Proceedings of International Conference on Applied Innovation in IT
2022/03/09, Volume 10, Issue 1, pp.113-117

Information Technology for Land Degradation Assessment Based on Remote Sensing


Nataliia Kussul, Andrii Shelestov, Leonid Shumilo, Dmytro Titkov, Hanna Yailymova


Abstract: Since the launch of ESA Copernicus program, satellite data of high resolution became publicly available and methods and tools for their automated processing to solve a wide range of applications have developed rapidly. An important scientific task is to assess land degradation and achieve zero levels of degradation. There are many methods for determining land degradation. Known approaches to the tasks of environmental land monitoring usually use the same methodology for all types of land cover. The paper represents the approach to the calculation of land degradation based on remote sensing data and modelling results taking into account the specifics of land degradation for different land cover and land use types. Our method is based on the classification of different land cover and land use types from satellite imagery and application of different schemes of land degradation assessment for each of them. We consider forest cuts as land degradation for forests and assess them using deep learning models. Land degradation for croplands is estimated by comparison of real leaf area index (LAI) and ideal LAI, calculated with the bio-physical crop development model. And land degradation for grassland is determined with a traditional approach based on vegetation index NDVI extracted from satellite imagery. The proposed approach was implemented for the territory of Ukraine.

Keywords: Geospatial Data Analysis, Machine Learning, Land Degradation, Remote Sensing, Land Cover

DOI: 10.25673/76941

Download: PDF

References:

  1. A. Lehmann,Y.Guigoz, N. Ray, E. Mancosu,K. C. Abbaspour, E. R. Freund, and G. Giuliani, “A web platform for landuse, climate, demography, hydrology andbeach erosion in the Black Sea catchment”, Scientific data,2017, vol. 4 (1), pp.1-15.
  2. A. Kolotii, N. Kussul, A. Shelestov, S. Skakun, B. Yailymov,R.Basarab, and V. Ostapenko, “Comparison of biophysicaland satellite predictors for wheat yield forecasting in Ukraine.International Archives of the Photogrammetry”, RemoteSensing and Spatial Information Sciences - ISPRSArchives, 2015, vol. 40 (7W3), pp. 39-44.
  3. N. Kussul, et al., “Crop inventory at regional scale inUkraine: developing in season and end of season crop mapswith multi-temporal optical and SAR satellite imagery”,European Journal of Remote Sensing 51.1, 2018,pp. 627-636.
  4. A. N. Kravchenko, N. N. Kussul, E. A. Lupian,V. P. Savorsky, L. Hluchy, and A. Y. Shelestov, “Water resource quality monitoring using heterogeneous data and high-performance computations”, Cybernetics and Systems Analysis, 2008, vol. 44(4), pp. 616-624.
  5. G. Giuliani, P. Mazzetti, M. Santoro, S. Nativi, J. Van Bemmelen, G. Colangeli, and A. Lehmann, “Knowledgegeneration using satellite earth observations to supportsustainable development goals (SDG): A use case on Landdegradation”, International Journal of Applied EarthObservation and Geoinformation, 2020, no. 88, p. 102068.
  6. G. Giuliani, B. Chatenoux, A. Benvenuti, P. Lacroix, M.Santoro, P. Mazzetti, “Monitoring land degradation atnational level using satellite Earth Observation time-seriesdata to support SDG15–exploring the potential of data cube”,Big Earth Data, 2020, vol. 4(1), pp. 3-22.
  7. M. Claverie, J. Ju, J. G. Masek, J. L.Dungan, E. F. Vermote,J. C. Roger, C. Justice, “The Harmonized Landsat andSentinel-2 surface reflectance data set”, Remote sensing of environment, 2018, no. 219, pp. 145-161.
  8. N. Kussul, A. Shelestov, M. Lavreniuk, I. Butko, S. Skakun, “Deep learning approach for large scale land cover mappingbased on remote sensing data fusion”, Geoscience andRemote Sensing Symposium (IGARSS), 2016, pp.198-201.
  9. N. Kussul, G. Lemoine, J. Gallego, S. Skakun, and M. Lavreniuk, “Parcel based classification for agriculturalmapping and monitoring using multi-temporal satellite imagesequences,” 2015 IEEE International Geoscience and RemoteSensing Symposium (IGARSS), 2015, pp. 165-168.
  10. A. Shelestov, M. Lavreniuk, V. Vasiliev, L. Shumilo,A. Kolotii, B. Yailymov, H.Yailymova, “Cloud approach toautomated crop classification using Sentinel-1 imagery”,IEEE Transactions on Big Data, 2019, vol. 6(3), pp. 572-582.

    HOME

       - Call for Papers
       - For authors
       - Important Dates
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceedings


    PROCEEDINGS

       - Volume 12, Issue 1 (ICAIIT 2024)        - Volume 11, Issue 2 (ICAIIT 2023)
       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


    PAST CONFERENCES

       ICAIIT 2024
         - Photos
         - Reports

       ICAIIT 2023
         - Photos
         - Reports

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports

    ETHICS IN PUBLICATIONS

    ACCOMODATION

    CONTACT US

 

DOI: http://dx.doi.org/10.25673/115729


        

         Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0


                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License


           ISSN 2199-8876
           Publisher: Anhalt University of Applied Sciences

        site traffic counter

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.