| 摘要: |
| 极端高温事件的频发使得优化城市热环境迫在眉睫。滨水住区作为常见住区类型，其室外热环境影响因素和改善措施值得关注。既往研究缺乏对地表要素影响室外热环境的空间异质性考量，难以指导滨水住区内部具体规划实践。以银川市4个典型滨水住区为例，利用无人机采集地表空间数据，并分析其热环境特征；进而采用线性回归和地理加权回归分析地表要素与室外热环境的相关性和空间分异特征，研究表明：地表要素对室外热环境的影响取决于其自身温度与住区建筑环境条件；产生空间异质性的主要原因为水体影响、植被遮荫以及车行道路邻接裸露地表。最后，从住区建筑的退台式形态控制、植被配置的提升绿化质量与构建隔离带、以及开放空间的增加遮荫和滨水区绿化改造的3方面提出了滨水住区的室外热环境优化策略，以期对气候适应性的住区规划设计提供参考。 |
| 关键词: 滨水住区 低空红外遥感 室外热环境 地理加权回归 空间分异 |
| DOI： |
| 分类号:TU119 |
| 基金项目:国家自然科学基金项目面上项目：寒冷地区城区-街区地表形态对空间碳绩效的影响机理与优化方法研究（52378066）；宁夏自然科学基金项目："双碳”目标导向下的宁夏城市空间形态低碳优化方法研究（2024AAC03071）；宁夏自然科学基金项目：基于多模型耦合的石嘴山市绿色基础设施构建方法研究（2020AAC03046） |
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| The influence of ground surface factors on the outdoor thermal environment of waterfront residential areas and its spatial differentiation characteristics.— An empirical study based on four typical waterfront residential areas in Yinchuan |
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Chen Tian,Zuo Minghao,Li Muhan,Gu Haozhuo,Liu Jia
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| Abstract: |
| The intensification of global climate change has resulted in the frequent occurrence of extreme heat disasters, which seriously affects the production and life of urban residents, and the optimisation of the urban thermal environment is urgent. Waterfront settlements, as a common type of settlement, deserve attention for their outdoor thermal environment influencing factors and improvement measures. Previous studies lacked the spatial heterogeneity of surface elements affecting the outdoor thermal environment, which makes it difficult to guide the specific planning practices within waterfront settlements. In this study, we take four typical waterfront settlements in Yinchuan City as examples, and collect the impacts of surface space and thermal environment data using both visible light and infrared remote sensing thermal imaging tools from unmanned aerial vehicles (UAVs), to study the global correlation between surface elements and outdoor thermal environment and their spatial heterogeneity in urban waterfront settlements. In order to explore the spatial differentiation within the settlements based on the differences between the settlements, and to better compare the results of different methods and draw more accurate conclusions, the study adopts the Ordinary Least Squares (OLS) linear regression model, which is widely used in the field of thermal environment research, and the Geographically Weighted Regression (GWR) model, which is widely used in the field of thermal environment research. Weighted Regression (GWR) model, the study firstly determines the optimal statistical grid scale by the significance of the coefficient of determination of the OLS model and the correlation between each element and the land surface temperature (LST). Afterwards, the OLS and GWR regression models were constructed with the area share of each element as the independent variable and the average LST within the grid as the dependent variable, respectively, to explore the global impacts of different surface elements on the thermal environment and analyse their spatial differentiation characteristics in depth, so as to put forward the theoretical support for the design strategy of waterfront settlements as well as the updating scheme, with a view to providing scientific guidance for the design of climate-adapted settlements. The study has the following conclusions: (1) the temperature of trees, shrubs and grassland elements are low, while the elements of the carriageway are concentrated in high temperature distribution (2) the effect of temperature rise and fall of surface elements depends on their own temperature and the conditions of the built environment of the settlement. In low-density high-rise settlements, trees, shrubs and grass elements all have a cooling effect; in terms of warming effect, the element with the strongest effect varies in different settlements, but grass elements all have the weakest warming effect. (3) The main reasons for the spatial heterogeneity of the influence of surface elements on the outdoor thermal environment of waterfront settlements are the influence of water bodies, vegetation shading, and the bare surface adjacent to the carriageway. The closer to the water body, the worse the cooling effect of trees and shrubs and the warming effect of buildings, the shading effect of trees and shrubs can alleviate the warming effect of grass, pavements and other hard paving elements while cooling, and the direct connection between carriageway and bare surface will enhance the warming effect. Finally, this study proposes strategies for optimising the outdoor thermal environment of waterfront settlements from three aspects, with a view to informing the planning and design of climate-resilient settlements. The recommendations are as follows: (1) for the architecture of the settlement, to construct a spatial environment of high-rise buildings combined with high green coverage, and to form an overall architectural layout of the settlement with a water-facing setback through the combination of high-rise and low-rise buildings; (2) for the vegetation configuration, to construct a rich combination of trees, shrubs, and grasses while avoiding the appearance of bare land, and to form a separation zone between the settlement and the surrounding roads through the rich green configuration; and (3) to provide a green environment of the settlement with a high level of green coverage. (3) In terms of open space, for the large area of hard pavement in the settlement, the first consideration is to use buildings, greenery, structures and so on to increase the shade to alleviate the high temperature situation, while for the large area of hard pavement along the waterfront, the cooling effect provided by the shade is limited, and priority can be given to the pavement greening modification method. |
| Key words: Waterfront residential area, low-altitude infrared remote sensing, outdoor thermal |
