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Extraction of urban construction development with using Landsat satellite images and geoinformation systems

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In recent times there have been many changes on Earth, which have appeared after anthropogenic impact. Finding solutions to problems in the environment requires studying the problems quickly, make proper conclusions and creating safe and useful measures. Humanity has always had an effect on the environment. There can be many changes on the Earth because of direct and indirect effects of humans on nature. Determining these changes at the right time and organizing meas-urements of them requires the creation of quick analysing methods. This development has improved specialists’ interest for remote sensing (RS) imagery. Moreover, in accordance with analysis of literature sources, agriculture, irrigation and ecology have the most demand for RS imagery. This article is about using geographic information system (GIS) and RS technologies in cadastre and urban construction branches. This article covers a newly created automated method for the calculation of artificial surface area based on satellite images. Accuracy of the analysis is verified according to the field experiments. Accuracy of analysis is 95%. According to the analysis from 1972 to 2019 artificial area enlargement is 13.44%. This method is very simple and easy to use. Using this data, the analysis method can decrease economical costs for field measures. Using this method and these tools in branches also allows for greater efficiency in time and resources.
Wydawca
Rocznik
Tom
Strony
65--69
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
  • Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, 39 Kari Niyazov Str. Tashkent 100000, Uzbekistan
  • Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, 39 Kari Niyazov Str. Tashkent 100000, Uzbekistan
  • Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, 39 Kari Niyazov Str. Tashkent 100000, Uzbekistan
Bibliografia
  • ARIFJANOV A., APAKHODJAEVA T., AKMALOV SH. 2019a. Calculation of losses for transpiration in water reservoirs with using new computer technologies. In: International Conference on Information Science and Communication Technologies (ICISCT). 04–06.11.2019 Tashkent. IEEE p. 1–4. DOI 10.1109/ICISCT47635.2019.9011883.
  • ARIFJANOV A., SAMIEV L., APAKHODJAEVA T., AKMALOV SH. 2019b Distribution of river sediment in channels. In: XII International Scientific Conference on Agricultural Machinery Industry. 10–13.09.2019 Don State Technical University, Russian Federation. IOP Conference Series: Earth and Environmental Science. Vol. 403, 012153. DOI 10.1088/1755-1315/403/1/012153.
  • AYRES-SAMPAIO D., TEODORO A.C., FREITAS T.A., SILLERO N. 2012. The use of remotely sensed environmental data in the study of asthma disease. Remote Sensing for Agriculture, Ecosystems, and Hydrology 14. Vol. 8531, 853124. DOI 10.1117/12. 974539.
  • BALAWEJDER M., NoGa K. 2016. The influence of the highway route on the development of patchwork of plots. Journal of Water and Land Development. No. 30 p. 3–11. DOI 10.1515/jwld-2016-0015.
  • BEKHIRA A., HABI M.,MORSLI B. 2019. Management of hazard of flooding in arid region urban agglomeration using HEC-RAS and GIS software: The case of the Bechar's city. Journal of Water and Land Development. No. 42 (VII–IX) p. 21–32. DOI 10.2478/jwld-2019-0041.
  • BIEDA A., BYDŁOSZ J., WARCHOŁ A., BALAWEJDER M. 2020. Historical underground structures as 3D cadastral objects. Remote Sensing. Vol. 12. Iss. 10, 1547 p. 1–29. DOI 10.3390/rs12101547.
  • BRIGANTE R., RADICIONINI F. 2014. Use of multispectral sensors with high spatial resolution for territorial and environmental analysis. Geographia Technica. Vol. 9. No. 2 p. 9–20.
  • CAPOLUPO A., MONTERISI C., TARANTINO E. 2020. Landsat Images Classification Algorithm (LICA) to automatically extract land cover information in Google Earth engine environment. Remote Sensing. Vol. 12. Iss. 7, 1201. DOI 10.3390/ rs12071201.
  • CHEN Z., NING X., ZHANG J. 2012. Urban land cover classification based on WorldView-2 image data. In: International Symposium on Geomatics for Integrated Water Resource Management. IEEE p. 1–5.
  • DINKA M.O., CHAKA D.D. 2019. Analysis of land use/land cover change in Adei watershed, Central Highlands of Ethiopia. Journal of Water Land Development. No. 41 p. 146–153. DOI 10.2478/jwld-2019-0025.
  • GINIYATULLINA O.L., POTAPOV V.P., SCHACTLIVTCEV E.L. 2014 Integral methods of environmental assessment at mining regions based on remote sensing data. International Journal of Engineering and Innovative Technology (IJEIT). Vol. 4. Iss. 4 p. 220–224.
  • Impactmin 2010. WP4-Satelite remote sensing deliverable D4. 1 Report on the limitations and potentials of satelite EO data [online]. Contract No. 244166. Impact Monitoring of Mineral Resources Exploitation pp. 143. [Access 08.05.2020]. Available at: https://impactmin.geonardo.com/downloads/impactmin_d41.pdf
  • MACHAULT V., VIGNOLLES C., BORCHI F., VOUNATSOU P., BRIOLANT S., LACAUX J.P., ROGIER C. 2011. The use of remotely sensed environmental data in the study of malaria. Geospatial Health. Vol. 5. No. 2 p. 151–168. DOI 10.1117/ 12.974539.
  • NAVULUR K. 2006. Multispectral image analysis using the object-oriented paradigm. UK CRC Press. ISBN 987-1-4200-4306-8 pp. 204.
  • NAVULUR K., PACIFICI F., BAUGH B. 2013. Trends in optical commercial remote sensing industry [Industrial profiles]. IEEE Geoscience and Remote Sensing Magazine. Vol. 1. Iss. 4 p. 57–64. DOI 10.1109/MGRS.2013.2290098.
  • RAMOELO A., CHO M. 2014. Dry season biomass estimation as an indicator of rangeland quantity using multi-scale remote sensing data. In: 10th International Conference on African Association of Remote Sensing of Environment (AARSE). University of Johannesburg p. 27–31.
  • RONCZYK M., WOJTASZEK-LEVENTE H. 2012. Object-based classification of urban land cover extraction using high spatial resolution imagery. In: The impact of urbanization, industrial, agricultural and forest technologies on the natural environment. Eds. M. Neményi, B. Heil. Sopron. Nyugat-magyarországi Egyetem p. 171–181.
  • TOGAEV I., NURKHODJAEV A., AKMALOV SH. 2020. Structurally decryptable complexes-a new taxonomic unit in cosmo-geological research. In: E3S Web of Conferences. EDP Sciences. Vol. 164 p. 07027. DOI 10.1051/e3sconf/2020164 07027
  • TUKHLIEV N., KREMENSOVA А. 2007. O’zbekiston milliy ensiklopediyasi [National encyclopedy of Uzbekistan]. State Scientific Publishing. Tashkent. Uzbekistan p. 560.
  • Uzkommunkhizmat 2010. Water supply of Syr Darya province. World Bank Project [online]. Uzbekistan, Tashkent Agency «Uzkommunservice» pp. 152. [Access 12.02.2020]. Availa-ble at: http://documents1.worldbank.org/curated/pt/198941468127470671/pdf/E23850P11176001C10EIA71Report1Final.pdf
  • XU D., GUO X., LI Z., YANG X., YIN X. 2014. Measuring the dead component of mixed grassland with Landsat Imagery. Remote Sensing of Environment. Vol. 142 p. 33–43. DOI 10.1016.j.rse.2013.11.017.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-77654574-516c-4a66-a04b-1a5c37c44c34
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