[1]ZHONG Yang,LIN Aiwen.Spatial Pattern Evolution and Optimization of Urban Agglomerations in the Middle Reaches of the Yangtze River-A Method Based on DMSP/OLS Night Light Data[J].Research of Soil and Water Conservation,2018,25(06):298-305.
Copy
Spatial Pattern Evolution and Optimization of Urban Agglomerations in the Middle Reaches of the Yangtze River-A Method Based on DMSP/OLS Night Light Data
Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
25
Number of periods:
2018 06
Page number:
298-305
Column:
Public date:
2018-10-26
- Title:
- Spatial Pattern Evolution and Optimization of Urban Agglomerations in the Middle Reaches of the Yangtze River-A Method Based on DMSP/OLS Night Light Data
- Keywords:
- night light data; urban agglomeration in the middle reaches of Yangtze River; comparative method based on auxiliary data; spatial pattern
- CLC:
- F291;P237
- DOI:
- -
- Abstract:
- The Landsat satellite image data and cluster analysis methods were used to correct the nighttime lighting data, and then the threshold value of the urban built-up area was determined using the comparison method based on auxiliary data. The total landscape area and patch were selected. A total of 8 typical landscape pattern indicators, such as density and landscape shape index, were used to calculate 8 landscape pattern indicators reflecting the spatial evolution of urban development in the middle reaches of the Yangtze River during 1992-2013 using the landscape pattern analysis software FRAG-STATS 4.2. The calculation results show that from 1992 to 2013, the urbanization of the urban clusters in the middle reaches of the Yangtze River rapidly expanded, the area of urban built-up areas increased by 7.02 times, the number of patches increased by 2.35 times, and the increase in patch density was also obvious. The complexity of shape gradually increased, and small and medium-sized cities continued to grow. At the same time, there were also a number of emerging small towns. Specifically, the average annual growth rate of the total boundary length and average boundary density were 18.86% and 18.87%, respectively. The largest patch index and the aggregation index presented the decline year by year, but overall the landscape area, the largest plate index, and the average boundary density all showed the characteristics of some fluctuations and disorder.
- References:
-
[1] 方创琳,王振波,马海涛.中国城市群形成发育规律的理论认知与地理学贡献[J].地理学报,2018,73(4):651-665.
[2] 方创琳.城市群空间范围识别标准的研究进展与基本判断[J].城市规划学刊,2009(4):1-6.
[3] 苗长虹,王海江.中国城市群发展态势分析[J].城市发展研究,2005,12(4):11-14.
[4] 赵丹,张京祥.高速铁路影响下的长三角城市群可达性空间格局演变[J].经济地理,2012,21(4):391-398.
[5] 尚正永,张小林,卢晓旭.基于可达性的城市功能用地空间格局演变研究:以江苏省淮安市为例[J].地理科学,2014,32(2):154-162.
[6] 陈伟,常黎丽,彭翀.基于网络化发展的长株潭城市群空间格局及优化策略[J].经济地理,2016,32(2):74-79.
[7] 顾雯娟,欧向军,叶磊.基于要素流的长三角城市群空间布局[J].热带地理,2015,35(6):833-841.
[8] 陈肖飞,张落成,姚士谋.基于新经济地理学的长三角城市群空间格局及发展因素[J].地理科学进展,2015,34(2):229-236.
[9] 柴攀峰,黄中伟.基于协同发展的长三角城市群空间格局研究[J].经济地理,2014,34(6):75-79.
[10] 朱政,郑伯红,贺清云.珠三角城市群空间结构及影响研究[J].经济地理,2011,31(3):404-408.
[11] 冯兴华,钟业喜,李建新,等.长江中游城市群县域城镇化水平空间格局演变及驱动因子分析[J].长江流域资源与环境,2015,24(6):899-908.
[12] 王涛,曾菊新.长江中游城市群城际竞合空间格局分析:基于城市竞争力与空间相互作用的视角[J].热带地理,2014,34(3):390-398.
[13] 朱道才,陆林,晋秀龙.基于引力模型的安徽城市空间格局研究[J].地理科学,2011,31(5):551-556.
[14] 张群生,颜苇.基于引力模型的贵州省城市空间格局研究[J].西南师范大学学报:自然科学版,2015,40(5):101-105.
[15] 黄洁,钟业喜.长江中游城市群铁路客运联系及其空间格局演变[J].世界地理研究,2016,25(2):72-81.
[16] 杨璐璐.中部六省城镇化质量空间格局演变及驱动因素:基于地级及以上城市的分析[J].经济地理,2015,35(1):68-75.
[17] 胡盈,张津,刘转花,等.基于引力模型和城市流的长江中游城市群空间联系研究[J].现代城市研究,2016(1):52-57.
[18] 胡平,伍新木,文余源.基于面板数据SDM的长江中游城市群FDI决定因素分析[J].经济地理,2014,34(1):15-60.
[19] 蒋大亮,孙烨,任航.基于百度指数的长江中游城市群城市网络特征研究[J].长江流域资源与环境,2015,24(10):1654-1664.
[20] 张雅杰,金海,谷兴,等.基于ESDADGWR多变量影响的经济空间格局演化:以长江中游城市群为例[J].经济地理,2015,35(3):28-35.
[21] 王圣云,秦尊文,戴璐,等.长江中游城市集群空间经济联系与网络结构:基于运输成本和网络分析方法[J].经济地理,2013,33(3):64-69.
[22] 马勇,刘军.长江中游城市群产业生态化效率研究[J].经济地理,2015,35(6):124-129.
[23] 卢丽文,张毅,李小帆,等.长江中游城市群发展质量评价研究[J].长江流域资源与环境,2014,23(10):1337-1343.
[24] 周克昊,刘艳芳,谭荣辉,等.长江中游城市群综合发展水平时空分异研究[J].长江流域资源与环境,2014,23(11):1510-1518.
[25] 何胜,唐承丽,周国华.长江中游城市群空间相互作用研究[J].经济地理,2014,34(4):46-53.
[26] 李雪松,孙博文.长江中游城市群区域一体化的测度与比较[J].长江流域资源与环境,2013,22(8):996-1003.
[27] Croft T. Nighttime images of the earth from space[J]. Scientif-Ic American, 1978,239:68-79.
[28] 杨洋,黄庆旭,章立玲.基于DMSP/OLS夜间灯光数据的土地城镇化水平时空测度研究:以环渤海地区为例[J].经济地理,2015,35(2):141-149.
[29] Small C, Pozzi F, Elvidge C D. Spatial analysis of global urban extent from DMSP-OLS night lights[J]. Remote Sensing of Environment, 2005,96(3/4):277-291.
[30] Small C, Elvidge C D, Balk D, et al. Spatial scaling of stable night lights[J]. Remote Sensing of Environment, 2011,115(2):269-280.
[31] 吴健生,刘浩,彭建等.中国城市体系等级结构及其空间格局:基于DMSP/OLS夜间灯光数据的实证[J].地理学报,2014,69(6):759-770.
[32] 杨洋,李雅静,何春阳等.环渤海地区三大城市群城市规模分布动态比较:基于1992-2012年夜间灯光数据的分析和透视[J].经济地理,2016,36(4):59-69.
[33] 王翠平,王豪伟,李春明.基于DMSP/OLS影像的我国主要城市群空间扩张特征分析[J].生态学报,2012,32(3):942-954.
[34] Elvidge C D, Hsu F C, Baugh K E, et al. National trends in satellite-observed lighting[J]. Global Urban Monitoring and Assessment Through Earth Observation, 2014,23:97-118.
[35] 王钊,杨山,刘帅宾.基于DMSP/OLS数据的江苏省城镇人口空间分异研究[J].长江流域资源与环境,2015,24(12):2021-2029.
[36] 柴子为,王帅磊,乔纪纲.基于夜间灯光数据的珠三角地区镇级GDP估算[J].热带地理,2015,35(3):379-385.
[37] 吴健生,牛妍,彭建.基于DMSP/OLS夜间灯光数据的1995-2009年中国地级市能源消费动态[J].地理研究,2014,33(4):625-634.
[38] 潘竟虎,李俊峰.基于夜间灯光影像的中国电力消耗量估算及时空动态[J].地理研究,2016,35(4):627-638.
[39] 郭忻怡,闫庆武,谭晓悦.基于DMSP/OLS与NDVI的江苏省碳排放空间分布模拟[J].世界地理研究,2016,25(4):102-110.
[40] 苏泳娴,陈修治,叶玉瑶,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.
- Similar References:
Memo
-
Last Update:
1900-01-01