Mapping the maximum extents of urban green spaces in 1039 cities using dense satellite images

Spatial data of urban green spaces (UGS) are critical for cities worldwide to evaluate their progress towards achieving the urban sustainable development goals on UGS. However, UGS maps at the global scale with acceptable accuracies are not readily available. In this study, we mapped UGS of all 1039 mid- and large-sized cities across the globe in 2015 with dense remote sensing data (i.e. 51 494 Landsat images) and Google Earth Engine (GEE) platform. Also, we quantified the spatial distribution and accessibility of UGS within the cities. By combining the greenest pixel compositing method and the percentile-based image compositing method, we were able to obtain the maximum extent of UGS in cities while better differentiating UGS from other vegetation such as croplands. The mean overall classification accuracy reached 89.26% (SD = 3.26%), which was higher than existing global land cover products. Our maps showed that the mean UGS coverage in 1039 cities was 38.46% (SD = 20.27%), while the mean UGS accessibility was 82.67% (SD = 22.89%). However, there was a distinctive spatial equity issue as cities in high-income countries had higher coverage and better accessibility than cities in low-income countries. Besides developing a protocol for large-scale UGS mapping, our study results provide key baseline information to support international endeavors to fulfill the relevant urban sustainable development goals.

Automated summary

This study mapped urban green spaces in 1039 mid- and large-sized cities globally in 2015 using remote sensing data and Google Earth Engine platform, with a high overall classification accuracy. The findings revealed a spatial equity issue where cities in high-income countries had higher green space coverage and accessibility compared to cities in low-income countries. The study also provides baseline information to support international efforts in achieving urban sustainable development goals.