基于夜间灯光与LUC数据的川渝地区人口空间化研究

doi:10.12082/dqxxkx.2018.170224

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (1): 68-78.doi: 10.12082/dqxxkx.2018.170224

• 地理空间分析综合应用 • 上一篇下一篇

基于夜间灯光与LUC数据的川渝地区人口空间化研究

胡云锋^1,^*(), 赵冠华^1,², 张千力^1,²

1. 中国科学院地理科学与资源研究所,北京 100101
2. 中国科学院大学,北京 100049

收稿日期:2017-04-30 修回日期:2017-08-25 出版日期:2018-01-20 发布日期:2018-01-20
作者简介:
*作者简介：胡云锋（1974-）,男,江西赣州人,博士,副研究员,主要从事遥感监测与区域可持续发展评价研究。E-mail: huyf@lreis.ac.cn
基金资助:
国家重点研发计划项目（2016YFB0501502、2016YFC0503701）;高分专项（00-Y30B14-9001-14/16）

Spatial Distribution of Population Data Based on Nighttime Light and LUC Data in the Sichuan-Chongqing Region

HU Yunfeng^1,^*(), ZHAO Guanhua^1,², Zhang QianLi^1,²

1. Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2017-04-30 Revised:2017-08-25 Online:2018-01-20 Published:2018-01-20
Contact: HU Yunfeng
Supported by:
National Key Research and Development Program of China, No.2016YFB0501502, 2016YFC0503701;Key Project of High Resolution Earth Observation System, No.00-Y30B14-9001-14/16.

摘要/Abstract

摘要：

高精度的人口空间分布数据是开展小尺度人口活动变化规律研究的关键数据。夜间遥感影像对于反映人类社会活动具有独特的能力,因而被广泛的应用于社会经济领域的空间数据挖掘。本研究以DMSP/OLS夜间灯光数据、NPP/VIIRS夜间灯光数据、常住人口统计数据、土地利用数据为数据源,在县级尺度上建立逐步回归模型,构建川渝地区人口空间分布数据;并随机抽取研究区内500个乡镇,以常住人口统计数据为真实数据,对人口空间化结果进行精度检验。结果表明：① 2种夜间灯光数据与人口均有较高的相关性,相关系数均在0.76以上,NPP/VIIRS夜间灯光数据与人口的相关性要高于DMSP/OLS,且受拟合模型的影响不大。② 与人口相关性较高的土地利用类型有多种,耕地、林地对人口空间分布也有影响,在建模时不应只考虑建成区。③ 在2种夜间灯光数据分别与土地利用与土地覆被数据（Land Use/ Land Cover, LUC）结合时,使用DMSP/OLS夜间灯光数据和NPP/VIIRS夜间灯光数据得到的逐步回归模型的复相关系数R²分别为0.796、0.817,模型拟合率较高,而基于NPP/VIIRS夜间灯光数据得到的人口空间化结果分辨率（500 m）相比DMSP/OLS提高了一倍（1 km）,中心城区与周边城区人口密度变化更加自然,更符合实际人口分布情况。④ 与LUC数据结合时,使用NPP/VIIRS夜间灯光数据得到的人口空间化结果精度要高于DMSP/OLS夜间灯光数据,表明NPP/VIIRS夜间灯光数据相比DMSP/OLS更适用于人口数据空间化研究。

关键词: DMSP/OLS, NPP/VIIRS, 人口模拟, 逐步回归, LUC

Abstract:

Spatial distribution data of the population with high-precision is the important data to study the law of the variation of population activity at small-scale. Remote sensing images of nighttime light have the unique ability of reflecting human social activities. Thus, they were widely used in spatial data mining of the socio-economic field. In this study, DMSP/OLS nighttime light data, NPP/VIIRS nighttime light data, resident population data and land use data were used as data sources. Then, we used these data to build the stepwise regression at county scale. The spatial distribution data of population in Sichuan and Chongqing area were established based on the stepwise regression model. Finally, we took the resident demographic data of the randomly selected 500 townships as the practical data to assess the accuracy of spatial distribution data of the population. The analysis shows that: (1) both of the two nighttime light data have high correlation with the population. The correlation coefficients are both above 0.76. The correlation of NPP / VIIRS night light data and population is higher than DMSP / OLS. The fitting model does not change the results. (2) There are many types of land use that are highly relevant to population. Farmland and woodland can also affect the spatial distribution of population. Thus, built area should not be considered as the only type of land use for building the population distribution model. (3) When the two nighttime lights were combined with LUC( Land Use/ Land Cover), the complex correlation coefficient (R²) of the stepwise regression model using DMSP / OLS nighttime light data and NPP / VIIRS night light data is 0.796 and 0.817 respectively, and the model fitting rate is higher. Compared with the results based on DMSP/OLS (1 km), the spatial resolution of population based on NPP/VIIRS nighttime light data increases to 500 m. The change of population density is more natural from the central city to the surrounding urban area, and the population distribution is more real. (4) When combined with LUC data, the results obtained with NPP / VIIRS nighttime light data were more accurate than DMSP / OLS nighttime light data, indicating that NPP / VIIRS nighttime light data is more suitable for the research of spatial distribution of population than DMSP / OLS.

Key words: DMSP/OLS, NPP/VIIRS, population simulation, stepwise Regression, LUC

胡云锋, 赵冠华, 张千力. 基于夜间灯光与LUC数据的川渝地区人口空间化研究[J]. 地球信息科学学报, 2018, 20(1): 68-78.DOI:10.12082/dqxxkx.2018.170224

HU Yunfeng,ZHAO Guanhua,Zhang QianLi. Spatial Distribution of Population Data Based on Nighttime Light and LUC Data in the Sichuan-Chongqing Region[J]. Journal of Geo-information Science, 2018, 20(1): 68-78.DOI:10.12082/dqxxkx.2018.170224

图/表 13

图1

图2

图 3

表 1

图 4

图5

图 6

表 2

表3

图 7

表 4

表 5

图 8

参考文献 24

[1]	林丽洁,林广发,颜小霞,等.人口统计数据空间化模型综述[J].亚热带资源与环境学报,2010,5(4):10-16. doi: 10.3969/j.issn.1673-7105.2010.04.002
	[Lin L J, Lin G F, Yan X X, et al.A review on spatialization models of census data[J]. Journal of Subtropical Resources and Environmen, 2010,5(4):10-16. ] doi: 10.3969/j.issn.1673-7105.2010.04.002
[2]	Alahmadi M, Atkinson P, Martin D.Estimating the spatial distribution of the population of Riyadh, Saudi Arabia using remotely sensed built land cover and height data[J]. Computers, Environment and Urban Systems, 2013,41:167-176. doi: 10.1016/j.compenvurbsys.2013.06.002
[3]	叶宇,刘高焕,冯险峰.人口数据空间化表达与应用[J].地球信息科学,2006,8(2):59-65.
	[Ye Y, Liu G H, Feng X F.Presentation and application of spatial distribution of the population data[J]. Geo-information Science, 2006,8(2):59-65. ]
[4]	胡云锋,王倩倩,刘越,等.国家尺度社会经济数据格网化原理和方法[J].地球信息科学学报,2011,13(5):573-578. doi: 10.3724/SP.J.1047.2011.00573
	[Hu Y F, Wang Q Q, Liu Y, et al.Principles and methods of the social and economic grid database at national scale[J]. Journal of Geo-information Science,2011,13(5):573-578. ] doi: 10.3724/SP.J.1047.2011.00573
[5]	柏中强,王卷乐,杨飞.人口数据空间化研究综述[J].地理科学进展,2013,32(11):1692-1702. doi: 10.11820/dlkxjz.2013.11.012
	[Bai Z Q, Wang J L, Yang F.Research progress in spatialization of population data[J]. Progress in Geography, 2013,32(11):1692-1702. ] doi: 10.11820/dlkxjz.2013.11.012
[6]	Azar D, Engstrom R, Graesser J, et al.Generation of fine-scale population layers using multi-resolution satellite imagery and geospatial data[J]. Remote Sensing of Environment, 2013,130:219-232. doi: 10.1016/j.rse.2012.11.022
[7]	Lung T, Lübker T, Ngochoch J K, et al.Human population distribution modelling at regional level using very high resolution satellite imagery[J]. Applied Geography, 2013,41:36-45. doi: 10.1016/j.apgeog.2013.03.002
[8]	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.
	[Chai Z W, Wang S L, Qiao J G.Township GDP estimation of the Pearl River Delta based on the NPP-VIIRS night-time satellite data[J]. Tropical Geography, 2015,35(3):379-385. ]
[9]	韩向娣,周艺,王世新,等.夜间灯光遥感数据的GDP空间化处理方法[J].地球信息科学学报,2012,14(1):128-136.
	[Han X D, Zhou Y, Wang S X, et al.GDP spatialization in China based on nighttime imagery[J]. Journal of Geo-information Science, 2012,14(1):128-136. ]
[10]	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):141-148,168.
	[Yang Y, Huang Q X, Zhang L L.The spatial-temporal measurement on the land urbanization level using DMSP/OLS nighttime light data: A case study of Bohai rim[J]. Economic Geography, 2015,35(2):141-148,168. ]
[11]	Yang X, Yue W, Gao D.Spatial improvement of human population distribution based on multi-sensor remote-sensing data: An input for exposure assessment[J]. International Journal of Remote Sensing, 2013,34(15):5569-5583. doi: 10.1080/01431161.2013.792970
[12]	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. doi: 10.1016/j.rse.2005.02.002
[13]	Sutton P, Roberts D, Elvidge C, et al.A comparison of nighttime satellite imagery and population density for the Continental United Sates[J]. American Society for Photogrammetry and Remote Sensing, 1997,63(11):1303-1313. doi: 10.1007/s001900050141
[14]	Zhang Q, Seto K C.Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data[J]. Remote Sensing of Environment, 2011,115(9):2320-2329. doi: 10.1016/j.rse.2011.04.032
[15]	Baugh K, Hsu F-C, Elvidge C D, et al.Nighttime lights compositing using the VIIRS day-night band: Preliminary results[J]. Proceedings of the Asia-Pacific Advanced Network, 2013,35:70-86. doi: 10.7125/APAN.35.8
[16]	Dai Z, Hu Y, Zhao G.The suitability of different nighttime light data for GDP estimation at different spatial scales and regional levels[J]. Sustainability, 2017,9(2):305-320. doi: 10.3390/su9020305
[17]	Li X, Xu H, Chen X, et al.Potential of NPP-VIIRS nighttime light imagery for modeling the regional economy of China[J]. Remote Sensing, 2013,5(6):3057-3081. doi: 10.3390/rs5063057
[18]	Shi K, Yu B, Huang Y, et al.Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: A comparison with DMSP-OLS data[J]. Remote Sensing, 2014,6(2):1705-1724. doi: 10.3390/rs6021705
[19]	李强,苏奋振,王雯玥.基于VIIRS数据的油气平台提取技术研究[J].地球信息科学学报,2017,19(3):398-406. doi: 10.3724/SP.J.1047.2017.00398
	[Li Q, Su F Z, Wang W Y.Research on oil and gas platform extraction technology based on VIIRS data[J]. Journal of Geo-information Science, 2017,19(3):398-406. ] doi: 10.3724/SP.J.1047.2017.00398
[20]	高义,王辉,王培涛,等.基于人口普查与多源夜间灯光数据的海岸带人口空间化分析[J].资源科学,2013,35(12):2517-2523.
	[Gao Y, Wang H, Wang P T, et al.Analysis on spatial processing of the population for Chinese coastal zones based on census and multiple night light data[J]. Resources Science, 2013,35(12):2517-2523. ]
[21]	杨继瑞,李月起,汪锐.川渝地区:“一带一路”和长江经济带的战略支点[J].经济体制改革,2015(4):58-64.
	[Yang J R, Li Y Q, Wang R.Sichuan and Chongqing region: Strategic fulcrum of the Belt and Road initiatives and Yangtze River economic zone[J]. Reform of Economic System,2015(4):58-64. ]
[22]	王炳赟,范广洲,董一平,等.川渝地区气候与物候的变化特征分析[J].地理科学,2011(6):674-681.
	[Wang B Y, Fan G Z, Dong Y P, et al.Analysis on the variations of climate and phenology in Sichuan and Chongqing region[J]. Scientia Geographica Sinica, 2011(6):674-681. ]
[23]	Liu J Y, Kuang W H, Zhang Z X, et al.Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s[J]. Journal of Geographical Sciences, 2014,24(2):195-210. doi: 10.1007/s11442-014-1082-6
[24]	梁友嘉,徐中民.基于LUCC和夜间灯光辐射数据的张掖市甘州区人口空间分布建模[J].冰川冻土,2012,34(4):999-1006.
	[Liang Y J, Xu Z M.Modeling the spatial distribution of population based on night light radiation data and LUCC: A case study in Ganzhou District, Zhangye municipality[J]. Journal of Glaciology and Geocryology, 2012,34(4):999-1006. ]

数据类型	年份	数据来源	比例尺/分辨率
行政区划边界	2012	国家基础地理信息中心	1:25万
人口统计资料	2013	统计年鉴以各地区统计公报	县、乡镇
LUC	2013	中科院资源环境科学数据中心	1:10万/1 km
DMSP/OLS	2013	美国国家地球物理数据中心	1 km（采样后）
NPP/VIIRS	2013	美国国家地球物理数据中心	0.5 km（采样后）

土地利用类型		DMSP/OLS			NPP/VIIRS
土地利用类型		系数		Sig.	系数	Sig.
耕地	NU	392.399	0.000		95.736	0.000
	NL	442.249	0.000		163.979	0.000
林地	LE	29.270	0.024		85.934	0.028
城镇及建设用地	LE	106.833	0.000		71.042	0.000
常数	Con	39 848.898	0.050		45 683.36	0.017
	R²	0.796			0.817

误差类型	DMSP/OLS	NPP/VIIRS
MAE	10 851	10 450
MRE/%	46.3	44.62
RMSE	637 518	587 170

夜间灯光	严重低估	一般低估	较准确估计	一般高估	严重高估
夜间灯光	(-100%,-50%]	(-50%,-20%]	(-20%,20%]	(20%,50%]	(50%,100%]
DMSP/OLS	35	129	166	94	76
NPP/VIIRS	28	116	188	83	85

基于夜间灯光与LUC数据的川渝地区人口空间化研究

Spatial Distribution of Population Data Based on Nighttime Light and LUC Data in the Sichuan-Chongqing Region

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 13

参考文献 24

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	任鹏, 彭建东, 杨红, 王安琪, 代琦. 武汉市轨道交通站点周边地区职住平衡与建成环境的关系研究[J]. 地球信息科学学报, 2021, 23(7): 1231-1245.
[2]	许正森, 徐永明. 整合DMSP/OLS和NPP/VIIRS夜间灯光遥感数据的长江三角洲城市格局时空演化研究[J]. 地球信息科学学报, 2021, 23(5): 837-849.
[3]	秦佳睿, 盛业华, 王燕锋, 何育枫. 南京市住宅价格影响因素分析[J]. 地球信息科学学报, 2021, 23(5): 882-890.
[4]	朱守杰, 杜世宏, 李军, 商硕硕, 杜守基. 融合多源空间数据的城镇人口分布估算[J]. 地球信息科学学报, 2020, 22(8): 1607-1616.
[5]	张佰发, 苗长虹, 宋雅宁, 王娟娟. 一种DMSP/OLS稳定夜间灯光影像中国区域的校正方法[J]. 地球信息科学学报, 2020, 22(8): 1679-1691.
[6]	朱惠, 张清凌, 张珊. 1992—2017年基于夜光遥感的中亚社会经济发展时空特征分析[J]. 地球信息科学学报, 2020, 22(7): 1449-1462.
[7]	赵忠旭, 张燕杰, 潘影, 武俊喜, 李振男. 夜间灯光数据支持下西藏人类活动强度变化对生态系统调节服务的影响[J]. 地球信息科学学报, 2020, 22(7): 1544-1554.
[8]	姚可桢, 岳书平. 网络大数据下的中国现代食甜习惯空间分布特征及其影响因素研究[J]. 地球信息科学学报, 2020, 22(6): 1202-1215.
[9]	刘纪远, 张增祥, 张树文, 颜长珍, 吴世新, 李仁东, 匡文慧, 史文娇, 黄麟, 宁佳, 董金玮. 中国土地利用变化遥感研究的回顾与展望——基于陈述彭学术思想的引领[J]. 地球信息科学学报, 2020, 22(4): 680-687.
[10]	曹子阳, 吴志峰, 米素娟, 阳柯. DMSP/OLS稳定夜间灯光影像在中国的分类校正方法[J]. 地球信息科学学报, 2020, 22(2): 246-257.
[11]	许文鑫, 梁娟珠. 基于复合指数模型的DMSP/OLS夜间灯光影像饱和校正方法[J]. 地球信息科学学报, 2020, 22(11): 2227-2237.
[12]	林中立, 徐涵秋, 黄绍霖. 基于DMSP/OLS夜间灯光影像的中国东部沿海地区城市扩展动态监测[J]. 地球信息科学学报, 2019, 21(7): 1074-1085.
[13]	梁立, 李鑫杨, 刘庆生, 刘高焕, 黄翀, 李贺. 基于多源遥感与地形信息的北极城镇用地信息提取[J]. 地球信息科学学报, 2019, 21(6): 969-982.
[14]	卢秀, 李佳, 段平, 李晨, 王金亮. 基于夜间灯光和土地利用数据的云南沿边地区GDP空间差异性分析[J]. 地球信息科学学报, 2019, 21(3): 455-466.
[15]	李小敏, 郑新奇, 袁涛. DMSP/OLS夜间灯光数据研究成果知识图谱分析[J]. 地球信息科学学报, 2018, 20(3): 351-359.