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The 3–30–300 rule Compliance: A geospatial tool for urban planning

TitoloThe 3–30–300 rule Compliance: A geospatial tool for urban planning
Tipo di pubblicazioneArticolo su Rivista peer-reviewed
Anno di Pubblicazione2025
AutoriLopez, Marco Antonio, De Marco Alessandra, Anav A., Sorrentino Beatrice, Paoletti Elena, Manzini Jacopo, Hoshika Yasutomo, and Sicard Pierre
RivistaLandscape and Urban Planning
Volume261
Type of ArticleArticle
ISSN01692046
Parole chiaveClimate 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.

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-105004187206&doi=10.1016%2fj.landurbplan.2025.105396&partnerID=40&md5=398c2d3ff69e9b0f0389de2d64157919
DOI10.1016/j.landurbplan.2025.105396
Citation KeyLopez2025