DMSP/OLS夜间灯光影像中国区域的校正及应用

doi:10.3724/SP.J.1047.2015.01092

地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (9): 1092-1102.doi: 10.3724/SP.J.1047.2015.01092

DMSP/OLS夜间灯光影像中国区域的校正及应用

曹子阳^1,²(), 吴志峰^3,^*(), 匡耀求¹, 黄宁生¹

1. 中国科学院广州地球化学研究所边缘海地质重点实验室,广州 510640
2. 中国科学院大学,北京 100049
3. 广州大学地理科学学院,广州 510006

收稿日期:2014-12-08 修回日期:2015-01-04 出版日期:2015-09-10 发布日期:2015-09-07
通讯作者: 吴志峰 E-mail:eationcao@163.com;gzuwzf@163.com
作者简介:
作者简介：曹子阳(1988-),男,博士生,研究方向为区域可持续发展与城市遥感。E-mail: eationcao@163.com
基金资助:
国家自然科学基金项目“环珠江口区域城市扩张及其环境生态效应分析与模拟”(41171446);广州市属高校“羊城学者”科研项目“城市不透水面扩张及其环境效应遥感监测”(12A002G)

Correction of DMSP/OLS Night-time Light Images and Its Application in China

CAO Ziyang^1,²(), WU Zhifeng^3,^*(), KUANG Yaoqiu¹, HUANG Ningsheng¹

1. Key Laboratory of Marginal Sea Geology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China

Received:2014-12-08 Revised:2015-01-04 Online:2015-09-10 Published:2015-09-07
Contact: WU Zhifeng E-mail:eationcao@163.com;gzuwzf@163.com
About author:
*The author: SHEN Jingwei, E-mail:jingweigis@163.com

摘要/Abstract

摘要：

美国国防气象卫星搭载的业务型线扫描传感器（DMSP/OLS）获取的夜间灯光影像,可客观地反映人类开发建设活动强度,其广泛应用于城市遥感的多个领域。但该数据缺少星上的辐射校正,下载的原始影像数据集不能直接用于研究,需进行区域校正。长时间序列的DMSP/OLS夜间灯光影像数据集主要存在2个问题需在校正过程中解决：（1）原始影像数据集中的影像是非连续性的;（2）数据集中的每一期影像都存在着像元DN值饱和的现象。针对这2个问题,本文提出了一种不变目标区域法的影像校正方法,对提取出来的每一期中国区域的夜间灯光影像进行了校正,该校正方法包括相互校正、饱和校正和影像间的连续性校正。最后,为了检验校正方法的合理性与可靠性,本文将校正前后中国夜间灯光影像与GDP和电力消耗值,分别进行回归分析评价表明,校正后的影像更客观合理地反映区域经济发展的差异。

关键词: DMSP/OLS, 夜间灯光影像, 中国, 校正方法, GDP

Abstract:

DMSP/OLS (Defense Meteorological Satellite Program Operational Linescan System) night-time light images can objectively reflect the intensity of human activities; therefore they were widely used in a variety of fields for urban remote sensing. However, the raw night-time dataset cannot be used directly in these researches due to the lack of inflight calibration, thus it needs to be further corrected. There are two problems existed in the long-time series of DMSP/OLS night-time light image dataset that should be addressed in the image correction procedure. First, every image in the raw night-time light image dataset cannot directly compare with each other due to the issue of discontinuity; second, there is a pixel saturation phenomenon existed in every image of the raw night-time light image dataset. In order to solve these problems, a method based on invariant region was proposed. This method included the intercalibration, the saturation correction, and the continuity correction procedures among all the images from the raw images dataset. All the night-time light images of China, which were extracted from the raw images dataset, were corrected using this method. Finally, this correction method was evaluated by analyzing the relationships between the night-time light images and the corresponding gross domestic product (GDP) data and the corresponding electric power consumption data respectively. Through the analysis toward the evaluated results, two main conclusions were acquired. One was that this method had solved the problem of discontinuity in the raw image dataset; the other one was that this method could reduce the pixel saturation phenomenon that existed in every images of the raw night-time light image dataset. However, this method has not completely solved the problem of pixel saturation. How to perfectly solve this problem is the core issue for future research on night-time light data application.

Key words: DMSP/OLS, night-time light images, China, correction method, GDP

曹子阳, 吴志峰, 匡耀求, 黄宁生. DMSP/OLS夜间灯光影像中国区域的校正及应用[J]. 地球信息科学学报, 2015, 17(9): 1092-1102.DOI:10.3724/SP.J.1047.2015.01092

CAO Ziyang,WU Zhifeng,KUANG Yaoqiu,HUANG Ningsheng. Correction of DMSP/OLS Night-time Light Images and Its Application in China[J]. Journal of Geo-information Science, 2015, 17(9): 1092-1102.DOI:10.3724/SP.J.1047.2015.01092

图/表 9

图1

图2

图3

表1

图4

图5

表2

表3

表4

参考文献 30

[1]	Elvidge C D, Baugh K E, Kihn E A, et al.Mapping city lights with nighttime data from the DMSP operational linescan system[J]. Photogrammetric Engineering and Remote Sensing, 1997,63(6):727-734.
[2]	邹进贵,陈艳华,田径,等.基于ArcGIS的DMSP/OLS夜间灯光影像校正模型的构建[J].测绘地理信息,2014,39(4):33-37.
[3]	Lo C P.Urban indicators of China from radiance-calibrated digital DMSP-OLS nighttime images[J]. Annals of the Association of American Geographers, 2002,92(2):225-240.
[4]	卓莉,史培军,陈晋,等.20世纪90年代中国城市时空变化特征——基于灯光指数CNLI方法的探讨[J].地理学报,2003,58(6):893-902.
[5]	Ma T, Zhou C, Pei T, et al.Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities[J]. Remote Sensing of Environment, 2012,124: 99-107.
[6]	Elvidge C D, Suttonb P C, Ghoshc T, et al.A global poverty map derived from satellite data[J]. Computers & Geosciences, 2009,35(8):1652-1660.
[7]	Wang W, Cheng H, Zhang L.Poverty assessment using DMSP/OLS night-time light satellite imagery at a provincial scale in China[J]. Advances in Space Research, 2012,49(8):1253-1264.
[8]	Wu J, Wang Z, Li W, et al.Exploring factors affecting the relationship between light consumption and GDP based on DMSP/OLS nighttime satellite imagery[J]. Remote Sensing of Environment, 2013,134:111-119.
[9]	Lo C P.Modeling the population of China using DMSP operational linescan system nighttime data[J]. Photogrammetric Engineering and Remote Sensing, 2001,67(9):1037-1047.
[10]	卓莉,陈晋,史培军,等.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.
[11]	Doll C N H, Pachauri S. Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery[J]. Energy Policy, 2010,38(10):5661-5670.
[12]	He C, Ma Q, Li T, et al.Spatiotemporal dynamics of electric power consumption in Chinese Mainland from 1995 to 2008 modeled using DMSP/OLS stable nighttime lights data[J]. Journal of Geographical Sciences, 2012,22(1): 25-136.
[13]	Cao X, Wang J, Chen J, et al.Spatialization of electricity consumption of China using saturation-corrected DMSP-OLS data[J]. International Journal of Applied Earth Observation and Geoinformation, 2014,28:193-200.
[14]	Su Y, Chen X, Li Y, et al.China's 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines[J]. Renewable & Sustainable Energy Reviews, 2014,35:231-243.
[15]	Chalkias C, Petrakis M, Psiloglou B, et al.Modelling of light pollution in suburban areas using remotely sensed imagery and GIS[J]. Journal of Environmental Management, 2006,79(1):57-63.
[16]	Elvidge C D, Hobson V R, Baugh K E, et al.DMSP-OLS estimation of tropical forest area impacted by surface fires in Roraima, Brazil: 1995 versus 1998[J]. International Journal of Remote Sensing, 2001,22(14):2661-2673.
[17]	张鹏强,余旭初,刘智,等.多时相遥感图像相对辐射校正[J].遥感学报,2006,10(3):339-344.
[18]	Elvidge C D, Ziskin D, Baugh K E, et al.A fifteen year record of global natural gas flaring derived from satellite data[J]. Energies, 2009,2(3):595-622.
[19]	Liu Z, He C, Zhang Q, et al.Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008[J]. Landscape and Urban Planning, 2012,106(1):62-72.
[20]	Letu H, Hara M, Yagi H,et al.Estimating the energy consumption with nighttime city light from the DMSP/OLS imagery[J]. Joint Urban Remote Sensing Event, 2009(1-3):1364-1370.
[21]	Raupach M R, Rayner P J, Paget M.Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions[J]. Energy Policy, 2010,38(9):4756-4764.
[22]	Wu J, He S, Peng J, et al.Intercalibration of DMSP-OLS night-time light data by the invariant region method[J]. International Journal of Remote Sensing, 2013,34(20):7356-7368.
[23]	Hall F G, Strebel D E, Nickeson J E, et al.Radiometric rectification-toward a common radiometric response among multidate, multisensor images[J]. Remote Sensing of Environment, 1991,35(1):11-27.
[24]	Lenney M P, Woodcock C E, Collins J B, et al.The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from Landsat TM[J]. Remote Sensing of Environment, 1996,56(1):8-20.
[25]	何春阳,史培军,李景刚,等.基于DMSP/OLS夜间灯光数据和统计数据的中国大陆20世纪90年代城市化空间过程重建研究[J].科学通报,2006,51(7):856-861.
[26]	Elvidge C D, Baugh K E, Kihn E A, et al.Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption[J]. International Journal of Remote Sensing, 1997,18(6):1373-1379.
[27]	Elvidge C D, Imhoff M L, Baughc K E, et al.Night-time lights of the world: 1994-1995[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2001,56(2):81-99.
[28]	Forbes D J.Multi-scale analysis of the relationship between economic statistics and DMSP-OLS night light images[J]. Giscience & Remote Sensing, 2013,50(5): 483-499.
[29]	王琪,袁涛,郑新奇.基于夜间灯光数据的中国省域GDP总量分析[J].城市发展研究,2013,20(7):44-48.
[30]	Silvana A, Gilberto C, Antonio M, et al.Estimating population and energy consumption in Brazilian Amazonia using DMSP night-time satellite data[J]. Computers, Environment and Urban Systems, 2005,29:179-195.

卫星序号	年份	a	b	R²
F10	1992	0.6044	1.3244	0.8662
	1993	0.7039	1.3265	0.9112
	1994	0.7654	1.2925	0.9265
F12	1994	0.6443	1.3133	0.9156
	1995	0.8080	1.2227	0.9179
	1996	1.0288	1.1393	0.8972
	1997	0.7561	1.2646	0.8878
	1998	0.6846	1.2333	0.8880
	1999	0.7741	1.2315	0.8645
F14	1997	1.3708	1.1824	0.8579
	1998	0.9664	1.2229	0.8663
	1999	1.0912	1.2263	0.9098
	2000	0.9913	1.1753	0.9308
	2001	0.8686	1.2266	0.9351
	2002	0.9894	1.1583	0.9153
	2003	0.9003	1.2019	0.9290
F15	2000	0.8093	1.1830	0.8592
	2001	0.6655	1.2646	0.8619
	2002	0.5923	1.2795	0.9121
	2003	1.0439	1.1822	0.9111
	2004	1.1450	1.0847	0.9281
	2005	1.3548	1.0228	0.8498
	2006	1.2481	1.0738	0.9545
	2007	1.1421	1.1297	0.9384
F16	2004	0.7415	1.1576	0.9413
	2005	0.9048	1.1776	0.9485
	2006	0.8296	1.1883	0.9647
	2007	0.7314	1.2132	0.9369
	2008	0.7927	1.1487	0.9186
	2009	0.6051	1.1525	0.8923
F18	2010	0.3427	1.2188	0.8387
	2011	0.7035	1.0872	0.8545
	2012	0.4821	1.1866	0.8849

年份	校正前						校正后
年份	F10		F12	F14	F15	F16	F18
1992	0.7096						0.7303
1993	0.5953						0.6944
1994	0.5979	0.6664					0.6546
1995		0.6781					0.6850
1996		0.6903					0.7272
1997		0.7129	0.7201				0.7363
1998		0.7208	0.7265				0.7388
1999		0.7545	0.7641				0.7504
2000			0.7100	0.7469			0.7595
2001			0.7372	0.7373			0.7648
2002			0.8086	0.7806			0.7898
2003			0.8383	0.8482			0.8204
2004				0.8338	0.7822		0.8238
2005				0.7877	0.7636		0.8399
2006				0.8110	0.7869		0.8446
2007				0.8170	0.7908		0.8453
2008					0.7784		0.8480
2009					0.6904		0.8309
2010						0.6964	0.8234
2011						0.7755	0.8160
2012						0.7293	0.7911

年份	校正前						校正后
年份	F10		F12	F14	F15	F16	F18
1992	0.6588						0.7094
1993	0.6521						0.7069
1994	0.6577	0.6572					0.7046
1995		0.6337					0.6813
1996		0.6266					0.6685
1997		0.6114	0.6543				0.6526
1998		0.6180	0.6632				0.6066
1999		0.5717	0.6081				0.5955
2000			0.6137	0.5877			0.6061
2001			0.5429	0.5396			0.5613
2002			0.4540	0.4235			0.4863
2003			0.4452	0.4683			0.4799
2004				0.4354	0.4076		0.4757
2005				0.3766	0.3729		0.4299
2006				0.3590	0.3393		0.4103
2007				0.3406	0.3130		0.3914
2008					0.3081		0.3866
2009					0.3147		0.3875
2010						0.2657	0.3877
2011						0.2742	0.3846
2012						0.2732	0.3955

年份	校正前						校正后
年份	F10		F12	F14	F15	F16	F18
1992	-						-
1993	-						-
1994	-	-					-
1995		0.7578					0.7476
1996		-					-
1997		-	-				-
1998		-	-				-
1999		0.7858	0.8171				0.8207
2000			0.7823	0.8135			0.8549
2001			0.8231	0.8217			0.8648
2002			0.8447	0.8323			0.8754
2003			0.8608	0.8767			0.8791
2004				0.8681	0.8030		0.8834
2005				0.8272	0.8009		0.8819
2006				0.8512	0.8402		0.8911
2007				0.8577	0.8505		0.8985
2008					0.8427		0.8980
2009					0.7458		0.8902
2010						0.7561	0.8789
2011						0.8424	0.8759
2012						0.8279	0.8704

DMSP/OLS夜间灯光影像中国区域的校正及应用

Correction of DMSP/OLS Night-time Light Images and Its Application in China

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 30

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	薛浩男, 张雪英, 吴明光, 曹天阳. 基于新闻数据的新冠疫情事件下“全球-中国”国际关系变化分析方法[J]. 地球信息科学学报, 2021, 23(2): 351-363.
[2]	张小东, 韩昊英, 陈宇. 2003—2018年中国地级城市土地出让交易状况及时空动态特征[J]. 地球信息科学学报, 2020, 22(9): 1823-1836.
[3]	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.
[4]	闫庆武, 厉飞, 李玲. 基于2种夜间灯光影像亮度修正指数的城市建成区提取研究[J]. 地球信息科学学报, 2020, 22(8): 1714-1724.
[5]	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.
[6]	宋荣, 胡业翠. 中国城市土地整治综合潜力分区与提升路径[J]. 地球信息科学学报, 2020, 22(7): 1522-1531.
[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]	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.
[9]	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.
[10]	张永树, 杨振凯, 訾璐, 曹一冰, 余航. 中国艾滋病空间格局和时空演化分析[J]. 地球信息科学学报, 2020, 22(2): 198-206.
[11]	杜培培, 侯西勇. 基于多源数据的中国海岸带地区人口空间化模拟[J]. 地球信息科学学报, 2020, 22(2): 207-217.
[12]	李文梁, 汪驰升, 朱武. 中国大陆地区TanDEM-X 90 m DEM误差空间分布特征[J]. 地球信息科学学报, 2020, 22(12): 2277-2288.
[13]	刘艳霞, 冯莉, 田慧慧, 阳少奇. 中国气候舒适度时空分布特征分析[J]. 地球信息科学学报, 2020, 22(12): 2338-2347.
[14]	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.
[15]	杜震洪, 吴森森, 王中一, 汪愿愿, 张丰, 刘仁义. 基于地理神经网络加权回归的中国PM_2.5浓度空间分布估算方法[J]. 地球信息科学学报, 2020, 22(1): 122-135.