基于绿视率的福州市鼓楼区道路绿化水平评价

编号 lyqk008872

中文标题 基于绿视率的福州市鼓楼区道路绿化水平评价

作者 林锦涵  陈芸芝  汪小钦 

作者单位 1. 福州大学卫星空间信息技术综合应用国家地方联合工程研究中心 福州 350116;
2. 空间数据挖掘与信息共享教育部重点实验室 福州 350116

期刊名称 中国城市林业 

年份 2021 

卷号 19

期号 3

中文摘要 针对当前城市街道绿化评价中缺乏对三维空间绿化效果的考量，文章以福州市鼓楼区为例，采集该区域的百度街景图片，基于SegNet图像语义分割模型计算绿视率以评估三维空间绿化水平，通过像元二分法计算植被覆盖度表征平面绿化水平，在此基础上利用熵权法对鼓楼区道路的整体绿化水平进行评价。结果表明：1）福州市鼓楼区道路总体的绿视率均值为32.44%、植被覆盖度均值为31.32%；鼓楼区道路绿化的绿视率水平比植被覆盖度水平更稳定。2）在基于熵权法的道路整体绿化水平评价中，鼓西街道的道路整体绿化评价得分最高。3）在所有道路中，鼓西街道的北梦山路绿化水平最好，而安泰街道的新权路最差。

关键词 街景图片  绿视率  SegNet  植被覆盖度  熵权法  福州 

基金项目 中央引导地方科技发展专项（2017L3012）

英文标题 Road Greening Level Evaluation of Gulou District in Fuzhou Based on Visible Green Index

作者英文名 Lin Jinhan, Chen Yunzhi, Wang Xiaoqin

单位英文名 1. National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350116, China;
2. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou 350116, China

英文摘要 In view of the lack of consideration of the three-dimensional greening effect in the current urban street greening assessment, this paper takes the Gulou District of Fuzhou City as a case to collect Baidu Streetscape pictures to calculate the visible green index using the SegNet image semantic segmentation model and the vegetation coverage using the pixel dichotomy method to characterize the effect of two-dimensional greening. On this basis, the entropy weight method is used to evaluate the overall street greening level of the study area. The results show that: 1) The average visible green index of street in Gulou District is 32.44%, and the average vegetation coverage is 31.32%. The overall visible green index is more stable than the vegetation coverage level; 2) In the evaluation of the street greening level based on the entropy method, the overall street greening evaluation of Guxi Sub-districts is the highest scored; and 3) Among all the streets, Beimengshan Road in Guxi Sub-district has the best greening level, while Xinquan Road in Antai Sub-district has the worst.

英文关键词 street view picture;visible green index;SegNet;vegetation coverage;entropy weight method;Fuzhou

起始页码 73

截止页码 77,84

投稿时间 2019/11/20

作者简介 林锦涵(1993-),男,硕士生,研究方向为空间信息技术与城乡规划管理。E-mail:18144046763@163.com

通讯作者介绍 陈芸芝(1982-),女,博士,副研究员,研究方向为环境与自然资源遥感。E-mail:chenyunzhi@fzu.edu.com

E-mail chenyunzhi@fzu.edu.com

DOI 10.12169/zgcsly.2019.11.20.0002

参考文献 [1] 邓小军,王洪刚.绿化率绿地率绿视率[J].新建筑,2002(6):75-76.
[2] 张丽英,裴韬,陈宜金,等.基于街景图像的城市环境评价研究综述[J].地球信息科学学报,2019,21(1):46-58.
[3] 龙瀛,叶宇.人本尺度城市形态:测度、效应评估及规划设计响应[J].南方建筑,2016(5):41-47.
[4]吴正旺,马欣,杨鑫.高密度城市居住区的绿视率调查:以北京为例[J].华中建筑,2014,32(2):85-89.
[5]肖希,韦怡凯,李敏.日本城市绿视率计量方法与评价应用[J].国际城市规划,2018,33(2):98-103.
[6]唐婧娴,龙瀛,翟炜,等.街道空间品质的测度、变化评价与影响因素识别:基于大规模多时相街景图片的分析[J].新建筑,2016(5):110-115.
[7]LI XJ,RATTI C,SEIFERLING I.Mapping urban landscapes along streets using google street view[C]//International Cartographic Conference. Springer,Cham,2017:341-356.
[8]LI X J,ZHANG C R,LI W D,et al.,ssessing street-level urban greenery using google street view and a modified green view index[J].Urban Forestry & Urban Greening,2015,14(3):675-685.
[9]Lu Y.The association of urban greenness and walking behavior:using google street view and deep learning techniques to estimate Residents' exposure to urban greenness[J].International Journal of Environmental Research and Public Health,2018,15(8):1576.
[10]LONG Y,LIU L.How green are the streets?An analysis for central areas of Chinese cities using tencent street view[J].PLoS ONE,2017,12(2):e0171110.
[11]叶宇,张灵珠,颜文涛,等.街道绿化品质的人本视角测度框架:基于百度街景数据和机器学习的大规模分析[J].风景园林,2018,25(8):24-29.
[12]崔喆,何明怡,陆明.基于街景图像解译的寒地城市绿视率分析研究:以哈尔滨为例[J].中国城市林业,2018,16(5):34-38.
[13]陈明,戴菲,李文佩,等.基于绿视率的城市绿化评估:以武汉江汉区为例[J].中国城市林业,2019,17(3):1-6.
[14]常亮,邓小明,周明全,等.图像理解中的卷积神经网络[J].自动化学报,2016,42(9):1300-1312.
[15]BADRINARAYANAN V,KENDALL A,CIPOLLA R.SegNet:a deep convolutional encoder-decoder architecture for scene segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017(99):1.
[16]杨建宇,周振旭,杜贞容,等.基于SegNet语义模型的高分辨率遥感影像农村建设用地提取[J].农业工程学报,2019,35(5):251-258.
[17]赵庆,唐洪辉,魏丹,等.基于绿视率的城市绿道空间绿量可视性特征[J].浙江农林大学学报,2016,33(2):288-294.
[18]YANG J,ZHAO L,MCBRIDE J,et al.Can you see green?Assessing the visibility of urban forests in cities[J].Landscape and Urban Planning,2009,91(2):97-104.
[19]李小江,蔡洋,卡洛·拉蒂.基于街道图像与深度学习的城市景观研究[J].景观设计学,2018,6(2):20-29.
[20]李苗苗,吴炳方,颜长珍,等.密云水库上游植被覆盖度的遥感估算[J].资源科学,2004,26(4):153-159.
[21]程启月.评测指标权重确定的结构熵权法[J].系统工程理论与实践,2010,30(7):1225-1228.

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