AUTHOR=Cardenas-Ritzert Orion S. E. , Shah Heydari Shahriar , Rode Daniel T. , Filippelli Steven K. , Laituri Melinda , McHale Melissa R. , Vogeler Jody C. 

TITLE=The role of data selection in mapping urban green and open spaces: a comparison across high and very-high resolution satellite imagery sources in two African cities

JOURNAL=Frontiers in Remote Sensing

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2025.1625373

DOI=10.3389/frsen.2025.1625373

ISSN=2673-6187

ABSTRACT=Urban green and open spaces (UGOS) provide essential social, cultural, environmental, and economic benefits to a city; therefore, monitoring UGOS is critical for guiding management and strengthening urban resilience. Spatial analysis of Earth Observation data provides a practical means of evaluating UGOS, and with the availability of high and very-high spatial resolution (VHR) satellite imagery (≤10 m), UGOS can be accurately characterized across broad spatial and temporal scales. While VHR satellite imagery (≤3 m) can enable more refined characterizations of land cover (LC), its use may be constrained by high monetary costs, accessibility barriers, and reduced spatial and temporal coverage. This study investigates the implications of utilizing imagery sources of varying spatial resolution (≤10 m) and differing classification approaches—pixel-based versus object-based—on LC characterizations and subsequent UGOS spatial assessments in two urbanizing cities: Mekelle, Ethiopia and Polokwane, South Africa in 2020. LC classifications were derived from Sentinel-2 imagery (10 m), PlanetScope SuperDove imagery (3 m), and Maxar WorldView-3 multispectral (2 m) and pansharpened (0.5 m) imagery. Mapping accuracy and UGOS characteristics were evaluated for each city, including the composition of undeveloped versus developed land, tall vegetation cover, and LC within selected public spaces. Additionally, the share of streets and open space under Sustainable Development Goal Indicator 11.7.1 were assessed. WorldView-3 multispectral (2 m) LC maps consistently achieved the highest overall classification accuracies, at 92% in Mekelle and 86% in Polokwane, suggesting that spatial resolution alone does not guarantee higher mapping accuracy, and that spectral richness is an important characteristic for mapping complex vegetation. Although VHR imagery enhanced the detection of small and fragmented landscape features, such as trees, classification performance depended heavily on context, resolution, method, and image characteristics. Coarser imagery like Sentinel-2 proved to be practical for broader assessments (e.g., SDG 11.7.1) but based on our results, still may underrepresent total undeveloped space and fails to capture fine-scale spatial variation. The results revealed clearer spatial patterns and resolution-dependent trends in Mekelle, while findings in Polokwane were more variable, suggesting that local landscape structure and urban form may influence classification outcomes and UGOS metrics. Overall, this study highlights the importance of carefully selecting and interpreting Earth Observation imagery based on sensor characteristics, spatial and spectral resolution, classification method, acquisition timing, and local landscape context, especially when data options are limited.