Title | The 3–30–300 rule Compliance: A geospatial tool for urban planning |
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Publication Type | Articolo su Rivista peer-reviewed |
Year of Publication | 2025 |
Authors | Lopez, Marco Antonio, De Marco Alessandra, Anav A., Sorrentino Beatrice, Paoletti Elena, Manzini Jacopo, Hoshika Yasutomo, and Sicard Pierre |
Journal | Landscape and Urban Planning |
Volume | 261 |
Type of Article | Article |
ISSN | 01692046 |
Keywords | Climate change, detection method, Europe, Remote sensing, satellite imagery, Urban planning, Urban Population |
Abstract | As the global urban population is expected to reach 70% by 2050, sustainable urban planning is essential for creating resilient and livable cities. Urban trees and green spaces are vital for mitigating climate change and enhancing public health. The 3–30–300 rule, introduced in 2021, mandates that every citizen should see at least three mature trees from their home, live in neighborhoods with at least 30% tree canopy cover, and be within 300 m of a high-quality green space. Despite its significance, practical methods for measuring and evaluating this rule have been lacking. To address this gap, we developed a geospatial tool using remote sensing and Geographic Information System techniques to assess compliance with the 3–30–300 rule. The tool employs very-high-resolution satellite imagery for detecting trees and estimating canopy cover (Components 3 and 30) and integrates OpenStreetMap data to assess proximity to green spaces (Component 300). We applied this tool to two study areas: Aix-en-Provence (France) and Florence (Italy). Overall, more buildings in Aix-en-Provence meet all three components than in Florence. Field validation in Aix-en-Provence showed that the algorithm results are highly accurate, supporting the reliability of the proposed approach. The geospatial mapping and satellite-based approaches to assess the 3–30–300 rule compliance is instrumental in helping cities to develop resilient and climate-neutral Urban Greening Plans. © 2025 Elsevier B.V. |
Notes | Cited by: 0 |
URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004187206&doi=10.1016%2fj.landurbplan.2025.105396&partnerID=40&md5=398c2d3ff69e9b0f0389de2d64157919 |
DOI | 10.1016/j.landurbplan.2025.105396 |
Citation Key | Lopez2025 |