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Automated decryption of bodies of water on the basis of LANDSAT-8 satellite images with reference to controlled classification

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Warianty tytułu
PL
Zautomatyzowane odszyfrowywanie zdjęć zbiorników wody na podstawie obrazów satelitarnych LANDSAT-8
Języki publikacji
EN
Abstrakty
EN
An operative method of automated decryption of Landsat-8 satellite images allowing for detection of water bodies is created. Application of the developed method allows for the detection of water bodies more than 30 m in size and specifies the obtained masks of water bodies significantly.
PL
Przedstawiono metodę automatycznego odszyfrowywania zdjęć satelitarnych Landsat-8 umożliwiającą wykrywanie części wód. Zastosowanie opracowanej metody pozwala na wykrycie zbiorników wodnych większych niż 30 m.
Rocznik
Strony
115--118
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
  • Ternopil Ivan Pului National Technical University, Department of Instruments and Control and Measurement Systems, 56 Ruska Str., 46001, Ternopil, Ukraine
Bibliografia
  • [1] Howard A., Nibbelink N., Bernardes S., Fragaszy D., Madden M., Remote sensing and habitat mapping for bearded capuchin monkeys (Sapajus libidinosus): landscapes for the use of stone tools, Journal of Applied Remote Sensing, (2015), 9, 1.
  • [2] Stewart J., Pomeroy P., Duck C., Twiss S., Finescale ecological niche modeling provides evidence that lactating gray seals (Halichoerus grypus) prefer access to fresh water in order to drink, Marine Mammal Science, 30 (2014), 4, 1456–1472.
  • [3] Pohrebennyk V., Korchenko O., Kreta D., Klymenko V., Anpilova Y., GIS and remote sensing as important tools for assessment of environmental pollution, 19th International multidisciplinary scientific geoconference SGEM, (2019), 19, (2.1), 297–304.
  • [4] Kashkin V., Sukhinin A., Digital processing of aerospace images. Version 1.0: electron. studying allowance. (2008), Krasnoyarsk: IPK SFU.
  • [5] Parkinson C., Ward A., King M., Earth Science Reference Handbook. A Guide to NASA’s Earth Science Program and Earth Observing Satellite Missions, National Aeronautics and Space Administration Washington, (2010).
  • [6] Karpinski M., Pohrebennyk V., Bernatska N., Ganczarczyk J., Shevchenko O., Simulation of artificial neural networks for assessing the ecological state of surface water, 18th International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 18 (2.1), (2018), 693-700.
  • [7] Schowengerdt R., Remote sensing: models and methods for image processing. Burlington: Academic Press, (2007), 560.
  • [8] Tang Q., Huilin G., Lu H., Lettenmaier D., Remote sensing: Hydrology, Progress in Physical Geography, 33 (2009), 4, 490-509.
  • [9] Pimentel R., Herrero J., Polo M., Snow evolution in a semi-arid mountainous area combining snow modelling and Landsat spectral mixture analysis, Proceedings of the International Association of Hydrological Sciences, 368 (2015), 33-39.
  • [10] Pohrebennyk V., Ganczarczyk J., Korchenko O., Sheviakina N., Zagorodnia S., Use of modern information technologies for monitoring and management of nature reserve areas, 19th International multidisciplinary scientific geoconference SGEM, (2019), 19, (2.1), 697–704.
  • [11] Kataev M., Bekerov A., Methodology of the water objects detection from multi-spectrum satellite measurements, Proceedings of Tomsk State University of Control Systems and Radioelectronics, 20 (2017), 4, 105-108.
  • [12] Vishnevsky V., Shevchuk S., Estimation of the state of water objects of Kyiv according to the data of remote sensing of the Earth, Ukrainian Journal of Remote Sensing of Earth, 11 (2016), 4, 9-14.
  • [13] Evdokimov S., Mikhalap S., Determination of the physical meaning of a combination of LANDSAT snapshots for monitoring the state of terrestrial and aquatic ecosystems, Herald of Pskov State University. Series Natural and Physical and Mathematical Sciences, 4 (2015), 7, 21-31 [in Russian].
  • [14] Skakun R., Wulder M., Franklin S., Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage, Remote Sensing of Environment, 86 (2003), 433-443.
  • [15] Xu H., Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery, International Journal of Remote Sensing, 27 (2006), 14, 3025–3033.
  • [16] Trifonova T., Mishchenko N., Krasnoshkov A., Geoinformation Systems and Remote Sensing in Environmental Studies: Educational manual for high schools. Moscow: Akademicheskiy proekt, (2005), 352 p.
  • [17] Landsat 8 Data Users Handbook. Retrieved 01.06.2020, https://www.usgs.gov/land-resources/nli/landsat/landsat-8-datausers-handbook.
  • [18] Lyalka V., Popov M., Multispectral methods of remote sensing of the Earth in the problems of the nature’s management. Kiev: Naukova dumka, (2006), 375 p.
  • [19] Gorshenin A., Kondratenko S., Osadchuk R., Peregud A., Space methods of remote sensing of the Earth. Zhytomyr: ZVI NAU, (2011), 280 p.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c301f0bf-6ff1-4400-86f8-04addeed68d6
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