| 摘要: |
| 以福州市主城区为例，基于腾讯街景数据与卫星遥感影像，结合传统遥感解译与机器学习算法，测度并比较其街道绿视率与植被覆盖率空间差异，并进一步分析街道绿视率的影响因素，最终提出集约化提升街道绿化品质的规划设计策略。研究发现：1）福州市主城区的绿视率形成“二环内较高，二环外递减”的“核心–边缘”结构，植被覆盖率普遍存在“街道低，山体与城郊高”的现象。2）福州市主城区的绿视率与植被覆盖率在空间分布上存在明显差异，绿视率高而植被覆盖率低的区域较多，主要由周围自然环境或一些绿色的构筑物引起。绿视率低而植被覆盖率高的区域较少，主要由遮挡与高差引起。3）影响绿视率的积极因素包括较好的景观渗透性、较大的附属绿地进深、街旁绿地的布置、高大的行道树、乔灌搭配、街边休憩空间营造等，消极因素包括实墙遮挡、道路高差、基础设施落后以及临街入口开敞等。通过植被覆盖率与绿视率对比研究揭示人本尺度下绿化品质测度的趋势转向，而街道绿化品质的规划设计策略也能为相关研究提供一定参考。 |
| 关键词: 风景园林 绿视率 植被覆盖率 绿化品质 机器学习 空间特征 |
| DOI：10.14085/j.fjyl.2021.02.0062.07 |
| 分类号:TU985.12 |
| 基金项目:国家自然科学基金（编号 52008112） |
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| Urban Street Greenery Quality Measurement, Planning and Design Promotion Strategies Based on Multi-Source Data: A Case Study of Fuzhou’s Main Urban Area |
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LI Miaoyi, YANG Zhonghao, XUE Feng
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Fuzhou University
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| Abstract: |
| Taking the main urban area of Fuzhou City as an example, this research, based on Tencent street view data and satellite remote sensing images, integrates the traditional remote sensing interpretation and machine learning algorithms to measure and compare the spatial difference between its street green view index and vegetation coverage rate. It further analyzes the street green view index influencing factors, and finally proposes a planning and design strategy to intensively improve the quality of street greenery. The research has found that: 1) The green view index in the main urban area of Fuzhou forms a “core-edge” structure that is “higher inside the second ring road and decreases outside the second ring road”, and the vegetation coverage rate is generally “low in streets, and high in mountains and suburbs”. 2) There is a significant difference in the spatial distribution of green view index and vegetation coverage rate in the main urban area of Fuzhou. There are many areas with a high green view index but a low vegetation coverage rate, which is mainly caused by the surrounding natural environment or some green structures. There are few areas with a low green view index but a high vegetation coverage rate, which is mainly caused by occlusion and difference in height. 3) Positive factors influencing green view index include better landscape permeability, greater depth of auxiliary green space, layout of green space beside the streets, tall sidewalk trees, arbor and shrub collocation, and street-side recreation space creation. Negative factors include solid wall blocking, road elevation difference, backward infrastructure, and open entrance to the street. Through the comparative study on the vegetation coverage rate and green view index, this paper discloses the trend shift of greenery quality measurement on a human scale, and the planning and design strategy of the street greenery quality can also provide some reference for related researches. |
| Key words: landscape architecture green view index (GVI) vegetation coverage rate greenery quality machine learning spatial characteristics |
| 引用本文: | 李苗裔,杨忠豪,薛峰.基于多源数据的城市街道绿化品质测度与规划设计提升策略——以福州主城区为例[J].风景园林，2021，28（2）：62-68. |
| LI Miaoyi,YANG Zhonghao,XUE Feng.Urban Street Greenery Quality Measurement, Planning and Design Promotion Strategies Based on Multi-Source Data: A Case Study of Fuzhou’s Main Urban Area[J].Landscape Architecture Journal, 2021, 28(2):62-68. |
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