基于Google Earth Engine的粤港澳大湾区生态质量与城市化耦合协调度分析
Analysis of Coupling Coordination Degree of Ecological Quality and Urbanization of the Guangdong-Hong Kong-Macao Greater Bay Area Based on Google Earth Engine
DOI: 10.12677/GST.2022.104025, PDF,    科研立项经费支持
作者: 邱 琳, 郑华健*：广东省国土资源测绘院，广东 广州;自然资源部华南热带亚热带自然资源重点实验室，广东 广州;广东省自然资源科技协同创新中心，广东 广州
关键词: Google Earth Engine；粤港澳大湾区；生态环境；城市化；夜间灯光；Google Earth Engine； Guangdong-Hong Kong-Macao Greater Bay Area； Ecological Environment； Ur-banization； Nighttime Light
摘要: 研究快速城市化背景下如何平衡生态环境和城市化发展对促进城市群可持续发展具有重要意义。本文立足遥感视角，结合日间光学遥感和夜间灯光遥感数据，基于Google Earth Engine云平台对2000~2020年粤港澳大湾区城市群生态环境状况、城市化强度以及二者耦合协调度进行分析。结果表明：1) 粤港澳大湾区城市群在2000~2020年间RSEI均值稳定上升，生态环境呈现出好转的态势，但地区间的差异依旧显著；2) 经过20年的城市化发展，粤港澳大湾区城市群形成了以环珠江口的城市向四周逐步推进的城市化格局；3) 20年间粤港澳大湾区城市群的生态环境与城市化的协调程度空间分异较为明显，总体有所改善。研究结果可以为粤港澳大湾区城市群可持续发展提供数据支撑和科学参考。
Abstract: It is of great significance to study how to balance the ecological environment and urbanization de-velopment under the background of rapid urbanization to promote the sustainable development of urban agglomerations. Based on the perspective of remote sensing, this paper combines the data of daytime optical remote sensing and nighttime light remote sensing, and based on the Google Earth Engine cloud platform to analyze the ecological environment, urbanization intensity and coupling coordination degree of the Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration from 2000 to 2020. The results show that: 1) the average RSEI of the Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration increased steadily from 2000 to 2020, and the ecological environment showed a trend of improvement, but the differences between regions were still significant; 2) after 20 years of urbanization development, The urban agglomeration of the Guang-dong-Hong Kong-Macao Greater Bay Area has formed an urbanization pattern with cities surrounding the Pearl River Estuary gradually advancing to the surrounding areas; 3) in the past 20 years, the degree of coordination between the ecological environment and urbanization of the Guang-dong-Hong Kong-Macao Greater Bay Area urban agglomeration has been significantly different in space, and the overall level has improved. The research results can provide data support and scientific reference for the sustainable development of the Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration.
文章引用：邱琳, 郑华健. 基于Google Earth Engine的粤港澳大湾区生态质量与城市化耦合协调度分析[J]. 测绘科学技术, 2022, 10(4): 240-252. https://doi.org/10.12677/GST.2022.104025

参考文献

[1] 林晓晴. 海峡西岸城市群城市化质量与生态环境交互耦合关系定量研究[D]: [硕士学位论文]. 福州: 福建农林大学, 2020.
[2] 程晨, 张毅, 宁晓静. 城市集聚对城市土地扩张的影响——以长江中游城市群为例[J]. 中国房地产(下旬刊), 2019(3): 29-35.
[3] 陈悦. 长江中游城市群城镇化与生态环境关系定量研究[D]: [硕士学位论文]. 武汉: 武汉理工大学, 2019.
[4] 李雪铭, 郭玉洁, 田深圳, 白芝珍, 刘贺. 辽宁省城市人居环境系统耦合协调度时空格局演变及驱动力研究[J]. 地理科学, 2019, 39(8): 1209-1217.
[5] 任亚文, 曹卫东, 张宇, 苏鹤放, 王雪微. 长江经济带三大城市群城镇化与生态环境时空耦合特征[J]. 长江流域资源与环境, 2019, 28(11): 258-2600.
[6] Rogan, J. and Chen, D.M. (2004) Remote Sensing Technology for Mapping and Monitoring Land-Cover and Land-Use Change. Progress in Planning, 61, 301-325. [Google Scholar] [CrossRef] 
[7] 李卓, 孙然好, 张继超, 张翀. 京津冀城市群地区植被覆盖动态变化时空分析[J]. 生态学报, 2017, 37(22): 7418-7426. [Google Scholar] [CrossRef] 
[8] Ji, X.L., Li, X.Z., He, Y.Q. and Liu, X.L. (2019) A Simple Method to Improve Estimates of County-Level Economics in China Using Nighttime Light Data and GDP Growthrate. ISPRS International Journal of Geo-Information, 8, Article No. 419. [Google Scholar] [CrossRef] 
[9] 卓莉, 陈晋, 史培军, 辜智慧, 范一大, 一之瀬俊明. 基于夜间灯光数据的中国人口密度模拟[J]. 地理学报, 2005, 60(2): 266-276.
[10] Zhang, B., Chen, Z., Peng, D., Benediktsson, J.A., Liu, B., Zou, L., et al. (2019) Remotely Sensed Big Data: Evolution in Model Development for Information Extraction [Point of View]. Proceedings of the IEEE, 107, 2294-2301. [Google Scholar] [CrossRef] 
[11] Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R. (2017) Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Remote Sensing of Environment, 202, 18-27. [Google Scholar] [CrossRef] 
[12] 国务院办公厅. 粤港澳大湾区发展规划纲要[EB/OL]. http://www.gov.cn/gongbao/content/2019/content_5370836.htm, 2021-04-23.
[13] 周玉科, 高锡章, 倪希亮. 利用夜间灯光数据分析我国社会经济发展的区域不均衡特征[J]. 遥感技术与应用, 2017, 32(6): 1107-1113.
[14] 张秋媛, 彭明春, 王崇云, 周长威. 基于DMSP/OLS夜间灯光数据的贵州省人口分布及影响因子分析[J]. 云南大学学报(自然科学版), 2019, 41(5): 992-1000.
[15] 苏泳娴, 陈修治, 叶玉瑶, 吴旗韬, 张虹鸥, 黄宁生, 等. 基于夜间灯光数据的中国能源消费碳排放特征及机理[J]. 地理学报, 2013, 68(11): 1513-1526.
[16] 陈颖彪, 郑子豪, 吴志峰, 千庆兰. 夜间灯光遥感数据应用综述和展望[J]. 地理科学进展, 2019, 38(2): 205-223. [Google Scholar] [CrossRef] 
[17] 徐涵秋. 城市遥感生态指数的创建及其应用[J]. 生态学报, 2013, 33(24): 7853-7862.
[18] 刘智才, 徐涵秋, 李乐, 唐菲, 林中立. 基于遥感生态指数的杭州市城市生态变化[J]. 应用基础与工程科学学报, 2015, 23(4): 728-739.
[19] Xu, H.Q., Wang, M.Y., Shi, T.T., Guan, H.D., Fang, C.Y. and Lin, Z.L. (2018) Prediction of Ecological Effects of Potential Population and Impervious Surface Increases Using a Remote Sensing Based Ecological Index (RSEI). Ecological Indicators, 93, 730-740. [Google Scholar] [CrossRef] 
[20] Zheng, Z.H., Yang, Z.W., Chen, Y.B., Wu, Z.F. and Marinello, F. (2019) The Interannual Calibration and Global Nighttime Light Fluctuation Assessment Based on Pixel-Level Linear Regression Analysis. Remote Sensing, 11, Article No. 2185. [Google Scholar] [CrossRef] 
[21] Li, X., Li, D.R., Xu, H.M. and Wu, C.Q. (2017) Intercalibration between DMSP/OLS and VIIRS Night-Time Light Images to Evaluate City Light Dynamics of Syria’s Major Human Settlement during Syrian Civil War. International Journal of Remote Sensing, 38, 5934-5951. [Google Scholar] [CrossRef] 
[22] 陈晋, 卓莉, 史培军, 一之濑俊明. 基于DMSP/OLS数据的中国城市化过程研究——反映区域城市化水平的灯光指数的构建[J]. 遥感学报, 2003, 7(3): 168-175+241.
[23] 郑子豪, 吴志峰, 陈颖彪, 杨智威, Francesco, M. 基于Google Earth Engine的长三角城市群生态环境变化与城市化特征分析[J]. 生态学报, 2021, 41(2): 717-729. [Google Scholar] [CrossRef] 
[24] Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R. (2017) Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Remote Sensing of Envi-ronment, 202, 18-27. [Google Scholar] [CrossRef] 

为你推荐