基于网格的街区尺度城市绿度度量方法

doi:10.12082/dqxxkx.2022.210743

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (8): 1475-1487.doi: 10.12082/dqxxkx.2022.210743

• 地球信息科学理论与方法 • 上一篇下一篇

基于网格的街区尺度城市绿度度量方法

廖周伟¹^,²(), 关燕宁¹^,^*(), 郭杉¹, 蔡丹路¹, 于敏³, 姚武韬¹, 张春燕¹, 邓锐¹^,²

1.中国科学院空天信息创新研究院,北京 100101
2.中国科学院大学,北京100049
3.深圳市城市规划设计研究院,深圳 518031

收稿日期:2021-11-19 修回日期:2022-02-08 出版日期:2022-08-25 发布日期:2022-10-25
通讯作者: *关燕宁(1963— ),女,北京人,研究员,研究方向为气候变化对环境的影响。E-mail: guan@irsa.ac.cn。
作者简介:廖周伟(1997— ),男,广西河池人,硕士生,研究方向为城市地表要素类型变化对环境的影响。E-mail: liaozhouwei19@mails.ucas.ac.cn
基金资助:
中国科学院战略性先导科技专项项目(XDA23100504)

A Measure of Block Scale Urban Green Index in Urban Area based on Grid Method

LIAO Zhouwei¹^,²(), GUAN Yanning¹^,^*(), GUO Shan¹, CAI Danlu¹, YU Min³, YAO Wutao¹, ZHANG Chunyan¹, DENG Rui¹^,²

1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Urban Planning & Design Institude of Shenzhen, Shenzhen 518031, China

Received:2021-11-19 Revised:2022-02-08 Online:2022-08-25 Published:2022-10-25
Contact: GUAN Yanning
Supported by:
The Strategic Priority Research Program of Chinese Academy of Science(XDA23100504)

摘要/Abstract

摘要：

本文提出一种基于网格的街区尺度城市绿度度量方法。其以900 m×900 m的网格单元作为以城市主干道划分所形成街区尺度的表征,根据网格单元内植被覆盖度与植被构成之间的关系构建绿度指标。通过与传统的基于植被面积占比的格网法所计算的绿度指数进行比较。发现本文方法建立的绿度指标能在街区尺度上提供更加丰富的城市绿度信息,有效地解决了传统方法难以反映的植被覆盖度近似区域因其内部植被构成不同所带来的绿度差异问题。为城市规划、设计以及管理提供了一种新视角。基于该绿度指标,利用Landsat TM/OLI 2009、2015和2019年不同季节的遥感数据分析北京市城市绿化的时空变化特征。结果表明：北京市主城区城市绿化的面积和质量在2009—2019年显著提升,且秋季最为明显。绿化的变化在四环内外呈现出不同的模式。四环内主要为绿化面积的增加,四环外则在绿化面积和绿化质量上都有明显的增长。

关键词: 城市绿化, 街区尺度, 网格法, 绿度指标, 季节变化, 遥感, 北京市

Abstract:

To quantify urban green space using remote sensing data, this paper proposed a grid-based urban green space measurement method at city block scale. The grid unit of 900 m×900 m was set to represent the block scale formed by the main road of the city. The vegetation coverage and vegetation composition were used together to generate the green index in each unit. Unlike the green index calculated by the traditional grid method based on the proportion of vegetation area, the green index in this paper enhanced urban greenness information particularly in areas with moderate to high vegetation coverage. We applied this method in Beijing city and diagnosed the spatiotemporal differences of local urban green space over years. We used Landsat TM/OLI datasets of 2009, 2015, and 2019 in spring, summer, and autumn, respectively. Results showed that both the quantity and quality of urban green space in Beijing have increased significantly from 2009 to 2019, especially in autumn. The changes of urban green space showed different patterns inside and outside of the Fourth Ring Road. Vegetation coverage showed an increase within the Fourth Ring Road, while outside the Fourth Ring Road, there was a significant increase in both vegetation coverage and quality.

Key words: urban green space, city block scale, grid method, green index, seasonal differentiation, remote sensing, Beijing

廖周伟, 关燕宁, 郭杉, 蔡丹路, 于敏, 姚武韬, 张春燕, 邓锐. 基于网格的街区尺度城市绿度度量方法[J]. 地球信息科学学报, 2022, 24(8): 1475-1487.DOI:10.12082/dqxxkx.2022.210743

LIAO Zhouwei, GUAN Yanning, GUO Shan, CAI Danlu, YU Min, YAO Wutao, ZHANG Chunyan, DENG Rui. A Measure of Block Scale Urban Green Index in Urban Area based on Grid Method[J]. Journal of Geo-information Science, 2022, 24(8): 1475-1487.DOI:10.12082/dqxxkx.2022.210743

图/表 15

图1

图2

表1

图3

表2

图4

图5

表3

图6

图7

图8

图9

图10

图11

图12

参考文献 39

[1]	邱国玉, 张晓楠. 21世纪中国的城市化特点及其生态环境挑战[J]. 地球科学进展, 2019, 34(6):640-649. doi: 10.11867/j.issn.1001-8166.2019.06.0640
	[ Qiu G Y, Zhang X N. China's urbanization and its ecological environment challenges in the 21^st Century[J]. Advances in Earth Science, 2019, 34(6):640-649. ] DOI: 10.11867/j.issn.1001-8166.2019.06.0640 doi: 10.11867/j.issn.1001-8166.2019.06.0640
[2]	Shepherd J M. Evidence of urban-induced precipitation variability in arid climate regimes[J]. Journal of Arid Environments, 2006, 67(4):607-628. DOI: 10.1016/j.jaridenv.2006.03.022 doi: 10.1016/j.jaridenv.2006.03.022
[3]	Yang J, Wang Y, Xiu C, et al. Optimizing local climate zones to mitigate urban heat island effect in human settlements[J]. Journal of Cleaner Production, 2020, 275:123767. DOI: 10.1016/j.jclepro.2020.123767 doi: 10.1016/j.jclepro.2020.123767
[4]	熊鹰, 章芳. 基于多源数据的长沙市人居热环境效应及其影响因素分析[J]. 地理学报, 2020, 75(11):2443-2458. doi: 10.11821/dlxb202011013
	[ Xiong Y, Zhang F. Thermal environment effects of urban human settlements and influencing factors based on multi-source data: A case study of Changsha city[J]. Acta Geographica Sinica, 2020, 75(11):2443-2458. ] DOI: 10.11821/dlxb202011013 doi: 10.11821/dlxb202011013
[5]	邵帅, 李欣, 曹建华. 中国的城市化推进与雾霾治理[J]. 经济研究, 2019, 54(2):148-165.
	[ Shao S, Li X, Cao J H. Urbanization promotion and haze pollution governance in China[J]. Economic Research Journal, 2019, 54(2):148-165. ]
[6]	王占山, 李云婷, 陈添, 等. 2013年北京市PM2.5的时空分布[J]. 地理学报, 2015, 70(1):110-120. doi: 10.11821/dlxb201501009
	[ Wang Z, Li Y, Chen T, et al. Spatial-temporal characteristics of PM2.5 in Beijing in 2013[J]. Acta Geographica Sinica, 2015, 70(1):110-120. ] DOI: 10.11821/dlxb201501009 doi: 10.11821/dlxb201501009
[7]	Taylor L, Hochuli D F. Defining greenspace: Multiple uses across multiple disciplines[J]. Landscape & Urban Planning, 2017, 158:25-38. DOI: 10.1016/j.landurbplan.2016.09.024 doi: 10.1016/j.landurbplan.2016.09.024
[8]	苏泳娴, 黄光庆, 陈修治, 等. 城市绿地的生态环境效应研究进展[J]. 生态学报, 2011, 31(23):7287-7300.
	[ Su Y X, Huang G Q, Chen X Z, et al. Research progress in the eco-environmental effects of urban green spaces[J]. Acta Ecologica Sinica, 2011, 31(23):7287-7300. ]
[9]	胡云锋, 赵冠华, 张云芝. 北京城市绿化的时空动态分析[J]. 地球信息科学学报, 2018, 20(3):332-339. doi: 10.12082/dqxxkx.2018.170080
	[ Hu Y F, Zhao G H, Zhang Y Z. Analysis of spatial and temporal dynamics of green coverage and vegetation greenness in Beijing[J]. Journal of Geo-information Science, 2018, 20(3):332-339. ] DOI: 10.12082/dqxxkx.2018.170080 doi: 10.12082/dqxxkx.2018.170080
[10]	Javadi R. Urban green space and health: the role of thermal comfort on the health benefits from the urban green space; a review study[J]. Building and Environment, 2021, 202(4):108039. DOI: 10.1016/j.buildenv.2021.108039 doi: 10.1016/j.buildenv.2021.108039
[11]	崔佳奇, 刘宏涛, 陈媛媛. 中国城市建成区绿化覆盖率变化特征及影响因素分析[J]. 生态环境学报, 2021, 30(2):331.
	[ Cui J Q, Liu H T, Chen Y Y. Changes in green coverage rate of urban built-up areas in china and influencing factors[J]. Ecology and Environment, 2021, 30(2):331. ] DOI: 10.16258/j.cnki.1674-5906.2021.02.012 doi: 10.16258/j.cnki.1674-5906.2021.02.012
[12]	刘晔. 我国城镇化率对建成区绿化覆盖率的影响研究[D]. 苏州: 苏州科技大学, 2017.
	[ Liu Y. Research on the relationship between green coverage rate of built district and urbanization rate in China[D]. Suzhou: Suzhou University of Science and Technology. 2017. ]
[13]	易扬, 胡昕利, 史明昌, 等. 基于MODIS NDVI的长江中游区域植被动态及与气候因子的关系[J]. 生态学报, 2021, 41(19):7796-7807.
	[ Yi Y, Hu X L, Shi M, et al. Vegetation dynamic and its relationship with climate factors in the middle reaches of the Yangtze River based on MODIS NDVI[J]. Acta Ecologica Sinica, 2021, 41(19):7796-7807. ] DOI: 10.5846/stxb202007251953 doi: 10.5846/stxb202007251953
[14]	谢舒笛, 莫兴国, 胡实, 等. 三北防护林工程区植被绿度对温度和降水的响应[J]. 地理研究, 2020, 39(1):152-165. doi: 10.11821/dlyj020181071
	[ Xie S D, Mo X G, Hu S, et al. Responses of vegetation greenness to temperature and precipitation in the Three- North Shelter Forest Program[J]. Geographical Research, 2020, 39:152-165. ] DOI: 10.11821/dlyj020181071 doi: 10.11821/dlyj020181071
[15]	Ghosh P, Singh K K. Spatiotemporal dynamics of urban green and blue spaces using geospatial techniques in Chandannagar city, India[J]. GeoJournal, 2021, 86(5):1-18
[16]	马默衡, 薛飞, 党安荣, 等. 基于动态遥感数据的北京主城区环带间植被覆盖变化[J]. 环境工程技术学报, 2019, 9(4):404-143.
	[ Ma M H, Xue F, Dang A R, et al. Study on the spatial-temporal change of vegetation coverage between the belts of Beijing's main urban area based on dynamic remote sensing data[J]. Journal of Environmental Engineering Technology, 2019, 9(4):404-143. ] DOI: 10.12153/j.issn.1674-991X.2019.05.141 doi: 10.12153/j.issn.1674-991X.2019.05.141
[17]	刘玉琴. 基于建筑物尺度的城市绿度空间遥感建模研究[D]. 福州: 福州师范大学, 2014.
	[ Liu Y Q. Study on urban green space based on built up scale using high resolution satellite data[D]. Fuzhou: Fujian Normal University. 2014. ]
[18]	吴俊, 孟庆岩, 占玉林, 等. 一种基于移动窗口的城市绿度遥感度量方法[J]. 地球信息科学学报, 2016, 18(4):544-552. doi: 10.3724/SP.J.1047.2016.00544
	[ Wu J, Meng Q Y, Zhan Y L, et al. A measure of urban green index in urban areas based on moving window method[J]. Journal of Geo-information Science, 2016, 18(4):544-552. ] DOI: 10.3724/SP.J.1047.2016.00544 doi: 10.3724/SP.J.1047.2016.00544
[19]	Schöpfer E, Lang S, Blaschke T. A "Green Index" incorporating remote sensing and citizen's perception of green space[J]. International Archives of Photogramm, Remote Sensing and Spatial Information Sciences, 2005, 27(6):1-6.
[20]	Gupta K, Kumar P, Pathan S K, et al. Urban neighborhood green index-a measure of green spaces in urban areas[J]. Landscape and Urban Planning, 2012, 105(3):325-335. DOI: 10.1016/j.landurbplan.2012.01.003 doi: 10.1016/j.landurbplan.2012.01.003
[21]	Hofmann P, Strobl J, Nazarkulova. A mapping green spaces in Bishkek—how reliable can spatial analysis be?[J]. Remote Sensing, 2011, 3(6):1088-1103. DOI: 10.3390/rs3061088 doi: 10.3390/rs3061088
[22]	Jin L, Pan X, Liu L, et al. Block-based local climate zone approach to urban climate maps using the UDC model[J]. Building and Environment, 2020, 186:107334. DOI: 10.1016/j.buildenv.2020.107334 doi: 10.1016/j.buildenv.2020.107334
[23]	Long Y, Shen Y, Jin X. Mapping block-level urban areas for all chinese cities[J]. Annals of the American Association of Geographers, 2015, 106(1):1-18. DOI: 10.1080/00045608.2015.1095062 doi: 10.1080/00045608.2015.1095062
[24]	李志强, 周伟奇. 街区尺度城市内部动态度的量化——以深圳市为例[J]. 生态学报, 2021, 41(6):2180-2189.
	[ Li Z Q, Zhou W Q. Quantifying within-city landscape dynamics at the block level: A case study of Shenzhen[J]. Acta Ecologica Sinica, 2021, 41(6):2180-2189. ] DOI: 10.5846/stxb202006051463 doi: 10.5846/stxb202006051463
[25]	赵格. 城市网格形态研究[D]. 天津: 天津大学, 2008.
	[ Zhao G. Research on city grid form. Tianjin: Tianjin University, 2008. ] DOI: 10.7666/d.y1517730 doi: 10.7666/d.y1517730
[26]	于立忠, 于水强, 史建伟, 等. 不同类型人工阔叶红松林高等植物物种多样性[J]. 生态学杂志, 2005, 24(11):1253-1257.
	[ Yu L Z, Yu S Q, Shi J W, et al. Higher plants species diversity in different types of artificial broad- leaved Korean pine forests[J]. Chinese Journal of Ecology, 2005, 24(11):1253-1257. ] DOI: 10.3321/j.issn:1000-4890.2005.11.001 doi: 10.3321/j.issn:1000-4890.2005.11.001
[27]	Gandhi G M, Parthiban S, Thummalu N, et al. NDVI: Vegetation change detection using remote sensing and gis: A case study of Vellore District[J]. Procedia computer science, 2015, 57:1199-1210. DOI: 10.1016/j.procs.2015.07.415 doi: 10.1016/j.procs.2015.07.415
[28]	Bhandari A K, Kumar A, Singh G K. Feature extraction using Normalized Difference Vegetation Index (NDVI): Acase study of Jabalpur city[J]. Procedia Technology, 2012, 6:612-621. DOI: 10.1016/j.protcy.2012.10.074 doi: 10.1016/j.protcy.2012.10.074
[29]	Liu Y, Li Z, Chen Y, et al. Evaluation of consistency among three NDVI products applied to High Mountain Asia in 2000-2015[J]. Remote Sensing of Environment, 2022, 269:112821. DOI: 10.1016/j.rse.2021.112821 doi: 10.1016/j.rse.2021.112821
[30]	Cai D, Fraedrich K, Sielmann F, et al. Climate and vegetation: An ERA-Interim and GIMMS NDVI analysis[J]. Journal of Climate, 2014, 27(13):5111-5118. DOI: 10.1175/jcli-d-13-00674.1 doi: 10.1175/jcli-d-13-00674.1
[31]	Cai D, Fraedrich K, Sielmann F, et al. Vegetation dynamics on the Tibetan Plateau (1982-2006): an attribution by ecohydrological diagnostics[J]. Journal of Climate, 2015, 28(11):4576-4584. DOI: 10.1175/jcli-d-14-00692.1 doi: 10.1175/jcli-d-14-00692.1
[32]	姚武韬, 关燕宁, 郭杉, 等. 三亚热带雨林环境植被和地表能量空间分布特征[J]. 地球信息科学学报, 2017, 19(7):950-961. doi: 10.3724/SP.J.1047.2017.00950
	[ Yao W T, Guan Y N, Guo S, et al. Spatial distribution of land surface vegetation-energy relationship in Sanya tropical rain forest regions[J]. Journal of Geo-information Science, 2017, 19(7):950-961. ] DOI: 10.3724/SP.J.1047.2017.00950 doi: 10.3724/SP.J.1047.2017.00950
[33]	王昭. 城市小尺度街区研究[D]. 合肥: 合肥工业大学, 2013.
	[ Wang Z. The study on the planning of urban blocks in the small-scale[D]. Heifei: Hefei University of Technology, 2013. ]
[34]	Yao L, Li T, Xu M, et al. How the landscape features of urban green space impact seasonal land surface temperatures at a city-block-scale: An urban heat island study in Beijing, China[J]. Urban Forestry & Urban Greening, 2020, 52:126704. DOI: 10.1016/j.ufug.2020.126704 doi: 10.1016/j.ufug.2020.126704
[35]	Wang H, Hu Y, Tang L, et al. Distribution of urban blue and green space in beijing and its influence factors[J]. Sustainability, 2020, 12(6):2252. DOI: 10.3390/su12062252 doi: 10.3390/su12062252
[36]	Xia C, Yeh A G O, Zhang A. Analyzing spatial relationships between urban land use intensity and urban vitality at street block level: A case study of five Chinese megacities[J]. Landscape and Urban Planning, 2020, 193:103669. DOI: 10.1016/j.landurbplan.2019.103669 doi: 10.1016/j.landurbplan.2019.103669
[37]	Liu X, Long Y. Automated identification and characterization of parcels with OpenStreetMap and points of interest[J]. Environment and Planning B: Planning and Design, 2016, 43(2):341-360. DOI: 10.1177/0265813515604767 doi: 10.1177/0265813515604767
[38]	肖彦, 绿色尺度下的城市街区规划初探[D]. 武汉: 华中科技大学, 2011.
	[ Xiao Y. The study on the planning of urban blocks in the green-scale[D]. Wuhan: Huazhong University of Science and Technology, 2011. ] DOI: 10.7666/d.d188551 doi: 10.7666/d.d188551
[39]	严亚瓴. 20世纪80年代至今北京城市绿色空间演变及机制研究[D]. 北京: 北京林业大学, 2017.
	[ Yan Y L. Evolvement and mechanism of urban green space in Beijing since the 1980s[D]. Beijing: Beijing Forestry University, 2017. ]

绿度指标	对应地表绿化特征
低盖低植	单位面积内植被较少,且基本单元内绿化构成主体为冠层繁茂程度和多样性水平较低,总体情况相对较差的植被
低盖中植	单位面积内植被较少,基本单元内绿化构成主体植被情况处于中等水平
低盖高植	单位面积内植被较少,但基本单元内绿化构成主体为冠层繁茂程度、多样性水平较高,总体情况良好的植被
中盖低植	单位面积内植被数量中等,但基本单元内绿化构成主体为冠层繁茂程度和多样性水平较低,总体情况相对较差的植被
中盖中植	单位面积内植被数量中等,基本单元内绿化构成主体植被情况处于中等水平
中盖高植	单位面积内植被数量中等,但基本单元内绿化构成主体为冠层繁茂程度、多样性水平较高,总体情况良好的植被
高盖低植	单位面积内植被较多,但基本单元内绿化构成主体为冠层繁茂程度和多样性水平较低,总体情况相对较差的植被
高盖中植	单位面积内植被较多,但基本单元内绿化构成主体植被情况处于中等水平
高盖高植	单位面积内植被较多,且基本单元内绿化构成主体为冠层繁茂程度、多样性水平较高,总体情况良好的植被

年份	春季	夏季	秋季
2009	20090517/TM	20090720/TM	20090922/TM
2015	20150518/OLI	20150822/OLI	20150923/OLI
2019	20190513/OLI	20190817/OLI	20190918/OLI

指标		本文绿度指标
指标		低盖低植/中盖低值/高盖低植	低盖中植/中盖中值/高盖中植	低盖高植/中盖高值/高盖高植
传统网格法计算的绿度指数	低覆盖度	10	0	0
	中覆盖度	7	3	0
	高覆盖度	5	5	0

基于网格的街区尺度城市绿度度量方法

A Measure of Block Scale Urban Green Index in Urban Area based on Grid Method

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 39

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	范兰馨, 吴艳红, 迟皓婧, 郑思齐, 闫家恒, 任永康, 孙忠华. 暖湿化下西北地区水体变化趋势遥感监测[J]. 地球信息科学学报, 2023, 25(9): 1842-1854.
[2]	邢子瑶, 董芯蕊, 昝糈莉, 杨帅, 黄梓焓, 刘哲, 张晓东. 融合VGI和遥感等多源数据的洪涝范围提取与模拟方法[J]. 地球信息科学学报, 2023, 25(9): 1869-1881.
[3]	张俊瑶, 杨晓梅, 王志华, 杨海坤, 张博淳, 万庆, 雷梅. 绿色发展理念视角下内蒙古煤矿区格局演变分析[J]. 地球信息科学学报, 2023, 25(8): 1655-1668.
[4]	曹煜, 方秀琴, 杨露露, 蒋心远, 廖美玉, 任立良. 基于随机森林的西辽河流域CCI土壤湿度降尺度研究[J]. 地球信息科学学报, 2023, 25(8): 1669-1681.
[5]	张寒博, 李彤, 李晓芳, 邓滢, 邓应彬, 荆文龙, 胡义强, 李勇, 杨骥. 无人机遥感和GWR结合的水华短时预测方法[J]. 地球信息科学学报, 2023, 25(8): 1682-1698.
[6]	林娜, 何静, 王斌, 唐菲菲, 周俊宇, 郭江. 结合植被光谱特征与Sep-UNet的城市植被信息智能提取方法[J]. 地球信息科学学报, 2023, 25(8): 1717-1729.
[7]	吴敏, 张明达, 李盼盼, 张勇健. 面向多源遥感影像数据的溯源模型研究[J]. 地球信息科学学报, 2023, 25(7): 1325-1335.
[8]	杨颖频, 吴志峰, 黄启厅, 骆剑承, 吴田军, 董文, 胡晓东, 肖文菊. 协同遥感与统计数据的粤西甘蔗种植分布提取及时空分析[J]. 地球信息科学学报, 2023, 25(5): 1012-1026.
[9]	刘潇, 刘智, 林雨准, 王淑香, 左溪冰. 面向遥感影像场景分类的类中心知识蒸馏方法[J]. 地球信息科学学报, 2023, 25(5): 1050-1063.
[10]	衡雪彪, 许捍卫, 唐璐, 汤恒, 许怡蕾. 基于改进全卷积神经网络模型的土地覆盖分类方法研究[J]. 地球信息科学学报, 2023, 25(3): 495-509.
[11]	高鹏飞, 曹雪峰, 李科, 游雄. 融合多元稀疏特征与阶层深度特征的遥感影像目标检测[J]. 地球信息科学学报, 2023, 25(3): 638-653.
[12]	秦肖伟, 程博, 杨志平, 李林, 董文, 张新, 杨树文, 靳宗义, 薛庆. 基于时序遥感影像的西南山区地块尺度作物类型识别[J]. 地球信息科学学报, 2023, 25(3): 654-668.
[13]	陈凯, 雷少华, 代文, 王春, 刘爱利, 李敏. 基于开源数据和条件生成对抗网络的地形重建方法[J]. 地球信息科学学报, 2023, 25(2): 252-264.
[14]	饶子昱, 卢俊, 郭海涛, 余东行, 侯青峰. 利用视角转换的跨视角影像匹配方法[J]. 地球信息科学学报, 2023, 25(2): 368-379.
[15]	王龙号, 蓝朝桢, 姚富山, 侯慧太, 武蓓蓓. 多源遥感影像深度特征融合匹配算法[J]. 地球信息科学学报, 2023, 25(2): 380-395.