The Development of comprehensive evaluation indicators of street greenery and the identification of priority greening Areas: Providing more accurate greening planning
• Construct a street greenery indicator by integrating SVI and remote sensing data. • The SGCEI broad the view of street greenery compared to the GVI. • Quantify accessibility for pedestrians and vehicles on various path preferences. • Identify priority greening areas by overlaying SGCEI with two-scale accessibility. Appropriate greening planning is crucial for improving urban quality of life and well-being. However, current greening assessment methods often focus on a single perspective, limiting the ability to integrate overhead top-down and street-level views for refined street-view planning. This study introduces the Street Greenery Comprehensive Evaluation Indicator (SGCEI) by combing street view imagery (SVI) with remote sensing data. Coupled with street accessibility at two scales, SGCEI helps identify priority greenery areas for urban planning. Taking Beijing’s Sixth Ring Road as a study area, the results show that: (1) SGCEI improves the Green View Index (GVI) by reducing occlusion effects, providing a more accurate assessment of street greenery. (2) Street accessibility, at both the pedestrian and vehicle scales, is highest within the inner ring roads and lower in the outer ring roads, with significant overlap in high-value areas. (3) SGCEI identifies priority greenery areas for 25% of the samples, offering better spatial aggregation and continuity than GVI. High-priority areas are primarily located within the Fourth Ring Road, while lower priority areas outside this boundary form distinct clusters. This method more accurately reflects the true quality of street greenery and offers valuable data and theoretical support for refining urban greening plans.
Authors
- Tiange Zhang (ORCID: https://orcid.org/0009-0001-6195-9448)
- Hao Wang (ORCID: https://orcid.org/0000-0003-1421-5953)
- Xiuce Yang
- Xiaogang Ning
- Yongqiang Wei
- Guangzhe Li
Institutions
- Chinese Academy of Surveying and Mapping (CN)
- Xi'an Railway Survey and Design Institute (CN)
- Shandong University of Science and Technology (CN)
Publication Details
- Journal
- Ecological Indicators
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1016/j.ecolind.2024.112630
- Citations
- 5
- Primary Topic
- Urban Green Space and Health
- Type
- article
- Field-Weighted Citation Impact
- 1.57