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
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Notice bibliographique
Résumé
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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle