Green space trends in small towns of Kyiv region according to EOS Land Viewer – a case study
The state of ecological balance of cities is determined by the analysis of the qualitative composition of green space. The lack of green space inventory in small towns in the Kyiv region has prompted the use of express analysis provided by the EOS Land Viewer platform, which allows obtaining an instantaneous distribution of the urban and suburban territories by a number of vegetative indices and in recent years – by scene classification. The purpose of the study is to determine the current state and dynamics of the ratio of vegetation and built-up cover of the territories of small towns in Kyiv region with establishing the rating of towns by eco-balance of territories. The distribution of the territory of small towns by the most common vegetation index NDVI, as well as by SAVI, which is more suitable for areas with vegetation coverage of less than 30%, has been monitored. We found that the share of dense vegetation in the territory of towns increased on average from 2.4 to 49.3% during 1990–2018. The share of the vegetation cover of moderate density decreased from 40.8 to 27.1%, and of sparse one from 37.5 to 14.9%. High variability of these indicators is noted. The share of open area for small towns decreased on average from 15.4 to 3.8%. The vegetation-free areas in 1990, 2005 and 2018 accounted for 3.8, 2.6 and 4.4%, respectively, which may indicate the intensive expansion of built-up areas over the last fifteen years. The development of urban greening systems was completely individual and depended not only on natural conditions but also on the manifestations of anthropogenic activity. The reduction of the ecological balance of the territories of small towns as of 2018 took place in the following sequence – Irpin, Tarashcha, Boiarka, Rzhyshchiv, Kaharlyk, Skvyra, Myronivka, Yahotyn, Uzyn.
ecological balance; express analysis; rating; vegetation indices
Deng J., Wang K., Hong Y., Qi J. (2009): Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landscape and Urban Planning, 92: 187–198. https://doi.org/10.1016/j.landurbplan.2009.05.001
Foody G., Boyd D. (1999): Detection of partial land cover change associated with the migration of inter-class transitional zones. International Journal of Remote Sensing, 20: 2723–2740. https://doi.org/10.1080/014311699211769
Gan M., Deng J., Zheng X., Hong Y., Wang K. (2014): Monitoring urban greenness dynamics using multiple endmember spectral mixture analysis. PLoS ONE, 9: e112202. https://doi.org/10.1371/journal.pone.0112202
He C., Convertino M., Feng Z., Zhang S. (2013): Using LiDAR data to measure the 3D green biomass of beijing urban forest in China. PLoS ONE, 8: e75920.
Herold M., Couclelis H., Clarke K. (2005): The role of spatial metrics in the analysis and modeling of urban land use change. Computers, Environment and Urban Systems, 29: 369–399. https://doi.org/10.1016/j.compenvurbsys.2003.12.001
Kopecká M., Szatmári D., Rosina K. (2017): Analysis of urban green spaces based on sentinel-2A: case studies from Slovakia. Land, 6: 25. https://doi.org/10.3390/land6020025
Liu J., Deng X. (2010): Progress of the research methodologies on the temporal and spatial process of LUCC. Chinese Science Bulletin, 55: 354–362. https://doi.org/10.1007/s11434-009-0733-y
Liu T., Yang X. (2013): Mapping vegetation in an urban area with stratified classification and multiple endmember spectral mixture analysis. Remote Sensing of Environment, 133: 251–264. https://doi.org/10.1016/j.rse.2013.02.020
Myint S. (2006): Urban vegetation mapping using sub-pixel analysis and expert system rules: A critical approach. International Journal of Remote Sensing, 27: 2645–2665. https://doi.org/10.1080/01431160500534630
Ovcharenko A., Zalyubovskaya O. (2018): Indicative landscape monitoring of national natural parks (on the example of the territory of Slobozhansky National Nature Park). Bulletin of Kharkiv National University, series “Geology, Geography, Ecology”, 49: 190–205. (in Ukrainian)
Panarin V., Panarin R. (2009): The use of space images in the municipal management of urban areas for territorial planning purposes. Geomatics, 3, 40–55. (in Ukrainian).
Patino J., Duque J. (2013) A review of regional science applications of satellite remote sensing in urban settings. Computers, Environment and Urban Systems, 37: 1–17. https://doi.org/10.1016/j.compenvurbsys.2012.06.003
Powell R., Roberts D., Dennison P., Hess L. (2007): Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil. Remote Sensing of Environment, 106: 253–267. https://doi.org/10.1016/j.rse.2006.09.005
Rudenko L., Golubtsov O., Chekhniy V., Tymuliak L., Farion Yu. (2019): Landuse changes in the forest-steppe zone of Ukraine during 1991–2018: methodology of research and main trends. Ukrainian Geography Journal, 1: 24–32. https://doi.org/10.15407/ugz2019.01.024
Small C. (2001): Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing, 22: 1305–1334. https://doi.org/10.1080/01431160151144369
Wenliang L. (2016): “Large-Scale Urban Impervous Surfaces Estimation Through Incorporating Temporal and Spatial Information into Spectral Mixture Analysis” Theses and Dissertations: 110.
Yukhnovskyi V., Zibtseva O. (2018): Dynamics of ecological stability of small towns in Kyiv region. Journal of Geology, Geography and Geoecology, 27: 2. https://doi.org/10.15421/111863
Yukhnovskyi V., Zibtseva O. (2019). Estimation of ecological stability of small town Bucha in Kyiv region. Ukrainian Geography Journal, 2: 49–56. https://doi.org/10.15407/ugz2019.02.049