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

Orion SE Cardenas-Ritzert^*Shahriar Shah HeydariDaniel T RodeSteven FilippelliMelinda LaituriMelissa R McHaleJody Vogeler

• Colorado State University, Fort Collins, United States

Urban green and open spaces (UGOS) provide essential social, cultural, environmental, and economic benefits; therefore, monitoring UGOS is critical for guiding management and strengthening urban resilience. Spatial analysis of Earth Observation data offers 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 characterized across broad spatial and temporal scales. While VHR imagery (≤3 m) can enable more refined characterizations of land cover (LC), its use may be constrained by costs, access barriers, and reduced coverage. This study investigates the implications of utilizing imagery sources of varying resolution (≤10 m) and differing classification approaches—pixel-based versus object-based—on LC characterizations and UGOS assessments in two urbanizing cities: Mekelle, Ethiopia and Polokwane, South Africa in 2020. LC classifications were derived from Sentinel-2 (10 m), PlanetScope SuperDove (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 was assessed. WorldView-3 multispectral LC maps consistently achieved the highest overall classification accuracies—92% in Mekelle and 86% in Polokwane—suggesting spatial resolution alone does not guarantee higher accuracy, and spectral richness is important for mapping complex vegetation. Although VHR imagery enhanced detection of small and fragmented landscape features, such as trees, classification performance depended on context, resolution, method, and image characteristics. Coarser imagery like Sentinel-2 proved practical for broader assessments (e.g., SDG 11.7.1) but may underrepresent undeveloped space and miss fine-scale variation. Results revealed clearer spatial patterns and resolution-dependent trends in Mekelle, while findings in Polokwane were more variable, suggesting that landscape structure and urban form influence classification outcomes and UGOS metrics. This study highlights the importance of carefully selecting and interpreting Earth Observation imagery based on resolution, method, timing, and landscape context, especially when data options are limited.