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首页> 外文期刊>The Science of the Total Environment >Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions
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Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions

机译:使用高空间分辨率的多源数据建模和映射眼睛级绿色能见度曝光

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摘要

The visibility of natural greenness is associated with several health benefits along multiple pathways, including stress recovery and attention restoration mechanisms. However, existing methodologies are inadequate for capturing eye-level greenness visibility exposure at high spatial resolutions for observers located on the ground. As a response, we developed an innovative methodological approach to model and map eye-level greenness visibility exposure for 5 m interval locations within a large study area. We used multi-source spatial data and applied viewshed analysis in conjunction with a distance decay model to compute a novel Viewshed Greenness Visibility Index (VGVI) at more than 86 million observer locations. We compared our eye-level visibility exposure map with traditional top-down greenness exposure metrics such as Normalised Differential Vegetation Index (NDVI) and a Street view based Green View Index (SGVI). Furthermore, we compared greenness visibility at street-only locations with total neighbourhood greenness visibility. We found strong to moderate correlations (r = 0.65-0.42, p < 0.05) between greenness visibility and mean NDVI, with a decreasing trend in correlation strength at increasing buffer distances from observer locations. Our findings suggest that top-down and eye-level measurements of greenness are two distinct metrics for assessing greenness exposure. Additionally, VGVI showed a strong correlation (r = 0.481, p < 0.01) with SGVI. Although the new VGVI has good agreement with existing street view based measures, we found that street-only greenness visibility values are not wholly representative of total neighbourhood visibility due to the under-representation of visible greenness in locations such as backyards and community parks. Our new methodology overcomes such underestimations, is easily transferable, and offers a computationally efficient approach to assessing eye-level greenness exposure.
机译:自然绿色的可见性与多种途径的几种健康益处相关,包括压力恢复和注意力恢复机制。然而,现有方法对于在高空间决议上捕获在地面上的观察者的高空分辨率下的眼睛级绿色能见度暴露不足。作为回应,我们开发了一种创新的方法论方法,可以在大型研究区域内的5米间隔位置进行模型和地图眼睛级绿色能见度暴露。我们使用多源空间数据和应用视图分析与距离衰减模型结合使用,以计算在超过8600万观察者位置的新型视图绿色可见度指数(VGVI)。我们将眼级可见性曝光地图与传统的自上而下的绿色曝光指标进行了比较,如归一化差分植被指数(NDVI)和基于街景的绿色视图指数(SGVI)。此外,我们将绿色的可见度与邻际绿色能见度的街道唯一的位置进行了比较。绿色能见度与平均NDVI之间的中等相关性(r = 0.65-0.42,p <0.05)发现强大的相关性,随着观察者位置的增加缓冲距离的相关强度的趋势降低。我们的研究结果表明,绿色的自上而下和眼睛级别测量是评估绿色暴露的两个不同度量。另外,VGVI显示出具有SGVI的强相关(R = 0.481,P <0.01)。虽然新的VGVI与现有的街景措施良好,但我们发现街道的绿色能见度值并不完全代表总邻域的能见度,因为后院和社区公园等地点的可见绿色的欠呈。我们的新方法克服了这种低估,很容易转移,并提供了评估眼睛级绿色曝光的计算有效方法。

著录项

  • 来源
    《The Science of the Total Environment》 |2021年第1期|143050.1-143050.14|共14页
  • 作者单位

    Department of Geography School of Environment Education and Development (SEED) University of Manchester Arthur Lewis building (1st Floor) Oxford Road Manchester M13 9PL United Kingdom Centre for Diet and Activity Research (CEDAR) MRC Epidemiology Unit University of Cambridge Clifford Allbutt Building CB2 0AH Cambridge United Kingdom;

    Department of Geography School of Environment Education and Development (SEED) University of Manchester Arthur Lewis building (1st Floor) Oxford Road Manchester M13 9PL United Kingdom;

    Department of Geography School of Environment Education and Development (SEED) University of Manchester Arthur Lewis building (1st Floor) Oxford Road Manchester M13 9PL United Kingdom;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Greenspace; Eye level greenness visibility; Environmental exposure; Geographic Information Systems; Urban health; Street view;

    机译:绿地;眼睛级绿色可见性;环境暴露;地理信息系统;城市健康;街景;

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