摘要
就城市尺度级别下的热环境进行评价,为了能够落实影响绿地规划的具体空间区域,并提供可以指导绿地布局的方法,以西安为例,量化植被、建设用地空间分布与地表温度的关系,确定绿地降温的具体区域;通过卷积计算,以温度变化为0作为界限,细化冷区和热区,提取城市微更新下绿地布局待调整的位置;基于机器学习的回归算法,由绿地、建设用地和水体分布预测作为热环境指示因子的地表温度,从而为规划的合理性提供参考。通过对研究结果的评价,以及与已有方法的比较,利用地表温度和地表覆盖变化,量化城市空间分布结构,为绿地规划提供可以落实的具体空间区域。
As for the evaluation of the thermal environment at the urban scale,to implement the specific spatial areas that affect green planning and provide methods that can guide the layout of green space,Xi'an is taken as an example to quantify the relationship between the spatial distribution of vegetation and construction land and the surface temperature,and determine the specific areas of green heat decreasing.Through convolution calculation,the temperature change is 0 as the boundary to refine the cold and hot regions,and extract the layout adjustment location of urban micro-renewal space.Besides,the regression algorithm based on machine learning is used to predict the landscape surface temperature including green land,construction land,and water as the indicator of thermal environment,thus providing a reference for the thoughtful planning.Through the evaluation of the research results and comparison with existing methods,the urban spatial distribution structure can be quantified by using the changes in surface temperature and land cover,which can provide specific spatial areas as reference for green planning.
出处
《中国园林》
北大核心
2020年第10期69-74,共6页
Chinese Landscape Architecture
关键词
风景园林
城市热环境
地表温度
地表覆盖物
绿地降温效应
landscape architecture
urban thermal environment
surface temperature
land cover
green space cooling effect
