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

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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.


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


        

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