摘要
With the expansion of a city,the urban green space is occupied and the urban heat island effect is serious.Greening the roof surfaces of urban buildings is an effective way to increase the area of urban green space and improve the urban ecological environment.To provide effective data support for urban green space planning,this paper used high-resolution images to(1)obtain accurate building spots on the map of the study area through deep learning assisted manual correction;and(2)establish an evaluation index system of roof greening including the characteristics of the roof itself,the natural environment and the human society environment.The weight values of attributes not related to the roof itself were calculated by Analytic Hierarchy Process(AHP).The suitable green roof locations were evaluated by spatial join,weighted superposition and other spatial analysis methods.Taking the areas within the Chengdu city’s third ring road as the study area,the results show that an accurate building pattern obtained by deep learning greatly improves the efficiency of the experiment.The roof surfaces unsuitable for greening can be effectively classified by the method of feature extraction,with an accuracy of 86.58%.The roofs suitable for greening account for 48.08%,among which,the high-suitability roofs,medium-suitability roofs and low-suitability roofs represent 45.32%,38.95%and 15.73%.The high-suitability green buildings are mainly distributed in the first ring district and the western area outside the first ring district in Chengdu.This paper is useful for solving the current problem of the more saturated high-density urban area and allowing the expansion of the urban ecological environment.
随着城市扩张,城市绿地被占用,城市热岛效应严重,对城市建筑屋顶面进行绿化,是增加城市绿地面积、改善城区生态环境的有效途径。针对高密度城区建设用地趋于饱和及生态环境难以拓展的现状,本文利用高分二号卫星影像,通过深度学习辅助人工修正获取研究区精确建筑图斑,建立含屋顶自身属性特征、自然环境特征、人文社会环境特征的屋顶绿化评价指标体系,运用层次分析法计算非屋顶自身属性特征的权重值,通过空间连接、加权叠加等空间分析方法综合评估适宜绿化的屋顶面。文章以成都市三环线以内区域为示范区展开研究,研究结果表明,通过深度学习获取的精确的建筑图斑,可以提高实验效率;而通过特征提取的方法,可对不适宜绿化的屋顶面有效地进行分类,精度达86.58%,其中适宜绿化的图斑数量占总数比48.08%,高适类、中适类、低适类的建筑图斑占比分别为45.32%、38.95%、15.73%,高适宜绿化建筑主要分布在成都市一环区及一环区以外西部地区。研究结果可为后续空间规划研究提供有效数据支撑。
作者
LUO Luhua
CHEN Mingjie
DONG Lulu
SU Wei
LI Xin
HU Xiaodong
ZHANG Xin
LI Chen
CHENG Weiming
SHI Hanning
LUO Jiancheng
罗露花;陈铭杰;董路路;苏薇;李昕;胡晓东;张新;李晨;程维明;石含宁;骆剑承(兰州交通大学测绘与地理信息学院,兰州730070;地理国情监测技术应用国家地方联合工程研究中心,兰州730070;甘肃省地理国情监测工程实验室,兰州730070;中国矿业大学(北京)地球科学与测绘工程学院,北京100083;河北工程大学地球科学与工程学院,河北邯郸056000;天津大学城市规划设计研究院有限公司,天津300072;中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101;中科天启有限公司,江苏苏州215000;中国科学院空天信息研究院究院,北京100101;天津大学建筑学院,天津300072)
基金
The China Postdoctoral Science Foundation(2019M650830)
The National Key Research and Development Program of China(2016YFC0502903,2017YFB0504201)
The Seed Foundation of Tianjin University(2021XSC-0036)
The Natural Science Foundation of Tianjin(19JCYBJC22400)。
