摘要
城市绿地是城市生态系统的关键组成部分,对改善生态环境和提高人居质量具有无法替代的作用。高精度的城市绿地识别是城市更新及绿色基础设施优化的基础,目前针对超大城市绿地的识别与空间异质性研究相对较少。本研究以西安市为例,结合城市街景图像与高分二号(GF-2)遥感影像,采用ISODATA分类、K-Means分类及卷积神经网络模型等方法,对绿地进行多维度、降尺度、高精度的识别与分析。结果表明:(1)K-Means分类方法的识别精度(84.5%)显著高于ISODATA分类方法(62.4%),且更能准确映射绿地的空间特征与异质性规律,K-Means方法识别的绿地覆盖率为0.2770,低于ISODATA的0.3607;(2)西安主城区内街道平均绿视率为0.1560,表明整体绿化水平良好,但不同道路存在明显的两极分化,其中30%的采样点的绿视率低于0.0800;主城区整体呈现出高等级道路平均绿视率高于低等级道路的趋势,即主要道路>次要道路>干道>三级道路;(3)西安主城区内街道绿视率与片区植被覆盖率整体呈现正相关,但在部分路段关联性较小,反映了街道地面纵剖面与天空俯视图的差异,二者结合可以更准确地评估和量化城市绿地。本研究可为西安市绿地规划、绿色基础设施建设和智慧管理提供一定借鉴,同时可为其他城市绿地的高精度识别与空间解析提供技术参考。
Urban green spaces are critical components of urban ecosystems,playing an irreplaceable role in improving the ecological environment and enhancing quality of life.High-precision identification of urban green spaces is fundamental for urban renewal and optimizing green infrastructure.However,research on the identification and spatial heterogeneity of green spaces in megacities remains relatively limited.This study,taking Xi'an as an example,integrates urban street view images and GF-2(Gaofen-2)satellite imagery,employing methods such as ISODATA classification,K-Means classification,and convolutional neural networks to achieve multi-dimensional,downscaled,and high-precision identification and analysis of green spaces.The results indicate the following:(1)The K-Means classification method demonstrates significantly higher accuracy(84.5%)compared to the ISODATA classification method(62.4%)and more accurately maps the spatial characteristics and heterogeneity patterns of green spaces.The green space coverage identified by the K-Means method is 0.2770,which is lower than the 0.3607 identified by ISODATA.(2)The average Green View Index(GVI)of streets in Xi'an's main urban area is 0.1560,indicating a generally good level of street greening.However,there is notable polarization across different roads,with 30%of sampling points having a GVI below 0.0800.Overall,the GVI of higher-grade roads is greater than that of lower-grade roads,following the trend:primary roads>secondary roads>trunk roads>tertiary roads.(3)There is a positive correlation between the GVI of streets and the vegetation coverage in their surrounding areas in Xi'an's main urban area.However,this correlation weakens in certain road sections,reflecting differences between vertical cross-sections and overhead views of the streets.Combining these perspectives provides a more accurate assessment and quantification of urban green spaces.This study provides a reference for green space planning,green infrastructure construction,and smart management in Xi'an,as well as technical guidance for high-precision identification and spatial analysis of urban green spaces in other cities.
作者
陈虹
汤军
龚阳春
陈志杰
王文达
王少华
CHEN Hong;TANG Jun;GONG Yangchun;CHEN Zhijie;WANG Wenda;WANG Shaohua(Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China;National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring,Lanzhou 730070,China;College of Architecture and Urban Planning,Beijing University of Technology,Beijing 100124,China;School of Architecture and Urban Planning,Lanzhou Jiaotong University,Lanzhou 730070,China;Gansu Provincial Engineering Laboratory for National Geographic State Monitoring,Lanzhou 730070,China;Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China)
出处
《地球信息科学学报》
EI
CSCD
北大核心
2024年第12期2818-2830,共13页
Journal of Geo-information Science
基金
国家重点研发计划项目(2023YFF0805904)
中国科学院百人计划青年项目(E43302020D、E2Z105010F)
甘肃省自然科学基金项目(24JRRA250)。
关键词
城市绿地
高分遥感影像
街景图像
遥感图像分类
西安市
urban green space
high-resolution remote sensing images
street view images
remote sensing image classification
Xi'an
作者简介
陈虹(1989-),女,浙江台州人,博士生,研究方向为地理空间分析与城市遥感。E-mail:chenplan@126.com;通讯作者:王少华(1983-),男,陕西宝鸡人,博士,研究员,研究方向为地理空间智能和空间优化。E-mail:wangshaohua@aircas.ac.cn。
