Analysis of Urban Greenness Landscape and Its Spatial Association with Urbanization and Climate Changes

Abstract

Urban green space and urban landscape patterns are of great significance to the sustainable development for urban ecosystems. Spatial statistics such as Moran’s I indicator can only reveal the spatial autocorrelation of urban green space or urban landscape itself, while the dynamic changes of urban green space and urban landscape in spatial correlation and spatial heterogeneity must be investigated under spatio-temporal contexts, with possible driving factors such as urban climate and urbanization process taken into account. The purpose of this paper was to study the dynamics of urban greenness patterns using urban landscape indices as well as the spatial association of the indices with the climate changes and urbanization process. Wuhan, a key megacity in Central China was selected as the case study. To this end, we mapped the urban greenness through NDVI of the city from 2000 to 2018 using Landsat imagery. The dynamics of the urban green space indicated by two landscape indices, namely Percent of landscape (PLAND) and landscape shape index (LSI), was analyzed for the study region. Time-series analysis using Mann-Kendall and Sen’s slope were adopted to reveal the temporal changes of the landscape pattern and Pearson’s correlation analysis was performed to explain its association with the climate changes and urbanization process. Results indicated that urban greenness not only significantly decreased but also fragmented.