta, M.P., Maxwell, B.D., Moreno, A., Beguería, S. & Wu, X. (2018). Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data. Remote Sensing, 10(3), art. num. 372.
  11. Prince, S.D., Haskett, J., Steininger, M., Strand, H. & Wright, R. (2001). Net Primary Production of US Midwest Croplands from Agricultural Harvest Yield Data. Ecological Applications, 1(4), 1194-1205.
  12. Xin, Q., Broich, M., Suyker, A.E., Yu, L. & Gong, P. (2015). Multi-scale Evaluation of Light use Efficiency in MODIS Gross Primary Productivity for Croplands in the Midwestern United States. Agricultural and Forest Meteorology, (201), 111-119.
  13. Li, J. & Roy, D.P. (2017). A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote Sensing, 9(9), art. num. 902.
  14. López-Lozano, R., Duveiller, G., Seguini, L., Meroni, M., García-Condado, S., Hooker, J. & Baruth, B. (2015). Towards Regional Grain Yield Forecasting with 1 km-resolution EO Biophysical Products: Strengths and Limitations at Pan- European Level. Agricultural and Forest Meteorology, (206), 12-32.
  15. Shelestov, A., Kolotii, A., Skakun, S., Baruth, B., Lopez Lozano, R. & Yailymov, B. (2017a). Biophysical Parameters Mapping Within the SPOT-5 Take 5 Initiative. European Journal of Remote Sensing, 50(1), 300-309. https://doi.org/10.1080/22797254.2017.1324743
  16. Skakun, S., Vermote, E., Roger, J.-C. & Franch, B. (2017). Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale. AIMS Geosciences, 3(2), 163-186. https://doi.org/10.3934/geosci.2017.2.163
  17. Claverie, M., Ju, J., Masek, J.G., Dungan, J.L., Vermote, E.F., Roger, J.-C., Skakun, S. & Justice, C.O. (2018). The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set. Remote Sensing of Environment, (219), 145-161. https://doi.org/10.1016/j.rse.2018.09.002
  18. Molod, A., Takacs, L., Suarez, M. & Bacmeister, J. (2015). Development of the GEOS-5 Atmospheric General Circulation Model: Evolution from MERRA to MERRA2. Geoscientific Model Development, 8(5), 1339-1356.
  19. Fileccia, T., Guadagni, M., Hovhera, V. & Bernoux, M. (2014). Ukraine: Soil Fertility to Strengthen Climate Resilience. Food and Agriculture Organization of the United Nations. Rome, Italy. http://www.fao.org/3/ai3905e.pdf
  20. Lesiv, M., Schepaschenko, D., Moltchanova, E., Bun, R., Dürauer, M., Prishchepov, A.V. & Kussul, N. (2018). Spatial Distribution of Arable and Abandoned Land Across Former Soviet Union Countries. Scientific Data, (5), art num. 180056.
  21. Shelestov, A., Shumilo, L., Lavreniuk, M., Vasiliev, V., Bulanaya, T., Gomilko, I., Kolotii, A, Medianovskyi, K, & Skakun, S. (2018). Indoor and outdoor air quality monitoring on the base of intelligent sensors for smart city. XVIII International Conference on Data Science and Intelligent Analysis of Information, (836), 134–145.
  22. Lavreniuk, M., Shelestov, A., Kolotii, A., Vasiliev, V., Bulanaya, T. & Gomilko, I. (2018). Air Quality Monitoring in Smart City Using Intelligent Sensors. EGU General Assembly Conference Abstracts, (20), 17087.
  23. Kussul, N., Lavreniuk, M., Kolotii, A., Skakun, S., Rakoid, O. & Shumilo, L. (2019). A workflow for sustainable development goals indicators assessment based on high-resolution satellite data. International Journal of Digital Earth, 13(2), 309-321.
  24. Kussul, N., Lavreniuk, M., Skakun, S. & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778-782.
  25. Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A. & Skakun, S. (2017). Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping. Frontiers in Earth Science, (5), 17.
  26. Shelestov, A., Kolotii, A., Lavreniuk, M., Yailymov, B., Shumilo, L. & Korsunska, Y. (2019). Smart City Services for Kiev City Within ERA-PLANET SMURBS Project. IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), 784-788.
  27. Shelestov, A., Raudner, A., Kolotii, A., Marinosci, I., Attanasio, A., Munafò, M., Lavreniuk, M., Speyer, O., Yailymov, B. & Kussul, N. (2019, 13–17 May). Urban Growth Services Within ERA-PLANET SMURBS Project. 2019 Living Planet Symposium, MiCo – Milano Congressi. Milan, Italy.
  28. Kussul, N., Skakun, S., Shelestov, A. & Kussul, O. (2014). The Use of Satellite SAR Imagery to Crop Classification in Ukraine within JECAM Project. IEEE Geoscience and Remote Sensing Symposium. 1497-1500.
  29. Shelestov, A., Lavreniuk, M., Vasiliev, V., Shumilo, L., Kolotii, A., Yailymov, B. & Yailymova, H. (2019). Cloud approach to automated crop classification using Sentinel-1 imagery. IEEE Transactions on Big Data.
  30. Kussul, N., Kolotii, A., Shelestov, A., Lavreniuk, M., Bellemans, N., Bontemps, S., Defourny, P. & Koetz, B. (2017). Sentinel-2 for agriculture national demonstration in Ukraine: Results and further steps. IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 5842-5845.
  31. Shumilo, L., Kussul, N., Shelestov. A., Korsunska, Y. & Yailymov, B. (2019). Sentinel-3 Urban Heat Island Monitoring and analysis for Kyiv Based on Vector Data. 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT), Leeds, United Kingdom. 131-135.
  32. Shumilo, L., Yailymov, B., Kussul, N., Lavreniuk, M., Shelestov, A. & Korsunska, Y. (2019, 16–18 April). Rivne City Land Cover and Land Surface Temperature Analysis Using Remote Sensing Data. 2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), Kyiv, Ukraine. 813–816.
  33. Shumilo, L., Shelestov, A., Yailymov, B., Korsunska, Y. & Kussul, N. (2019, 13–17 May). Land Surface Temperature estimation for Smart City. 2019 Living Planet Symposium, MiCo – Milano Congressi, Milan, Italy.
  34. Shelestov, A., Kolotii, A., Borisova, T., Turos, O., Milinevsky, G., Gomilko, I., …Kolos, L. (2019). Essential variables for air quality estimation. International Journal of Digital Earth, 13(2), 278–298.
  35. Kussul, N., Kolotii, A., Shelestov, A., Yailymov, B. & Lavreniuk, M. (2017). Land degradation estimation from global and national satellite based datasets within UN program. Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 9th IEEE International Conference, (1), 383-386.
  36. Korsunska, Y., Shumilo, L., Kolotii, A. & Shelestov, A. (2019, 2-6 July). Air Quality Estimation Using Satellite and In-situ Data for Kyiv City within ERA-PLANET Project. 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON), Lviv, Ukraine. 1032–1036.
  37. Shelestov, A., Shumilo, L., Kolotii, A. & Korsunska, Y. (2019, 9-13 September). Air Quality Estimation for the Kyiv City Within ERA-PLANET Project. Earth Observation Phi-Week, Rome, Italy.
  38. Shelestov, A., Kolotii, A., Lavreniuk, M., Medyanovskyi, K., Vasiliev, V., Bulanaya, T., & Gomilko, I. (2018). Air Quality Monitoring in Urban Areas Using in-Situ and Satellite Data Within Era-Planet Project. IGARSS, Valencia, Spain. 1668-1671.

 

            For section 3 

Підрозділ 3.1

  1. The SMURBS SDG Indicator 11.6.2 Earth Observation Platform. Retrieved October 4, 2021, from http://apcg.meteo.noa.gr/sdg1162/
  2. Air Quality Monitoring Map EcoCity. Retrieved October 4, 2021, from https://eco-city.org.ua/
  3. Air Pollution According to SaveDnipro. Retrieved October 4, 2021, from https://www.savednipro.org/about/
  4. Shelestov, A., Yailymova, H., Yailymov, B., Samoilenko, O., Volynskii, M. & Shumilo, L. (2021, 6-8 July). Ground based validation of Copernicus atmosphere monitoring service data for Kyiv. In Proceedings of the IEEE EUROCON 2021–19th International Conference on Smart Technologies, Lviv, Ukraine. 88-91. https://ela.kpi.ua/handle/123456789/48529
  5. AQICN. Retrieved October 4, 2021, from https://aqicn.org/
  6. Sensor Community. Retrieved October 4, 2021, from https://sensor.community/
  7. Luftdaten Monitoring System. Retrieved October 4, 2021, from https://luftdaten.info/
  8. Airly. Retrieved October 4, 2021, from https://airly.eu/
  9. IQAIR. Retrieved October 4, 2021, from https://www.iqair.com/
  10. Heymann, Y. (1994). CORINE Land Cover: Technical Guide; Office for Official Publications of the European Communities. Luxembourg.
  11. Kosmidis, E., Syropoulou, P., Tekes, S., Schneider, P., Spyromitros-Xioufis, E., Riga, M., Charitidis, P., Moumtzidou, A., Papadopoulos, S., Vrochidis, S., et al. (2018). hack AIR: Towards raising awareness about air quality in Europe by developing a collective online platform. ISPRS International Journal of Geo-Information, 7(5), 187. https://doi.org/10.3390/ijgi7050187
  12. Sarigiannis, D.A., Soulakellis, N., Schäfer, K., Tombrou, M., Sifakis, N.I., Assimakopoulos, D., Lointier, M., Dantou, A. & Saisana, M. (2002). ICAROS: An integrated computational environment for the assimilation of environmental data and models for urban and regional air quality. Water, Air, & Soil Pollution: Focus, (2), 641-654.
  13. Vohra, K., Marais, E.A., Suckra, S., Kramer, L., Bloss, W.J., Sahu, R., Gaur, A., Tripathi, S.N., Van Damm, M., Clarisse, L., et al. (2021). Long-term trends in air quality in major cities in the UK and India: A view from space. Atmospheric Chemistry and Physics, (21), 6275-6296.
  14. Ma, G., Hofmann, E.T. (2019). Population, immigration, and air quality in the USA: A spatial panel study. Population and Environment, (40), 283-302.
  15. SMURBS. Retrieved October 4, 2021, from https://smurbs.eu/the-project/
  16. Shelestov, A., Kolotii, A., Borisova, T., Turos, O., Milinevsky, G., Gomilko, I., Bulanay, T., Fedorov, O., Shumilo, L., Pidgorodetska, L., et al. (2019). Essential variables for air quality estimation. International Journal of Digital Earth, (13), 278-298. http://doi.org/10.1080/17538947.2019.1620881
  17. Shelestov, A., Kolotii, A., Lavreniuk, M., Yailymov, B., Shumilo, L. & Korsunska, Y. (2019, 16-18 April). Smart city services for Kiev city within ERA-PLANET SMURBS project. In Proceedings of the 2019 IEEE 39th International Conference on Electronics and Nanotechnology, Kyiv, Ukraine, 784-788.
  18. Copernicus Atmospheric Monitoring Service. Retrieved October 4, 2021, from https://www.regional.atmosphere.copernicus.eu/
  19. World Health Organization. (2016). Report Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease. Geneva, Switzerland. Retrieved October 4, 2021, from https://www.who.int/phe/publications/air-pollution-global-assessment/en/
  20. Air Pollution in Ukraine. Retrieved October 4, 2021, from https://www.saveecobot.com/
  21. Air Pollution in Europe: Real-Time Air Quality Index Visual Map. Retrieved October 4, 2021, from https://aqicn.org/map/europe/
  22. LUN City Air Stations. Retrieved October 4, 2021, from https://misto.lun.ua/air
  23. Air Quality Sensor Performance Evaluation Center. Retrieved October 4, 2021, from https://www.aqmd.gov/aq-spec/sensors
  24. Air Quality Transmitter AQT420. Retrieved October 4, 2021, from https://www.vaisala.com/en/products/weather-environmental-sensors/air-quality-transmitter-aqt420
  25. Register of Approved Types of Measuring Instruments. Retrieved October 4, 2021, from https://legalzvt.kiev.ua/
  26. Data on Air Pollution in Kyiv from the Borys Sreznevsky Central Geophysical Observatory. Retrieved October 4, 2021, from http://cgo-sreznevskyi.kyiv.ua/index.php?fn=lsza&f=lsza
  27. Dijkstra, L., Poelman, H. & Veneri, P. (2019). The EU-OECD Definition of a Functional Urban Area. OECD iLibrary OECD Publishing, Paris.
  28. Global Urban Centers. Retrieved October 4, 2021, from https://ghsl.jrc.ec.europa.eu/download.php?ds=smod
  29. The Humanitarian Data Exchange–Ukraine–Subnational Administrative Divisions. Retrieved October 4, 2021, from https://data.humdata.org/dataset/ukraine-administrative-boundaries-as-of-q2-2017
  30. Wastes Generation in 2018, by Region. Retrieved October 4, 2021, from http://www.ukrstat.gov.ua/operativ/operativ2018/ns/uv/uv_2018_u.htm
  31. Emissions of Pollutants and Greenhouse Gases into the Atmosphere from Stationary Sources of Pollution. Retrieved October 4, 2021, from http://www.ukrstat.gov.ua/operativ/operativ2018/ns/vzap/arch_vzrap_u.htm
  32. Global Human Settlement Layer. Retrieved October 4, 2021, from https://ghsl.jrc.ec.europa.eu/
  33. NASA Population Density. Retrieved October 4, 2021, from https://neo.sci.gsfc.nasa.gov/view.php?datasetId=SEDAC_POP
  34. Kussul, N., Yailymova, H., Drozd, S. & Shelestov, A. (2021, 22-25 September). Validation of the global human settlement layer and NASA population data for Ukraine. In Proceedings of the 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Cracow, Poland.
  35. Validation Report of the CAMS Near-Real Time Global Atmospheric Composition Service. Retrieved October 4, 2021, from https://atmosphere.copernicus.eu/sites/default/files/2021-01/23_CAMS84_2018SC2_D1.1.1_JJA2020.pdf
  36. Shumilo, L., Yailymov, B. & Shelestov, A. (2020, 26 September-2 October). Active fire monitoring service for Ukraine based on satellite data. In Proceedings of the IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2913-2916.
  37. Kwak, S.K. & Kim, J.H. (2017). Statistical data preparation: Management of missing values and outliers. Korean Journal of Anesthesiology, (70), 407-411.
  38. The Current Population of Ukraine. Retrieved October 4, 2021, from https://ukrstat.org/uk/druk/publicat/Arhiv_u/13/Arch_nnas_zb.htm
  39. Population for Districts of Kyiv. Retrieved October 4, 2021, from http://www.kiev.ukrstat.gov.ua/p.php3?c=1123%26lang=1
  40. Shelestov, A., Lavreniuk, M., Vasiliev, V., Shumilo, L., Kolotii, A., Yailymov, B., Kussul, N. & Yailymova, H. (2019). Cloud approach to automated crop classification using Sentinel-1 imagery. IEEE Transactions on Big Data, (6), 572-582.
  41. Kussul, N., Lavreniuk, M., Skakun, S. & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, (14), 778-782. https://doi.org/10.1109/LGRS.2017.2681128
  42. Meteosat Series. Retrieved October 4, 2021, from https://www.eumetsat.int/our-satellites/meteosat-series

 

Підрозділ 3.2.

  1. Canadian Forest Fire Weather Index (FWI) System [Electronic resourse]. https://cwfis.cfs.nrcan.gc.ca/background/summary/fwi
  2. European Forest Fire Information System [Electronic resourse]. http://effis.jrc.ec.europa.eu
  3. FIRMS FAQ – Earthdata – NASA.https://earthdata.nasa.gov/faq/fims-faq
  4. Van Wagner, C.E. (1987). Development and structure of the canadian forest fire weather index system. Canadian Forest Service.
  5. Stocks, B.J., Lawson, B.D., Alexander, M.E., Van Wagner, C.E., McAlpine, R.S., Lynham, T.J. & Dube, D E. (1989). The Canadian Forest Fire Danger Rating System: an overview. The Forestry Chronicle, (65), 450-457.
  6. Dowdy, A.J., Mills, G.A., Finkele, K. & de Groot, W. (2009). Australian fire weather as represented by the McArthur Forest Fire Danger Index and the Canadian Forest Fire Weather Index. Technical Report No. 010, CAWCR: Melbourne, Australia.  http://www.cawcr.gov.au/technical-reports/CTR_010.pdf
  7. Kussul, N. (Ed.) (2021). Information technology for assessing fire danger and monitoring fires in natural ecosystems based on satellite data [Unpublished report]. Institute of Space Research of the National Academy of Sciences of Ukraine and State Space Agency of Ukraine] [in Ukrainian]. https://nrfu.org.ua/wp-content/uploads/2022/01/2020.01_0268_kussul_083_01.2020_zz-1-stysnuto.pdf
  8. Pidgorodetska, L., Zyelyk, Y., Kolos, L. & Fedorov, O. (2021, 11–14 May). Surface soil moisture deficit assessment based on satellite data. Geoinformatics 2021 [Conference paper]. Kyiv, Ukraine, (2021), 1-6. http://doi.org/10.3997/2214-4609.20215521114
  9. Keetch, J.J. & Byram, G.M. (1968). A drought index for forest fire control. Res. Paper SE-38. U.S. Department of Agriculture Forest Service, Southeastern Forest Experiment Station, Asheville, NC, 32.
  10. Kostjakov, A.N. (1960). Basics of melioration. Selkhozgiz. [in Russian].
  11. Vlasova, O.V., Samoilenko, L.I., Pidhorodetska, L.V. & Kolos, L.M. (2009), Maize moisture deficit according to remote sensing of the Earth. Land reclamation and water management, (96), 58-64 [in Ukrainian].
  12. Campbell, G.S. & Campbell, C.S. (2005). Water Content and Potential, Measurement. Encyclopedia of Soils in the Environment (D. Hillel, Ed.). Elsevier, 253-257. http://doi.org/10.1016/B0-12-348530-4/00502-6
  13. Hengl, T. et al. (2017). SoilGrids250m: Global Gridded Soil Information Based on Machine Learning. PLOS ONE, 21. http://doi.org/10.1371/journal.pone.0169748
  14. Zadorozhnii, Yu.V. & Smirnova, I.V. (2018). Agricultural land reclamation: methodical recommendations for carrying out practical classes. Mykolaiv National Agrarian University. http://dspace.mnau.edu.ua/jspui/bitstream/123456789/4355/1/Silskohospodarska%20melioratsiia.pdf [in Ukrainian].
  15. Volkov, V.N. & Narimanjanc, E.V. (1972). Composition, physical and physico-chemical properties of soils: teaching aid. http://window.edu.ru/resource/887/68887/files/rsu664.pdf [Last accessed: 28.08.2021] [in Russian].
  16. Ajvazjan, S.A. (2003). Towards Methodologies for Measuring Statistical Categories of the Quality of Life of the Population. Economics and mathematical methods, 39(2), 33-53 [in Russian].
  17. Ajvazjan, S. & Mhitarjan, V. (2001). Applied statistics. Fundamentals of Econometrics: in 2 volumes. Vol. 1: Probability Theory and Applied Statistics. Unity. [in Russian].
  18. Keetch–Byram drought index.  http://en.wikipedia.org/wiki/Keetch%E2%80%93Byram_drought_index
  19. Garcia-Pratsa, A., Del Campo, A., Tarcнsio Fernandes, J. G. & Antonio Molina, J. (2015). Development of a Keetch and Byram-Based drought index sensitive to forest management in Mediterranean conditions. Agricultural and Forest Meteorology, (205), 40-50.
  20. Burgan, R.E. (1993). A method to initialize the Keetch-Byram drought index. Western Journalof AppliedForestry, 8(4), 109-115.
  21. National Fire Danger Rating System.  http://www.fs.usda.gov/detail/cibola/landmanagement/resourcemanagement/?cid=stelprdb5368839
  22. ERA5-Land hourly data from 1981 to present.  http://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview
  23. Tóth, B., Weynants, M., Pásztor, L. & Hengl, T. (2017). 3D soil hydraulic database of Europe at 250 m resolution. Hydrological Processes, 31(14), 2662-2666. http://doi.org/10.1002/hyp.11203 (Last accessed: 28.08.2021).
  24. Panagos, P., Van Liedekerke, M., Jones, A. & Montanarella, L. (2012). European Soil Data Centre: Response to European policy support and public data requirements. Land Use Policy, 29(2), 329-338. http://doi.org/10.1016/j.landusepol.2011.07.003 (Last accessed: 28.08.2021).
  25. European Soil Data Centre (ESDAC). http://esdac.jrc.ec.europa.eu
  26. Bosilovich, M.G., Lucchesi, R. & Suarez, M. (2016). MERRA-2: File Specification. GMAO Office Note No. 9 (Version 1.1). 73.  http://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich785.pdf
  27. Bauer-Marschallinger, B. & Paulik, C. (2019). Surface Soil Moisture Collection 1 km Version 1. Product User Manual. Issue I1.30. http://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_SSM1km-V1_I1.30.pdf
  28. Fire danger (Canadian Fire Weather Index system) in the Global Wildfire Information System (version 2-3-1). (2018). European Commission, Joint Research Centre (JRC) [Dataset]. http://data.europa.eu/89h/4b4f1ea6-da7b-4870-9be4-aa5b9a2a4b70
  29. Olofsson, P., et al. (2013). Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing of Environment, (129), 122-131.
  30. The State Emergency Service of Ukraine] [in Ukrainian]. http://www.dsns.gov.ua

 

                For section 4

  1. Ministry of Economic Development and Trade of Ukraine (2017). Goals of sustainable development: Ukraine. National report. http://menr.gov.ua/files/docs/Національна%20доповідь%20ЦСР%20України_липень%202017%20ukr.pdf [in Ukrainian].
  2. Fedorov, O.P., Samoilenko, L.I., Kolos, L.M. & Pidhorodetska L.V. (2019). Problems of using satellite data to the assessment of sustainable development goals of Ukraine. Space Science and Technology, 25(3), 40-56. https://doi.org/10.15407/knit2019.03.040 [in Ukrainian].
  3. United Nations Economic and Social Council (2018, 6-9 March). Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators. 49 session. https://unstats.un.org/unsd/statcom/49th-session/documents/2018-2-SDG-IAEG-E.pdf
  4. Earth Observations in Support of the 2030 Agenda for Sustainable Development. http://www.earthobservations.org/documents/publications/201703_geo_eo_for_2030_agenda.pdf
  5. Reyers, B., Stafford-Smith, M., Erb, K.-H., Scholes, R.J. & Selomane, O. (2017). Essential variables help to focus sustainable development goals monitoring. Current Opinion in Environmental Sustainability, (26), 97-105. https://doi.org/10.1016/j.cosust.2017.05.003
  6. Lehmann et al. (2019). GEOEssential – mainstreaming workflows from data sources to environment policy indicators with essential variables. International Journal of Digital Earth. https://doi.org/10.1080/17538947.2019.1585977
  7. ConnectingGEO. (2016). D2.1: Navigating Sustainability in a changing planet. https://ddd.uab.cat/pub/worpap/2016/146881/D2_1_SDG_workshop_report.pdf
  8. Masò, J., Serral, I., Domingo-Marimon, C. & Zabala Torres, A. (2019). Earth Observations for Sustainable Developement Goals Monitoring Based on Essential Variables and Driver-Pressure-State-Impact-Repsonse Indicators. International Journal of Digital Earth. https://doi.org/10.1080/17538947.2019.1576787
  9. UN. SDG Indicators. Metadata repository. https://unstats.un.org/sdgs/metadata
  10. Kuffer, M., Wang, J., Nagenborg, M., Pfeffer, K., Kohli, D., Sliuzas, R. & Persello, C. (2018). The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator. ISPRS International Journal of Geo-Information, (7), 428. https://www.mdpi.com/2220-9964/7/11/428
  11. Copernicus Global Land Service. https://land.copernicus.eu/global/products/ba
  12. Shelestov, A., Yailymova, H., Yailymov, B. et. al. (2021). The Wetland Map Validation for Ukraine. 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications. Cracow, Poland (virtual format). 343-376. https://doi.org/10.1109/IDAACS53288.2021.9661037
  13. Kolotii, A., Kussul, N., Shelestov, A., Skakun, S., Yailymov, B., Basarab, R., Lavreniuk, M., Oliinyk, T. & Ostapenko, V. (2015). Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Archives, 40(7W3), 39-44. https://doi.org/10.5194/isprsarchives-XL-7-W3-39-2015
  14. Kussul, N., Lavreniuk, M., Shelestov, A. et al. (2018). Crop inventory at regional scale in Ukraine: Developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery. European Journal of Remote Sensing, 51(1), 627-636. https://doi.org/10.1080/22797254.2018.1454265
  15. Hosseini, M., McNairn, H., Mitchell, S. et al. (2021). A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index. Remote Sensing, 13(7), 1-20.  https://doi.org/10.3390/rs13071348
  16. Kussul, N., Shelestov, A., Lavreniuk, M. et. al. (2016). Deep learning approach for large scale land cover mapping based on remote sensing data fusion. [Paper presentation]. International Geoscience and Remote Sensing Symposium (IGARSS), 198-201.
  17. Shelestov, A., Lavreniuk, M., Vasiliev, V. et. al. (2020). Cloud Approach to Automated Crop Classification Using Sentinel-1 Imagery.IEEE Transactions on Big Data, 6(3), 572-582. https://doi.org/10.1109/TBDATA.2019.2940237
  18. Kussul’, N.N., Kravchenko, A.M., Skakun, S.V. et. al. (2012). Regression models for estimating the yield of agricultural crops based on MODIS data. Modern problems of remote sensing of the Earth from space, 9(1), 95-107 [in Russian].
  19. Kussul’, N.N. & Shelestov, A.Ju. (2008). Grid-systems for Earth exploration tasks: Architecture, models and technologies. Naukova dumka. [in Russian].
  20. Shelestov, A., Lavreniuk, M., Vasiliev V. et al. (2019). Cloud approach to automated crop classification using Sentinel-1 imagery./IEEE Transactions on Big Data, 6(3), 572-582.
  21. Azarskov, V.N., Blohin, L.N., Zhiteckij, L.S. & Kussul’, N.N. (2004). Robust methods for estimation, identification and adaptive control. National Aviation University of Ukraine [in Russian].
  22. Makarichev, V., Vasilyeva, I., Lukin, V., Vozel, B., Shelestov, A. & Kussul, N. (2022). Discrete Atomic Transform-Based Lossy Compression of Three-Channel Remote Sensing Images with Quality Control. Remote Sensing, 14(1), 125. https://doi.org/10.3390/rs14010125

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