Estimating Long-Term Spatio-Temporal Variations of Urban Vegetation Using Landsat Time Series: A Case Study of Kyiv City, Ukraine

Abstract

Vegetation contributes significantly to the sustainability of urban environment and the well-being of city dwellers. For this reason, sustainable evidence-based urban policies require spatially explicit information on the change of vegetation cover in cities. This study estimates vegetation changes during active growing season from 1984 to 2020 for the city of Kyiv, Ukraine, using long-term Landsat data archive. During the study period of 37 years, for 38.9% of the study area significant long-term trends in vegetation cover are registered. For the entire study area NDVIs demonstrate a mean annual increase of 0.004$±$0.004 with prevailing positive trends. The negative changes of vegetation are localized in the central and western parts of the city relate to the processes of urban densification and urban sprawl. Within large peripheral forested areas, changes are positive with possible contributions from ecological succession and from an increase in the duration of the active growing season. Thus, remote sensing time series-based approaches for assessing changes in various environmental parameters could be of great use for developing spatially accurate and data driven urban planning policies and climate change adaptation measures.

Publication
Geoinformatics
Daria Svidzinska
Daria Svidzinska
Postdoctoral fellow / Earth System Data Science

My research interests focus on the spatio-temporal patterns of environmental change. To unravel these patterns I analyze the time series of remote sensing environmental variables. This information is then applied to inform and support data-driven strategies for sustainable and resilient development. My current research project seeks to reveal the impacts of war actions on protected ecosystems in Ukraine through remote sensing data analysis to guide future monitoring and restoration practices.