Skip to main content
SpringerLink
Log in

Assessment of Sustainable Development Goals Achieving with Use of NEXUS Approach in the Framework of GEOEssential ERA-PLANET Project

  • Conference paper
  • First Online:
Recent Developments in Data Science and Intelligent Analysis of Information (ICDSIAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 836))

Abstract

In this paper, we propose methodology for calculating indicators of sustainable development goals within the GEOEssential project, that is a part of ERA-PLANET Horizon 2020 project. We consider 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. For this, we used remote sensing data, weather and climatic models’ data and in-situ data. Accurate land cover maps are important for precisely land cover changes assessment. To improve the resolution and quality of existing global land cover maps, we proposed our own deep learning methodology for country level land cover providing. For calculating essential variables, that are vital for achieving indicators, NEXUS approach based on idea of fusion food, energy, and water was applied. Long-term land cover change maps connected with land productivity maps are essential for determining environment changes and estimation of consequences of anthropogenic activity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Group on Earth Observation, https://www.earthobservations.org/.

  2. 2.

    The European Network of observing our Changing Planet, http://www.era-planet.eu.

  3. 3.

    BioMA (Biophysical Models Applications) framework, http://bioma.jrc.ec.europa.eu/.

  4. 4.

    JRC Science for Policy Report, http://publications.jrc.ec.europa.eu/repository/bitstream/JRC80540/lb-na-26500-en-n%20.pdf.

References

  1. Skakun, S., Kussul, N., Shelestov, A., Kussul, O.: The use of satellite data for agriculture drought risk quantification in Ukraine. Geomat. Nat. Hazards Risk 7(3), 901–917 (2016)

    Article  Google Scholar 

  2. Kussul, N., Sokolov, B., Zyelyk, Y., Zelentsov, V., Skakun, S., Shelestov, A.: Disaster risk assessment based on heterogeneous geospatial information. J. Autom. Inf. Sci. 42(12), 32–45 (2010)

    Article  Google Scholar 

  3. Shelestov, A.Y.: Using the fuzzy-ellipsoid method for robust estimation of the state of a grid system node. Cybern. Syst. Anal. 44(6), 847–854 (2008)

    Article  MathSciNet  Google Scholar 

  4. Kussul, N., Shelestov, A., Skakun, S., Kravchenko, O.: Data assimilation technique for flood monitoring and prediction. Institute of Information Theories and Applications FOI ITHEA (2008)

    Google Scholar 

  5. Mandl, D., Frye, S., Cappelaere, P., Handy, M., Policelli, F., Katjizeu, M.: Use of the earth observing one (EO-1) satellite for the namibia sensorweb flood early warning pilot. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(2), 298–308 (2013)

    Article  Google Scholar 

  6. Lavreniuk, M., Skakun, S., Shelestov, A., Yalimov, B., Yanchevskii, S., Yaschuk, D., Kosteckiy, A.: Large-scale classification of land cover using retrospective satellite data. Cybern. Syst. Anal. 52(1), 127–138 (2016)

    Article  Google Scholar 

  7. Skakun, S., Kussul, N., Shelestov, A., Lavreniuk, M., Kussul, O.: Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(8), 3712–3719 (2016)

    Article  Google Scholar 

  8. Kussul, N., Lemoine, G., Gallego, F., Skakun, S., Lavreniuk, M., Shelestov, A.: Parcel-based crop classification in Ukraine using Landsat-8 data and Sentinel-1A data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(6), 2500–2508 (2016)

    Article  Google Scholar 

  9. Lavreniuk, M., Kussul, N., Meretsky, M., Lukin, V., Abramov, S., Rubel, O.: Impact of SAR data filtering on crop classification accuracy. In: 2017 First Ukraine Conference on Electrical and Computer Engineering, pp. 912–916 (2017)

    Google Scholar 

  10. Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y.: Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2094–2107 (2014)

    Article  Google Scholar 

  11. Geng, J., Fan, J., Wang, H., Ma, X., Li, B., Chen, F.: High-resolution SAR image classification via deep convolutional autoencoders. IEEE Geosci. Remote Sens. Lett. 12(11), 2351–2355 (2015)

    Article  Google Scholar 

  12. Hu, F., Xia, G.S., Hu, J., Zhang, L.: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 7(11), 14680–14707 (2015)

    Article  Google Scholar 

  13. Chen, Y., Zhao, X., Jia, X.: Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2381–2392 (2015)

    Article  Google Scholar 

  14. Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A.: Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sens. Lett. 14(5), 778–782 (2017)

    Article  Google Scholar 

  15. Shelestov, A., Kravchenko, A., Skakun, S., Voloshin, S., Kussul, N.: Geospatial information system for agricultural monitoring. Cybern. Syst. Anal. 49(1), 124–132 (2013)

    Article  Google Scholar 

  16. Kogan, F., et al.: Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models. Int. J. Appl. Earth Observ. Geoinf. 23, 192–203 (2013)

    Article  Google Scholar 

  17. Kolotii, A., et al.: Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. Int. Arch. Photogram. Remote Sens. Spatial Inf. Sci. 40(7), 39 (2015)

    Article  Google Scholar 

  18. Kussul, N., Kolotii, A., Shelestov, A., Yailymov, B., Lavreniuk, M.: Land degradation estimation from global and national satellite based datasets within UN program. In: Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), pp. 383–386 (2017)

    Google Scholar 

  19. Kussul, N., Kolotii, A., Adamenko, T., Yailymov, B., Shelestov, A., Lavreniuk, M.: Ukrainian cropland through decades: 1990–2016. In: Electrical and Computer Engineering (UKRCON), pp. 856–860 (2017)

    Google Scholar 

  20. Shelestov, A., Kolotii, A., Skakun, S., Baruth, B., Lozano, R.L., Yailymov, B.: Biophysical parameters mapping within the SPOT-5 Take 5 initiative. Eur. J. Remote Sens. 50(1), 300–309 (2017)

    Article  Google Scholar 

  21. Kussul, N., Shelestov, A., Skakun, S., Li, G., Kussul, O.: The wide area grid testbed for flood monitoring using earth observation data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(6), 1746–1751 (2012). Article Number 6236227

    Article  Google Scholar 

  22. Kussul, N., Skakun, S., Shelestov, A., Kussul, O.: The use of satellite SAR imagery to crop classification in Ukraine within JECAM project. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1497–1500 (2014). Article Number 6946721

    Google Scholar 

  23. Kussul, N., Skakun, S., Shelestov, A., Kravchenko, O., Gallego, J.F., Kussul, O.: Crop area estimation in Ukraine using satellite data within the MARS project. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3756–3759 (2012). Article Number 6350500

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Space Research Institute NASU-SSAU, Kyiv, Ukraine

    Nataliia Kussul, Mykola Lavreniuk, Leonid Sumilo, Andrii Kolotii, Bohdan Yailymov & Andrii Shelestov

  2. Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine

    Nataliia Kussul, Mykola Lavreniuk, Leonid Sumilo, Andrii Kolotii, Bohdan Yailymov & Andrii Shelestov

  3. EOS Data Analytics, Kyiv, Ukraine

    Mykola Lavreniuk, Leonid Sumilo, Andrii Kolotii, Bohdan Yailymov, Andrii Shelestov & Vladimir Vasiliev

  4. National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine

    Olena Rakoid

Authors
  1. Nataliia Kussul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mykola Lavreniuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leonid Sumilo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrii Kolotii
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Olena Rakoid
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bohdan Yailymov
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andrii Shelestov
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Vladimir Vasiliev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mykola Lavreniuk .

Editor information

Editors and Affiliations

  1. Applied Mathematics Department, Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine

    Oleg Chertov

  2. Department of Economics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Tymofiy Mylovanov

  3. Department of Intelligent Information Systems, Petro Mohyla Black Sea National University, Mykolaiv, Ukraine

    Yuriy Kondratenko

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  5. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  6. Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia

    Vadim Stefanuk

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kussul, N. et al. (2019). Assessment of Sustainable Development Goals Achieving with Use of NEXUS Approach in the Framework of GEOEssential ERA-PLANET Project. In: Chertov, O., Mylovanov, T., Kondratenko, Y., Kacprzyk, J., Kreinovich, V., Stefanuk, V. (eds) Recent Developments in Data Science and Intelligent Analysis of Information. ICDSIAI 2018. Advances in Intelligent Systems and Computing, vol 836. Springer, Cham. https://doi.org/10.1007/978-3-319-97885-7_15

Download citation

Publish with us

Policies and ethics

Search

Navigation