Information Technology for Land Degradation Assessment Based on Remote Sensing

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dc.contributor.authorKussul, Nataliia
dc.contributor.authorShelestov, Andrii
dc.contributor.authorShumilo, Leonid
dc.contributor.authorTitkov, Dmytro
dc.contributor.authorYailymova, Hanna
dc.date.accessioned2022-07-07T20:03:32Z
dc.date.available2022-07-07T20:03:32Z
dc.date.issued2022
dc.description.abstractenSince 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.uk
dc.description.sponsorshipNational Research Foundation of Ukraine within the project 2020.02/0284 «Geospatial models and information technologies of satellite monitoring of smart city problems» and Horizon 2020 e-shape project (https://e-shape.eu/)uk
dc.format.pagerangeP. 113-117uk
dc.identifier.citationInformation Technology for Land Degradation Assessment Based on Remote Sensing / Kussul, N., Shelestov, A., Shumilo, L., Titkov, D., Yailymova, H. // The 10th International Conference on Applied Innovation in IT (ICAIIT 2022), March 09, 2022, Koethen, Germany. - Koethen : Anhalt University of Applied Sciences, 2022. - P. 113-117.uk
dc.identifier.issn2199-8876
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/48532
dc.language.isoenuk
dc.publisherAnhalt University of Applied Sciencesuk
dc.publisher.placeKoethenuk
dc.sourceThe 10th International Conference on Applied Innovation in IT (ICAIIT 2022), March 09, 2022, Koethen, Germany.uk
dc.subjectGeospatial Data Analysisuk
dc.subjectMachine Learninguk
dc.subjectLand Degradationuk
dc.subjectRemote Sensinguk
dc.subjectLand Coveruk
dc.titleInformation Technology for Land Degradation Assessment Based on Remote Sensinguk
dc.typeArticleuk

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