一种DMSP/OLS稳定夜间灯光影像中国区域的校正方法

doi:10.12082/dqxxkx.2020.190397

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (8): 1679-1691.doi: 10.12082/dqxxkx.2020.190397

一种DMSP/OLS稳定夜间灯光影像中国区域的校正方法

张佰发¹(), 苗长虹¹^,²^,³^,^*(), 宋雅宁⁴, 王娟娟²

1.河南大学黄河文明省部共建协同创新中心,开封 475000
2.河南大学黄河文明与可持续发展研究中心,开封 475000
3.河南大学环境与规划学院,开封 475000
4.南昌大学经济管理学院,南昌 330031

收稿日期:2019-07-25 修回日期:2019-11-11 出版日期:2020-08-25 发布日期:2020-10-25
作者简介:张佰发（1994— ）,男,河南济源人,硕士生,主要从事人文地理研究。E-mail:157546338@qq.com
基金资助:
国家自然科学基金项目(41430637)

Correction of DMSP/OLS Stable Night Light Images in China

ZHANG Baifa¹(), MIAO Changhong¹^,²^,³^,^*(), SONG Yaning⁴, WANG Juanjuan²

1. Collaborative Innovation Center on Yellow River Civilization, Henan University, Kaifeng 475000, China
2. Key Research of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475000, China
3. The College of Environment and Planning, Henan University, Kaifeng 475000, China
4. School of Economics and Management, Nanchang University, Nanchang 330031, China

Received:2019-07-25 Revised:2019-11-11 Online:2020-08-25 Published:2020-10-25
Contact: MIAO Changhong
Supported by:
National Natural Science Foundation of China(41430637)

摘要/Abstract

摘要：

随着遥感技术的快速发展,国防气象卫星计划作战线扫描系统（DMSP/OLS）夜间灯光图像开始更多的应用于人文经济研究中。由于原始影像存在较多问题,如DN值饱和、年际不连续问题等,因此在使用之前需要对影像进行校正。“传统不变区域法”是应用较为广泛的校正方法,但仍存在部分问题,如未考虑目标区域长时间尺度上的微弱变化以及连续校正前基准年份的选取。本文对传统不变目标区域法进行改良,以黑龙江鹤岗市作为不变目标区域,选取3期辐射定标影像作为参考影像对DMSP/OLS稳定夜间灯光影像进行饱和校正,通过对比各年份影像饱和校正情况,选出最为合理的基准年份,从而对饱和校正后的影像进行连续性校正。为验证影像校正精度,本文从国家像元DN值、省级GDP与电力消费量、地级市GDP和县域GDP 4个层面与对应DN值进行线性回归检验,结果显示经改进方法校正后DMSP/OLS稳定夜间灯光影像TDN与市级GDP的拟合度（R2）平均值为0.85,远大于传统方法校正TDN与市级GDP拟合度（R2）平均值的0.53,且随着时间推移,传统不变目标区域法校正后拟合度逐渐降低至2013年的0.40,而利用改进后方法进行校正的拟合度未出现递减现象,2013年其R2仍为0.88,表明与传统不变区域法相比校正精度明显提高,饱和问题得到较大改善。

关键词: 夜间灯光影像, DMSP/OLS, 传统不变目标区域法, 校正, 拟合, GDP, 电力消耗, 对比检验

Abstract:

With the rapid development of remote sensing technology, the night light image of Defense Meteorological Satellite Program Scanning System (DMSP/OLS) has been more and more applied in the research of humanist economics. Since there are many problems in the original images, such as the saturation of DN value and inter-annual discontinuity, it is necessary to correct those data before using them. "Traditional invariant region method" is a widely-using correction method, but there are still some problems, such as not taking into account the weak changes from the prospect of a long-time scale of the target region and the selection of the reference year in a continuous correction. In this paper, the traditional invariant region method was improved. In order to reduce the impact on correction results, caused by the small changes of the target region in a long time scale, and to improve the saturation of the original images, the stable night light images of DMSP/OLS were saturation corrected with the help of selecting three group data (F12_1999, f14-15_2003 and F16_2006) as reference images. By the comparison between saturation corrected images and reference images, the reasonable reference year, F15_2006 (after the saturation correction), with the minimum error was selected, so as to carry out the continuous correction of the saturation corrected images. To verify the correction precision of images, in this paper, pixel DN value test was carried out at the national level. And linear regression test was carried out in the corresponding TDN value between GDP at the provincial level, electricity consumption at the provincial level, GDP at the prefecture-level and at the county level, at the same time, compared with the other published correction results such as Cao Z Y^[25], results shew that the average fit R2 between the improved DMSP/OLS stable night light images TDN and the city's GDP was 0.85 while the average fit R2 between the results with the traditional invariant region method and with city GDP was only 0.53. As time goes by, the fitting degree of the traditional invariant region method gradually decreased to 0.40 in 2013. However, the fitting degree using the improved correction method did not decrease. In 2013, the R2 was 0.88, which indicated that compared with the traditional invariant region method, the correction accuracy was significantly improved and the saturation problem was greatly improved. At the same time, the fitting degree of TDN and GDP on the county scale was about 0.6, indicating that the night light image data set on the county scale also had certain applicability. However, this approach did not completely solve the problem of pixel saturation. How to solve this problem perfectly is the core in the application research of noctilucent data in the future.

Key words: night light images, DMSP/OLS, traditional invariant region method correction, correction, fitting, GDP, electricity consumption, contrast test

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.DOI:10.12082/dqxxkx.2020.190397

ZHANG Baifa, MIAO Changhong, SONG Yaning, WANG Juanjuan. Correction of DMSP/OLS Stable Night Light Images in China[J]. Journal of Geo-information Science, 2020, 22(8): 1679-1691.DOI:10.12082/dqxxkx.2020.190397

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参考文献 33

[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, 1997,68(6):727-734.
[2]	CROFT T A. Night images of the earth from space[J]. Scientific American, 1978,239(1):86-101.
[3]	李德仁, 李熙. 论夜光遥感数据挖掘[J]. 测绘学报, 2015,44(6):591-601.
	[ Li D R, Li X. An overview on data mining of nighttime light remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 2015,44(6):591-601. ]
[4]	李德仁, 李熙. 夜光遥感技术在人道主义灾难评估中的应用[J]. 自然杂志, 2018,40(3):169-176.
	[ Li D R, Li X. Use of night-time light remote sensing in humanitarian disaster evaluation[J]. Chinese Journal of Nature, 2018,40(3):169-176. ]
[5]	范俊甫, 何惠馨, 胡桃英, 等. 一种叙利亚战争城市破坏程度的评估方法[J]. 测绘科学, 2018,43(9):129-134.
	[ Fan J F, He H X, Hu T Y, et al. An assement method of urban destruction degree in Syria War[J]. Science of Surveying and Mapping, 2018,40(9):129-134. ]
[6]	曹丽琴, 李平湘, 张良培. 基于DMSP/OLS夜间灯光数据的城市人口估算——以湖北省各县市为例[J]. 遥感信息, 2009(1):83-87.
	[ Cao L Q, Li P X, Zhang L P. Urban population estimation based on the DMSP/OLS night-time satellite data: A case of Hubei province[J]. Remote Sensing Information, 2009(1):83-87. ]
[7]	陈乐, 李郇, 姚尧, 等. 人口集聚对中国城市经济增长的影响分析[J]. 地理学报, 2018,73(6):1107-1120.
	[ Chen L, Li X, Yao Y, et al. Effects of population agglomeration on urban economic growth in China[J]. Acta Geographica Sinica, 2018,73(6):1107-1120. ]
[8]	Elvidge C D, Sutton P C, Ghosh T, et al. A global poverty map derived from satellite data[J]. Computers & Geosciences, 2009,35(8):1652-1660.
[9]	王明明, 王卷乐. 基于夜间灯光与土地利用数据的山东省乡镇级人口数据空间化[J]. 地球信息科学学报, 2019,21(5):699-709.
	[ Wang M M, Wang J L. Spatialization of township-level population based on nighttime light and land use data in Shandong province[J]. Journal of Geo-information Science, 2019,21(5):699-709. ]
[10]	潘竟虎, 赵宏宇, 董磊磊. 基于DMSP-OLS数据和可持续生计的中国农村多维贫困空间识别[J]. 生态学报, 2018,38(17):1-13.
	[ Pan J H, Zhao H Y, Dong L L. Spatial identification of multi-dimensional poverty in rural China by using nighttime light and sustainable livelihoods[J]. Acta Ecologica Sinica, 2018,38(17):1-13. ]
[11]	沈丹, 周亮, 王培安. 基于夜间灯光数据的六盘山连片特困区贫困空间分异特征分析[J]. 遥感信息, 2018,33(4):42-48.
	[ Shen D, Zhou L, Wang P A. Poverty identification of contiguous poor zone based on nighttime light data: a case study on Liupan Mountain[J]. Remote Sensing Information, 2018,33(4):42-48. ]
[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.
	[ Lin Z L, Xu H Q, Huang S L. Monitoring of the urban expansion dynamics in China's east coast using DMSP/OLS nighttime light imagery[J]. Journal of Geo-information Science, 2019,21(7):1074-1085. ]
[13]	陈昕, 彭建, 刘焱序, 等. 基于DMSP/OLS夜间灯光数据的京津冀地区城市空间扩展与空间关联测度[J]. 地理研究, 2018,37(5):898-909.
	[ Chen X, Peng J, Liu Y X, et al. Measuring spatial expansion and correlations of cities in Beijing-Tianjin-Hebei urban agglomeration using DMSP/OLS nighttime light data[J]. Geographical Research, 2018,37(5):898-909. ]
[14]	陈晋, 卓莉, 史培军, 等. 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.
	[ Chen J, Zhuo L, Shi P J, et al. The study on urbanization process in China based on DMSP/OLS data: Development of a light index for urbanization level estimation[J]. Journal of Remote Sensing, 2003,7(3):168-175. ]
[15]	高亚红, 顾羊羊, 乔旭宁, 等. 基于夜间灯光数据的南京城镇用地提取[J]. 测绘科学, 2017,42(6):93-98.
	[ Gao Y H, Gu Y Y, Qiao X N, et al. Study on extracting urban area in Nanjing based on DMSP/OLS nighttime light data[J]. Science of Surveying and Mapping, 2017,42(6):93-98. ]
[16]	赵晓伟, 孟治国, 郑兴明, 等. 夜间灯光数据的东三省城市群发展特征研究[J]. 测绘科学, 2019(4):1-13.
	[ Zhao X W, Meng Z G, Zheng X M, et al. Research on landscape spatial pattern estimation of cities in Northeast China using nighttime luminosity data[J]. Science of Surveying and Mapping, 2019(4):1-13. ]
[17]	杨雪, 张文忠. 基于栅格的区域人居自然和人文环境质量综合评价—以京津冀地区为例[J]. 地理学报, 2016,71(12):2141-2154.
	[ Yang X, Zhang W Z. Combining natural and human elements to evaluate regional human settlements quality based on raster data: a case study in Beijing-Tianjin-Hebei region[J]. Acta Geographica Sinica, 2016,71(12):2141-2154. ]
[18]	刘焱序, 吴文恒, 温晓金, 等. 晋陕蒙能源区城镇化过程及其对生态环境的影响[J]. 地理研究, 2013,32(11):2009-2020.
	[ Liu Y X, Wu W H, Wen X J, et al. Urban process and its eco-environmental impact in Shanxi-Shaanxi-Inner Mongolia energy area[J]. Geographical Research, 2013,32(11):2009-2020. ]
[19]	徐翀崎, 李锋, 韩宝龙, 等. 动态适应性生态经济区划模型及其应用[J]. 生态学报, 2017,37(5):1740-1748.
	[ Xu C Q, Li F, Han B L, et al. Adaptive eco-economic regionalization model and its application[J]. Acta Ecologica Sinica, 2017,37(5):1740-1748. ]
[20]	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.
	[ Zhang P Q, Yu X C, Liu Z, et al. A study on relative radiometric correction of multitemporal remote sensing images[J]. Journal of Remote Sensing, 2006,10(3):339-344. ]
[21]	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.
[22]	Liu Z F, He C Y, Zhang Q F, 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.
[23]	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.
	[ Zou J G, Chen Y H, Tian J, et al. Construction of the calibration model for DMSP/OLS nighttime light images based on ArcGIS[J]. Journal of Geomatics, 2014,39(4):33-37. ]
[24]	张梦琪, 何宗宜, 樊勇. DMSP/OLS稳定夜间灯光影像的校正方法[J]. 测绘通报, 2017(12):58-62.
	[ Zhang M Q, He Z Y, Fan Y. Calibration for DMSP/OLS stable nighttime light images[J]. Bulletin of Surveying and Mapping, 2017(12):58-62. ]
[25]	曹子阳, 吴志峰, 匡耀求, 等. 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.
	[ Cao Z Y, Wu Z F, Kuang Y Q, et al. Correction of DMSP/OLS night-time light images and its application in China[J]. Journal of Geo-information Science, 2015,17(9):1092-1102. ]
[26]	吴健生, 李双, 张曦文. 中国DMSP-OLS长时间序列夜间灯光遥感数据饱和校正研究[J]. 遥感学报, 2018,22(4):621-632.
	[ Wu J S, Li S, Zhang X W. Research on saturation correction for long-time series of DMSP-OLS nighttime light dataset in China[J]. Journal of Remote Sensing, 2018,22(4):621-632. ]
[27]	卓莉, 张晓帆, 郑璟, 等. 基于EVI指数的DMSP/OLS夜间灯光数据去饱和方法[J]. 地理学报, 2015,70(8):1339-1350.
	[ Zhuo L, Zhang X F, Zheng J, et al. An EVI-based method to reduce saturation of DMSP/OLS nighttime light data[J]. Acta Geographica Sinica, 2015,70(8):1339-1350. ]
[28]	郑子豪, 陈颖彪, 吴志峰, 等. 单元路网长度的DMSP/OLS夜间灯光数据去饱和方法[J]. 遥感学报, 2018,22(1):161-173.
	[ Zheng Z H, Chen Y B, Wu Z F, et al. Method to reduce saturation of DMSP/OLS nighttime light data based on UNL[J]. Journal of Remote Sensing, 2018,22(1):161-173. ]
[29]	National Geophysical Data Center[DB/OL]. https://ngdc.noaa.gov/eog/dmsp.
[30]	国家基础地理信息中心. 全国地理信息资源目录服务系统[DB/OL]. http://www.webmap.cn/commres.do?method=data Download.
	[ National Geomatics Center of China. National Catalogue Service for Geographic Information[DB/OL]. http://www.webmap.cn/commres.do?method=data Download.]
[31]	中国经济社会大数据平台. 统计年鉴[EB/OL]. http://data.cnki.net/Yearbook/Navi?type=type&code=A.
	[ Economic and Social Big Data Research Platform of China. Statistical Yearbook[EB/OL]. http://data.cnki.net/Yearbook/Navi?type=type&code=A.]
[32]	Feng-Chi, Hsu. Global radiance calibrated nighttime lights product readme[EB/OL]. tps://ngdc.noaa.gov/ eog/dmsp /radcal_readme.txt.2016.11.5.
[33]	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.

影像1	影像2	R2
F12_1992	F14_2003	0.8296
F12_1992	F15_2003	0.8194
F14_2003	F18_2013	0.8067
F15_2003	F18_2013	0.8040
F12_1992	F18_2013	0.7849

参考影像	卫星序号	年份	a	b	c	R²
F12_1999	F10	1992	0.0289	0.4592	0.5964	0.9141
		1993	0.0337	0.6344	0.2009	0.9317
		1994	0.0297	0.6427	0.3139	0.9462
	F12	1994	0.0268	0.5495	0.6740	0.9118
		1995	0.0251	0.5003	0.4986	0.9197
		1996	0.0282	0.4277	0.6795	0.9169
		1997	0.0261	0.6148	0.5313	0.9223
		1998	0.0243	0.4029	0.5352	0.9237
		1999	0.0233	0.5681	0.4711	0.9295
F14-15_2003	F14	1997	0.0718	1.0731	0.9221	0.8809
		1998	0.0698	0.6516	0.6792	0.8673
		1999	0.0703	0.9668	0.3548	0.8886
		2000	0.0614	0.3592	0.7600	0.8686
		2001	0.0539	0.5547	0.6327	0.8660
		2002	0.0481	0.5704	0.7558	0.8253
		2003	0.0450	0.7220	0.5305	0.8504
	F15	2000	0.0604	0.0747	0.9920	0.8028
		2001	0.0583	0.2804	0.8407	0.8267
		2002	0.0533	0.2138	0.6170	0.8584
		2003	0.0521	0.7015	0.8924	0.8551
		2004	0.0555	0.2121	0.9536	0.8418
		2005	0.054	0.3883	0.8141	0.8012
		2006	0.0606	0.1536	0.9569	0.8488
		2007	0.0586	0.5257	0.6138	0.8426
F16_2006	F16	2004	0.0313	0.3529	0.3995	0.8252
		2005	0.0302	0.7243	0.3269	0.8515
		2006	0.0323	0.6195	0.3945	0.8639
		2007	0.0320	0.5127	0.3281	0.8626
		2008	0.0345	0.3021	0.3709	0.8275
		2009	0.0360	-0.0223	0.5689	0.7892
	F18	2010	0.0335	-0.2870	0.6554	0.7384
		2011	0.0302	0.0554	0.5498	0.7219
		2012	0.0367	-0.1889	0.6730	0.7997
		2013	0.0335	-0.2636	0.7255	0.7279

年份	校正前						校正后
年份	F10	F12	F14	F15	F16	F18	校正后
1992	0.7744						0.7441
1993	0.6613						0.7468
1994	0.6642	0.7406					0.7508
1995		0.7423					0.7691
1996		0.7319					0.7578
1997		0.7457	0.7545				0.7720
1998		0.7549	0.7621				0.7880
1999		0.7811	0.7938				0.7754
2000			0.7470	0.7767			0.7964
2001			0.7692	0.7647			0.8266
2002			0.8205	0.7894			0.8811
2003			0.8440	0.8581			0.8941
2004				0.8470	0.7884		0.8967
2005				0.8188	0.7984		0.9135
2006				0.8395	0.8173		0.9163
2007				0.8461	0.8210		0.9179
2008					0.8180		0.9126
2009					0.7288		0.8944
2010						0.7263	0.8925
2011						0.8021	0.8843
2012						0.7623	0.8660
2013						0.7927	0.8694

年份	校正前						校正后
年份	F10	F12	F14	F15	F16	F18	校正后
2002			0.8479	0.8372			0.8554
2003			0.8711	0.8810			0.8732
2004				0.8842	0.9220		0.8778
2005				0.9406	0.8354		0.9048
2006				0.8536	0.8449		0.8791
2007				0.8716	0.8655		0.8979
2008					0.8572		0.8947
2009					0.7550		0.8736
2010						0.7263	0.8759
2011						0.8021	0.8581
2012						0.7623	0.8697
2013						0.7927	0.8789

年份	校正前						校正后
年份	F10	F12	F14	F15	F16	F18	校正后
1992	—						—
1993	—						—
1994	0.6389	0.6838					0.7612
1995		0.6732					0.7639
1996		0.6589					0.6442
1997		0.6767	0.7142				0.7875
1998		0.6906	0.7191				0.8012
1999		0.7069	0.7491				0.8118
2000			0.7174	0.7269			0.8308
2001			0.7306	0.7249			0.8470
2002			0.7975	0.7533			0.8780
2003			0.8063	0.8272			0.8877
2004				0.8177	0.7471		0.8850
2005				0.7931	0.7706		0.9125
2006				0.7913	0.7570		0.9112
2007				0.7761	0.7366		0.9045
2008					0.7430		0.9045
2009					0.6667		0.9015
2010						0.6548	0.8994
2011						0.7101	0.8895
2012						0.6790	0.8804
2013						0.6829	0.8798

一种DMSP/OLS稳定夜间灯光影像中国区域的校正方法

Correction of DMSP/OLS Stable Night Light Images in China

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