Chengjie Yang’s research while affiliated with Wuhan University and other places

Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000-2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

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.