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Combining Sentinel-1 and Sentinel-2 data for the identification of urban greenery

Remote Sensing & GIS for Environmental Monitoring

Authors

First and Last Name Academic degree E-mail Affiliation
Vadym Belenok Ph.D. belenok.vadim [at] nau.edu.ua National Aviation University
Kyiv, Ukraine
Liliia Hebryn-Baidy Ph.D. liliya.gebrinbaydi [at] gmail.com National Aviation University
Kyiv, Ukraine
Oles Zheleznyak No oles.zhelezniak [at] nau.edu.ua National Aviation University
Kyiv, Ukraine
Sofiia Alpert Ph.D. sonyasonet87 [at] gmail.com Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Science of the National Academy of Sciences of Ukraine
Kyiv, Ukraine

I and my co-authors (if any) authorize the use of the Paper in accordance with the Creative Commons CC BY license

First published on this website: 21.08.2023 - 13:09
Abstract 

In this work, we examined the accuracy of identifying various types of land use and land cover using the remote sensing data for Kyiv to determine the territory occupied by urban greenery. Sentinel-1 SAR data, Sentinel-2 MSI data, and a combination of both were used. Classes were assigned using three different Machine Learning Algorithms. The Enhanced Vegetation Index was used to divide urban greenery into zones depending on the amount of green biomass.

Keywords 
land use and land cover
Sentinel-1
Sentinel-2
urban greenery
References 
  1. Adorno, B.V., Körting, T.S., Amaral, S. (2023). Contribution of time-series data cubes to classify urban vegetation types by remote sensing. Urban Forestry and Urban Greening, 79, 127817. https://doi.org/10.1016/j.ufug.2022.127817
  2. Belenok, V., Hebryn-Baidy, L., Bielousova, N., Gladilin, V., Kryachok, S., Tereshchenko, A., Alpert, S., Bodnar, S. (2023), Machine learning based combinatorial analysis for land use and land cover assessment in Kyiv City (Ukraine). Journal of Applied Remote Sensing, 17 (1), 014506. https://doi.org/10.1117/1.JRS.17.014506
  3. Belenok V., Hebryn-Baidy L., Bіelousova N., Zavarika H., Sakal O., Kovalenko A. (2022). Geoinformation Mapping of Anthropogenically Transformed Landscapes of Bila Tserkva (Ukraine). Acta Scientiarum Polonorum. Formatio Circumiectus, 21 (1), 69–84. https://doi.org/10.15576/ASP.FC/2022.21.1.69
  4. Cetin, M., Adiguzel, F., Cetin, I.Z. (2023). Determination of the Effect of Urban Forests and Other Green Areas on Surface Temperature in Antalya. Concepts and Applications of Remote Sensing in Forestry, 319–336. https://doi.org/10.1007/978-981-19-4200-6_16
  5. Cetin, M., Adiguzel, F., Gungor, S., Kaya, E., Sancar, M.C. (2019) Evaluation of thermal climatic region areas in terms of building density in urban management and planning for Burdur, TurkeyAir Quality, Atmosphere and Health, 12 (9), 1103–1112. https://doi.org/10.1007/s11869-019-00727-3
  6. Chang, Q., Liu, X., Wu, J., He, P. (2015). MSPA-based urban green infrastructure planning and management approach for urban sustainability: Case study of longgang in China. Journal of Urban Planning and Development, 141(3), A5014006. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000247
  7. Chen, C., Bagan, H., Xie, X., La, Y., Yamagata, Y. (2021). Combination of sentinel-2 and palsar-2 for local climate zone classification: A case study of nanchang, China. Remote Sensing, 13 (10), 1902. https://doi.org/10.3390/rs13101902
  8. Derkzen, M.L., van Teeffelen, A.J.A., Verburg, P.H. (2015). REVIEW: Quantifying urban ecosystem services based on high-resolution data of urban green space: An assessment for Rotterdam, the Netherlands. Journal of Applied Ecology, 52 (4), 1020–1032. https://doi.org/10.1111/1365-2664.12469
  9. Pyszny, K., Sojka, M., Wró Yński, R. (2020). LiDAR based urban vegetation mapping as a basis of green infrastructure planning. E3S Web of Conferences, 171, 02008. https://doi.org/10.1051/e3sconf/202017102008
  10. Rizwan, A.M., Dennis, L.Y.C., Liu, C. (2008) A review on the generation, determination and mitigation of Urban Heat Island. Journal of Environmental Sciences, 20 (1), 120–128. https://doi.org/10.1016/S1001-0742(08)60019-4
  11. Schwarz, N., Schlink, U., Franck, U., Großmann, K. (2012). Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators - An application for the city of Leipzig (Germany). Ecological Indicators, 18, 693–704. https://doi.org/10.1016/j.ecolind.2012.01.001
  12. Shojanoori, R., Shafri, H.Z.M. (2016). Review on the Use of Remote Sensing for Urban Forest Monitoring. Arboriculture & Urban Forestry, 42 (6), 400–417. https://doi.org/10.48044/jauf.2016.034