Assessing urban vegetation inequalities: Methodological insights and evidence
Alicia González‐Marín, Marco Garrido‐Cumbrera
Abstract
Vegetation indices have become increasingly popular in analyzing urban inequalities in access to and use of green spaces. However, the methodologies used have been heterogeneous, leading to inconclusive or contradictory results. This study aims to conduct a scoping literature review of research that evaluates methodologies used to estimate the inequal distribution of vegetation in cities, providing evidence to establish standard guidelines for the selection of data sources, methodologies and indicators. The review includes 66 articles published between 2004 and 2023 from various global regions. We identified 10 vegetation indices, with the Normalized Difference Vegetation Index (NDVI) being the most frequently used, typically derived from Landsat satellite data. The review highlights the importance of image acquisition times and temporal resolution in capturing dynamic urban environments. The spatial scale most adopted is the census block group, suitable for assessing urban inequalities. We observe substantial heterogeneity in the methodologies and statistical tools employed, with spatial autocorrelation analysis being the most common, followed by Pearson's and Spearman's correlation coefficients. ArcGIS was the most widely used GIS platform, closely followed by the cloud-based geospatial analysis platform Google Earth Engine, while R-Studio and Statistical Package for the Social Sciences (SPSS) are popular for statistical analysis. This study underscores the need for standardized methodologies to enhance the comparability and reliability of research on urban vegetation inequalities. • Scoping review of methodologies employed to assess inequality in urban vegetation. • A total of ten vegetation indices were identified, with the most commonly applied being NDVI ( n = 32). • The most frequently used satellites were Landsat ( n = 23), followed by MODIS ( n = 12). • Preferred softwares are ArcGIS ( n = 16), Google Earth Engine ( n = 9), R-Studio ( n = 22), and Stata ( n = 4). • Highlight the importance of standardized methodologies for future research on urban greening.