Spatio-temporal analysis of surface water extraction methods reliability using COPERNICUS satellite data

dc.citation.epage18
dc.citation.issue1 (34)
dc.citation.journalTitleГеодинаміка
dc.citation.spage5
dc.contributor.affiliationКошицький технічний університет
dc.contributor.affiliationTechnical University of Košice
dc.contributor.authorКсенак, Любомир
dc.contributor.authorБартош, Кароль
dc.contributor.authorПуканська, Катаріна
dc.contributor.authorКішеля, Каміль
dc.contributor.authorKseňak, Ľubomir
dc.contributor.authorBartoš, Karol
dc.contributor.authorPukanská, Katarina
dc.contributor.authorKyšeľa, Kamil
dc.coverage.placenameЛьвів
dc.date.accessioned2024-02-13T09:29:35Z
dc.date.available2024-02-13T09:29:35Z
dc.date.created2023-06-26
dc.date.issued2023-06-26
dc.description.abstractМетою цього дослідження є порівняння та подальша оцінка придатності використання SAR (радара із синтетичною апертурою) та мультиспектральних (MSI) супутникових даних програми Copernicus для картографування та точної ідентифікації поверхневих водних тіл, враховуючи раптові зміни, спричинені значними кліматичними впливами. Методологія виділення наземних навігацій для видалення радіолокаційних шумів, то для цієї мети найкраще підходять фільтри Lee і Lee Sigma. Використовуваний розмір вікна залежить від конкретного типу об’єкта, а також від його просторового розміру. Екстракція водних поверхонь із зображення MSI обробляється за допомогою нормалізованого індексу різниці води (NDWI), модифікованого нормалізованого індексу різниці води (MNDWI), пари індексів автоматичного індексу вилучення води (AWEI) та індексу співвідношення води (WRI). Оцінка отриманих значень вилучення – графічна та числення – для уточнення результатів (з використанням кількісних показників точності). Автоматичне виділення водних поверхонь із зображень MSI у середовищі платформи GEE є порівняно точним, швидким і ефективним інструментом для визначення справжнього рівня ґрунтових вод. Підсумовуючи, можна сказати, що результати цих досліджень дають змогу достовірніше оцінювати раптові гідрологічні зміни, спричинені міжрічними коливаннями водойм країни. У поєднанні з різночасовим моніторингом цих змін вони можуть бути ефективним інструментом постійного моніторингу повеней і посух. передбачає стандартну попередню обробку зображень SAR і завершення визначення порогових значень у генерації бінарної маски. Опрацювання зображень MSI охоплює автоматичну алгоритмічну обробку та подальшу генерацію водяних масок через хмарну платформу Google Earth Engine. Результати опрацювання зображення SAR показують, що тип конфігурації поляризації VV (вертикальна–-вертикальна) є відповідним типом поляризації. Якщо брати інструменти фільтрації
dc.description.abstractThe aim of this research is the comparison and subsequent evaluation of the suitability of using SAR (Synthetic Aperture Radar) and multispectral (MSI) satellite data of the Copernicus program for mapping and accurate identification of surface water bodies. The paper considers sudden changes caused by significant climatological-meteorological influences in the country. The surface guidance extraction methodology includes the standard preprocessing of SAR images and concluding the determination of threshold values in binary mask generation. For MSI images, water masks are generated through automatic algorithmic processing on the Google Earth Engine cloud platform. During SAR image processing, it has been found that the VV polarization configuration type (vertical-vertical) is the most suitable. The Lee and Lee Sigma filters are recommended for eliminating radar noise. The chosen window size for filtering depends on the specific object and its spatial extent. The extraction of water surfaces from the MSI image is conducted using the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), a pair of Automated Water Extraction Index (AWEI) indices, and Water Ratio Index (WRI). Results are evaluated both graphically and numerically, using quantitative accuracy indicators to refine them. Automatic extraction of water surfaces from MSI images in the GEE platform environment is a fast, efficient, and relatively accurate tool for determining the true extent of groundwater. In conclusion, this research can provide more reliable estimates of hydrological changes and interannual variations in water bodies in the country. When combined with multitemporal monitoring, these results can be an effective tool for permanent monitoring of floods and droughts.
dc.format.extent5-18
dc.format.pages14
dc.identifier.citationSpatio-temporal analysis of surface water extraction methods reliability using COPERNICUS satellite data / Ľubomir Kseňak, Karol Bartoš, Katarina Pukanská, Kamil Kyšeľa // Geodynamics. — Lviv Politechnic Publishing House, 2023. — No 1 (34). — P. 5–18.
dc.identifier.citationenSpatio-temporal analysis of surface water extraction methods reliability using COPERNICUS satellite data / Ľubomir Kseňak, Karol Bartoš, Katarina Pukanská, Kamil Kyšeľa // Geodynamics. — Lviv Politechnic Publishing House, 2023. — No 1 (34). — P. 5–18.
dc.identifier.doidoi.org/10.23939/jgd2023.01.005
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/61311
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofГеодинаміка, 1 (34), 2023
dc.relation.ispartofGeodynamics, 1 (34), 2023
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dc.relation.referencesenBurshtynska, Kh. V., Babushka, A. V., Bubniak, I. M., Babiy, L. V., & Tretyak, S. K. (2019). Influence of geological structures on the nature of riverbed displacements for the rivers of the Dnister basin upper part. Geodynamics, 2(27), 24–38. https://doi.org/10.23939/jgd2019.02.024
dc.relation.referencesenBurshtynska, Kh. V., Tretyak, S., & Halockin, M. (2017). Study of horizontal displacements of the channel of Dniester river using remote sensing data and GIS-technologies. Geodynamics, 2(23), 14–24. https://doi.org/10.23939/jgd2017.02.014 (in Ukrainian)
dc.relation.referencesenCao, H., Zhang, H., Wang, C., & Zhang, B. (2019). Operational flood detection using sentinel-1 SAR data over large areas. Water, 11(4), 786. https://doi.org/10.3390/w11040786
dc.relation.referencesenChen, F., Chen, X., Van de Voorde, T., Roberts, D., Jiang, H., & Xu, W. (2020). Open water detection in urban environments using high spatial resolution remote sensing imagery. Remote Sensing of Environment, 242, 111706. https://doi.org/10.1016/j.rse.2020.111706
dc.relation.referencesenClement, M. A., Kilsby, C. G., & Moore, P. (2017). Multi-temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, 11(2), 152–168. https://doi.org/10.1111/jfr3.12303
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dc.relation.referencesenFang-fang, Z., Bing, Z., Jun-sheng, L., Qian, S., Yuanfeng, W., & Yang, S. (2011). Comparative analysis of automatic water identification method based on multispectral remote sensing. Procedia Environmental Sciences, 11, 1482–1487. https://doi.org/10.1016/j.proenv.2011.12.223
dc.relation.referencesenFerro-Famil, L., & Pottier, E. (2016). 1 - Synthetic Aperture Radar Imaging. Microwave Remote
dc.relation.referencesenSensing of Land Surface. Elsevier. pp. 1-65. ISBN 9781785481598 https://doi.org/10.1016/B978-1-78548-159-8.50001-3
dc.relation.referencesenFeyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated Water Extraction Index: A new technique for surface water mapping using landsat imagery. Remote Sensing of Environment, 140, 23–35. https://doi.org/10.1016/j.rse.2013.08.029
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dc.relation.referencesenHlotov, V., & Biala, M. (2022). Spatial-temporal geodynamics monitoring of land use and land cover changes in Stebnyk, Ukraine based on Earth remote sensing data. Geodynamics, 1(32), 5–15. https://doi.org/10.23939/jgd2022.02.005
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dc.relation.referencesenJiang, W., He, G., Pang, Z., Guo, H., Long, T., & Ni, Y. (2019). Surface water map of China for 2015 (SWMC-2015) derived from Landsat 8 satellite imagery. Remote Sensing Letters, 11(3), 265–273. https://doi.org/10.1080/2150704x.2019.1708501
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dc.relation.referencesenLi, W., Du, Z., Ling, F., Zhou, D., Wang, H., Gui, Y., Sun, B., & Zhang, X. (2013). A comparison of land surface water mapping using the Normalized Difference Water Index from Tm, ETM+ and Ali. Remote Sensing, 5(11), 5530–5549. https://doi.org/10.3390/rs5115530
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dc.relation.referencesenPakshyn, M., Liaska, I., Kablak, N., & Yaremko, H. (2021). Investigation of the mining departments influence of Solotvynsky salt mine SE on the Earth surface, buildings and constructions using satelite radar monitoring. Geodynamics, 2(31), 41–52. https://doi.org/10.23939/jgd2021.02.041
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dc.relation.referencesenPukanská, K., Bartoš, K., Bakoň, M., Papčo, J., Kubica, L., Barlák, J., Rovňák, M., Kseňak, Ľ., Zelenakova, M., Savchyn, I., & Perissin, D. (2023). Multi-sensor and multi-temporal approach in monitoring of deformation zone with permanent monitoring solution and management of environmental changes: A case study of solotvyno salt mine, Ukraine. Frontiers in Earth Science, 11. https://doi.org/10.3389/feart.2023.1167672
dc.relation.referencesenPulvirenti, L., Pierdicca, N., Chini, M., & Guerriero, L. (2013). Monitoring flood evolution in vegetated areas using COSMO-SkyMed data: The Tuscany 2009 case study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(4), 1807-1816. doi: 10.1109/JSTARS.2012.2219509.
dc.relation.referencesenSekertekin, A. (2020). A survey on global thresholding methods for mapping open water body using sentinel-2 satellite imagery and Normalized Difference Water Index. Archives of Computational Methods in Engineering, 28(3), 1335–1347. https://doi.org/10.1007/s11831-020-09416-2
dc.relation.referencesenShen, L., & Li, C. (Eds.). (2010). Water Body Extraction from Landsat ETM+ Imagery Using Adaboost Algorithm. Proceedings of 18th International Conference on Geoinformatics. Beijing, China: IEEE.
dc.relation.referencesenSun, F., Sun, W., Chen, J., & Gong, P. (2012). Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. International Journal of Remote Sensing, 33(21), 6854–6875. https://doi.org/10.1080/01431161.2012.692829
dc.relation.referencesenTsyganskaya, V., Martinis, S., Marzahn, P., & Ludwig, R. (2018). Detection of temporary flooded vegetation using sentinel-1 time series data. Remote Sensing, 10(8), 1286. https://doi.org/10.3390/rs10081286
dc.relation.referencesenTsyganskaya, V., Martinis, S., & Marzahn, P. (2019). Flood monitoring in vegetated areas using multitemporal sentinel-1 data: Impact of time series features. Water, 11(9), 1938. https://doi.org/10.3390/w11091938
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dc.rights.holder© Інститут геології і геохімії горючих копалин Національної академії наук України, 2023
dc.rights.holder© Інститут геофізики ім. С. І. Субботіна Національної академії наук України, 2023
dc.rights.holder© Національний університет «Львівська політехніка», 2023
dc.rights.holder© Ľ. Kseňak, K. Bartoš, K. Pukanská, K. Kyšeľa
dc.subjectДЗЗ
dc.subjectповерхневі води
dc.subjectрадар із синтетичною апертурою
dc.subjectSentinel-1
dc.subjectзображення MSI
dc.subjectSentinel-2
dc.subjectGoogle Earth Engine
dc.subjectRemote Sensing
dc.subjectSurface Water
dc.subjectSynthetic Aperture Radar
dc.subjectSentinel-1
dc.subjectMSI Images
dc.subjectSentinel-2
dc.subjectGoogle Earth Engine
dc.subject.udc550.837
dc.subject.udc551.24(477)
dc.titleSpatio-temporal analysis of surface water extraction methods reliability using COPERNICUS satellite data
dc.title.alternativeПросторово-часовий аналіз надійності методів виокремлення поверхневої води за даними супутника COPERNICUS
dc.typeArticle

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