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Evaluation and Correlation of Sentinel-2 NDVI and NDMI in Kyiv (2017–2021)

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
For the last 20 years researchers have tried to create new approaches of managing urban ecosystem by using remote sensing methods. The authors estimated the normalized vegetation index (NDVI) and moisture index (NDMI) indices of Kyiv and propose an approach which can be used for estimation vegetation of other cities. The aim of the study was to assess the indicators of NDVI and NDMI in Kyiv for the last 5 years. The authors consider the NDVI index as an important ecostabilizing component of the urban ecosystem, as well as an indicator of the well-being citizens in a modern city. The study used space images of the Sentinel-2 satellite system with resolution of 10×10 m and 10×20 m. The images were decrypted using the Sentinel Hub platform which updates the data-base daily. The paper presents the results of correlation analysis using Statistica-6.0 software and demonstrate the close relationship (r = 0.73, r2 = 0.55) between NDVI and NDMI. Thus, the statistical results of the study confirm a strong correlation between photosynthetic activity of plants and indicators of their water content by satellite imagery which allows using modern satellite technologies to assess the functional state of the urban vegetation. The changes in the length of active vegetative growth period were identified by NDVI and NDMI. The results of the research expand the directions of the methods of monitoring the condition of the urban vegetation cover in the aspect of applied landscape research.
Słowa kluczowe
Rocznik
Strony
212--218
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
  • Wroclaw University of Environmental and Life Sciences, 55 Grunwaldzka Str., 50-357 Wroclaw, Poland
  • Department of Landscape Architecture and Phytodesign, National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony st. 15, Kyiv 03041, Ukraine
  • Wroclaw University of Environmental and Life Sciences, 55 Grunwaldzka Str., 50-357 Wroclaw, Poland
  • Scientific and Research Department, National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony st. 15, Kyiv 03041, Ukraine
Bibliografia
  • 1. Barabash, M.O., Kulbida, M.V., Korzh, T.I. 2004. Zmina hlobalnoho klimatu i problema opusteliuvannia Ukrainy Naukovi zapysky Ternopilskoho DHP, 2, 82–88. (in Ukrainian)
  • 2. Carranza, C., Benninga, H.J., van der Velde, R., van der Ploeg, M. 2019. Monitoring agricultural field trafficability using Sentinel-1. Agricultural water management, 224(105698).
  • 3. Central Geophysical Observatory named after Boris Sreznevsky. Retrieved from: http://cgo-sreznevskyi.kyiv.ua/index.php?fn=k_klimat&f=kyiv
  • 4. Delgado-Moreno, D., Gao, Y. 2022. Forest Degradation Estimation Through Trend Analysis of Annual Time Series NDVI, NDMI and NDFI (2010–2020) Using Landsat Images. In: Tapia-McClung, R., Sánchez-Siordia, O., González-Zuccolotto, K., Carlos-Martínez, H. (eds). Advances in Geospatial Data Science. iGISc 2021. Lecture Notes in Geoinformation and Cartography. Springer, Cham.
  • 5. Gao, B.C. 1996. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266.
  • 6. Gascon, M., Cirach, M., Martínez, D., Dadvand, P., Valentín, A., Plasència, A., Nieuwenhuijsen, M.J. 2016. Normalized difference vegetation index (NDVI) as a marker of surrounding greenness in epidemiological studies: The case of Barcelona city. Urban Forestry and Urban Greening, 19, 88–94.
  • 7. Gu, Y., Hunt, E., Wardlow, B., Basara, J.B., Brown J.F., Verdin J.P. 2008. Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data, Geophys. Res. Lett. 35(L22401).
  • 8. Guha, S., Govil, H., Besoya, M. 2020. An investigation on seasonal variability between LST and NDWI in an urban environment using Landsat satellite data an investigation on seasonal variability between LST and NDWI in an urban environment using Landsat satellite data. Geomat. Nat. Hazards Risk, 11(1), 1319–1345.
  • 9. Isbaex, C., Coelho, A.M. 2021. The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review.
  • 10. James, P., Banay, R.F., Hart, J.E. et al. 2015. A Review of the Health Benefits of Greenness. Curr Epidemiol Rep, 2, 131–142.
  • 11. Koskinen, J., Leinonen, U., Vollrath, A., Ortmann, A., Lindquist E., D’Annunzio R. et al. 2019. Participatory mapping of forest plantations with Open Foris and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 148(63–74).
  • 12. Ku, N.W., Popescu, S.C. 2019. A comparison of multiple methods for mapping local-scale mesquite tree aboveground biomass with remotely sensed data. Biomass and Bioenergy, 122(270–279).
  • 13. Li, F., Han, L., Liujun, Z., Yinyou, H., Song, G. 2016. Urban vegetation mapping based on the HJ-1 NDVI reconstruction. The International Archives of the Photogrammetry, remote Sensing and Spatial Information Sciences, XLI-BB, 867–871.
  • 14. Master Plan of Kyiv. Substantive provisions. 2020. Kyiv, Department of Urban Planning and Architecture.
  • 15. Nasiłowska S., Kubiak K. 2016. Zmienność wskaźników NDVI oraz NDMI na przykładzie analizy uprawy kukurydzy w Etiopii. Teledetekcja Środowiska, 55, 15–26.
  • 16. Nuthammachot, N., Phairuang, W., Wicaksono, P., Sayektiningsih, T. 2018. Estimating aboveground biomass on private forest using sentinel-2 imagery. Journal of Sensors, 2018, 1–11.
  • 17. Robinson, S.L., Lundholm, J.T. 2012. Ecosystem services provided by urban spontaneous vegetation. Urban Ecosyst., 15, 545–557.
  • 18. Rozova, D., Supuka, J., Klein, J., Jasenka, M., Totha, A., Stefl, L. 2020. Effect of vegetation structure on urban climate mitigation. Acta Horticulturae et Regiotecturae, 23(2), 60–65.
  • 19. Shahfahad, Talukdar, S., Rihan, M. et al. 2022. Modelling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities. Environ Dev Sustain, 24, 3762–3790.
  • 20. Statistical Publication. 2022. Number of Present Population of Ukraine, as of January 1. State Service of Ukraine. Retrieved from: http://database.ukrcensus.gov.ua/PXWEB2007/ukr/publ_new1/2022/zb_%D0%A1huselnist.pdf
  • 21. Strashok, O. 2022. Comparative Analysis of Heat Resistance of Ornamental Urban Plants in Kyiv. Journal of Ecological Engineering, 23(3), 145–153.
  • 22. Wang, L., Qu, J.J., Hao, X., Hunt, J.E.R. 2011. Estimating dry matter content from spectral reflectance for green leaves of different species. Int. J. Remote Sen., 32, 7097–7109.
  • 23. Wilson, L., New, S., Daron, J., Golding, N. 2021. Climate Change Impacts for Ukraine. Met Office.
  • 24. Wu, J. 2010. Urban sustainability: An inevitable goal of landscape research. Landsc. Ecol., 25, 1–4.
  • 25. Xue, J., Su, B. 2017. Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 1–17.
  • 26. Yokoya, N., Chan, J.C.W., Segl, K. 2016. Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images. Remote Sensing, 8(3), 172.
  • 27. Yu, F., Price, K.P., Ellis, J., Kastens, D. 2004. Satellite Observations of the Seasonal Vegetation Growth in Central Asia: 1982–1990. Photogrammetric Engineering & Remote, 70(4), 461–469.
  • 28. Zaitunah, A., Sahara S.F. 2021. Mapping and assessment of vegetation cover change and species variation in Medan, North Sumatra. Heliyon, 7(7).
  • 29. Zhang, Y., Wang, P., Wang, T., Gao, Y., Teng, M., Li, J., Li Z. 2020. Using vegetation indices to characterize vegetation cover change in the urban areas of Southern China Sustainability, 12(22), 9403.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c57218ac-b26b-4027-99b2-8bfdd8065a81
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