棕地
再开发
环境规划
资源(消歧)
城市化
地理
环境资源管理
城市林业
土地利用
城市规划
环境科学
土木工程
计算机科学
工程类
计算机网络
考古
作者
Paul D. Preston,Rachel M. Dunk,Graham Smith,Gina Cavan
标识
DOI:10.1016/j.landurbplan.2022.104590
摘要
While the role of urban green space in mitigating environmental hazards and enhancing urban resilience is widely recognised, the current or potential contribution of brownfield land to urban green infrastructure and ecosystem services has been largely overlooked by planning legislation. The perception of brownfield as low value spaces has instead driven a focus on brownfield-first redevelopment, and thus, this dynamic resource is quickly being lost. This research, based on GIS and remote sensing data, develops a novel hierarchical brownfield classification methodology to understand the nature and distribution of brownfield, using k-means clustering of several physical attributes, which can be used for a range of objectives and is widely applicable to post-industrial cities. Application of the methodology to the case study, Greater Manchester, UK, produced a typology of twenty-six brownfield types with distinct characteristics and differing spatial patterns across the city. Land cover analysis reveals that over half (51%) of brownfield land is vegetated (comprising 27% trees and shrubs, 24% grass and herbaceous vegetation), highlighting the significant ‘hidden’ green space present on brownfield. Brownfield sites traditionally perceived as difficult to develop (e.g. those with uneven topography, irregular shapes, or a water body), are particularly highly vegetated. Predominantly pervious types are widely distributed across the conurbation, including in built-up areas, which are a principal target for redevelopment, and thus highly vegetated brownfields are likely being lost undetected. Brownfield land is evidently a valuable dynamic resource in post-industrial cities and redevelopment should be planned at the city-scale to ensure careful strategic selection of sites for redevelopment, greening, or interim use based upon their characteristics and location.
科研通智能强力驱动
Strongly Powered by AbleSci AI