基于夜间灯光影像的中国电力消耗量估算及时空动态

doi:10.11821/dlyj201604003

摘要/Abstract

摘要：

提出夜间灯光降饱和指数模型,以中国大陆为研究对象,基于DMSP/OLS夜间灯光数据、MODIS NDVI产品、基础地理信息数据及社会经济统计数据,构建电力消耗估算模型,定量估算了2000-2012年电力消耗量,并采用空间统计分析方法,从不同时间、空间角度对省级、地级和县级单元的电力消耗量变化趋势和空间集聚程度进行分析。结果表明：夜间灯光降饱和指数模型能较好地降低夜间灯光的数据饱和和溢出,其中MDNVI模型的效果最好。从县级尺度电力消耗变化趋势的显著性来看,无明显变化区域主要出现在青藏高原,迅猛增长型多数分布在京津冀、长三角、珠三角和中东部省会城市。

关键词: 电力消耗, 夜间灯光, 时空动态, DMSP/OLS, ESDA

Abstract:

Nighttime light (NTL) data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) are able to provide information on nighttime luminosity, a correlation of the built environment and energy consumption. NVI (Nighttime-light Vegetation Index), RNVI (Ratio Nighttime-light Vegetation Index), DNVI (Difference Nighttime-light Vegetation Index), NDNVI (Normalized Difference Nighttime-light Vegetation Index), SANVI (Soil-adjusted Nighttime-light Vegetation Index) and MDNVI (Modified Difference Nighttime-light Vegetation Index) were used to compensate for the shortage of DMSP/OLS data. DMSP/OLS night lights data, MODIS NDVI products, China GIS database and socio-economic statistical data are also taken into consideration. An electricity consumption estimation model is used to obtain a figure for electricity consumption from 2000 to 2012. Lastly, we divide electricity consumption into four ratings and analyze the spatio-temporal patterns by using ESDA method. Results are as follows: All of the indexes can compensate the shortage of DMSP/OLS data, among which MDNVI is the best model. We reduced spatial overflow effect of night lights data by using MDNVI model. Then we built a linear regression model of electricity consumption by regression analysis, and we used it for DMSP/OLS data to retrieve China's electricity consumption spatial layout. We compared the MRE (mean relative error) between the result and related research, which proves that our result has a lower MRE and a higher accuracy. Finally we find a way to obtain China electricity consumption data from 2000 to 2012 quickly and effectively. Electric consumption grew quickly in China from 2000 to 2012; on the whole, maximum electric consumption increased from 6.79 M kW $?$ h to14.82 M kW $?$ h. We discovered, using downscaling analysis, electricity consumption showed significant differences within regions. We analyzed electricity consumption levels, Moran's I and LISA cluster in the study area from 2000 to 2012 by using statistical data. Results showed that generating capacity and electricity consumption of 31 provinces have a strong spatial correlation. Gradually formed four "HH" cluster areas, namely Langfang-Tianjin, the Pearl River Delta, Shanghai-Hangzhou-Nanjing and the West Bank of the Taiwan Straits from 2000 to 2012. Spatial agglomeration of electricity consumption at county scale is significant, with "HH" clusters mainly located in Beijing-Tianjin, the Yangtze River Delta, Pearl River Delta, Shandong Peninsula, Changchun-Harbin-Dalian area, and North Tianshan Mountains area. The "LL" pattern shows a gradual trend moving from the south-eastern edge of the Qinghai-Tibet Plateau to the Tibetan Plateau. The index proposed here combines information from both DMSP/OLS NTL data and MODIS NDVI data for more detailed characterizations in nighttime luminosity. Our assessments confirm its ability to reduce the NTL saturation. Moreover, its simplicity enables rapid characterization and monitoring of electricity consumption.

Key words: electricity consumption, nightlight, NVI, DMSP/OLS, ESDA

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.

Jinghu PAN, Junfeng LI. Estimate and spatio-temporal dynamics of electricity consumption in China based on DMSP/OLS images[J]. GEOGRAPHICAL RESEARCH, 2016, 35(4): 627-638.

图/表 7

图1

图2

图3

表1

图4

图5

图6

参考文献 9

[9]	[Yang Mei, Wang Shixin, Zhou Yi, et al.Review on applications of DMSP/OLS night-time emissions data. Remote Sensing Technology and Application, 2011, 26(1): 45-51.]
[10]	王鹤饶, 郑新奇, 袁涛. DMSP/OLS数据应用研究综述. 地理科学进展, 2012, 31(1): 11-19. doi: 10.11820/dlkxjz.2012.01.002
	[Wang Herao, Zheng Xinqi, Yuan Tao.Overview of researches based on DMSP/OLS nighttime light data. Progress in Geography, 2012, 31(1): 11-19.] doi: 10.11820/dlkxjz.2012.01.002
[11]	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. 科学通报, 2006, 51(7): 856-861. doi: 10.1360/972004-300
	[He Chunyang, Shi Peijun, Li Jinggang, et al.Research on the process of urbanization in China in 1990s based on DMSP/OLS night light data and statistical data. Chinese Science Bulletin, 2006, 51(7): 856-861.] doi: 10.1360/972004-300
[12]	潘竟虎, 韩文超. 近20a中国省会及以上城市空间形态演变. 自然资源学报, 2013, 28(3): 470-480. doi: 10.11849/zrzyxb.2013.03.012
	[Pan Jinghu, Han Wenchao.Spatial-temporal changes of urban morphology of provincial capital cities or above in China. Journal of Natural Resources, 2013, 28(3): 470-480.] doi: 10.11849/zrzyxb.2013.03.012
[13]	Jordan C F.Derivation of leaf-area index from quality of light on the forest floor. Ecology, 1969, 50(4): 663-666. doi: 10.2307/1936256
[1]	孙宁, 徐菊艳. 我国三大产业电力消耗影响因素的分解分析. 长江流域资源与环境, 2011, 20(8): 918-921.
	[Sun Ning, Xu Juyan.Decomposed analysis of affecting factors about electric power consumption of three main industries in China. Resources and Environment in the Yangtze Basin, 2011, 20(8): 918-921.]
[14]	Tucker C J.Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 1979, 8(2): 127-150. doi: 10.1016/0034-4257(79)90013-0
[15]	Huete A R.A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 1988, 25(3): 295-309. doi: 10.1016/0034-4257(88)90106-X
[2]	Elvidge C D, Baugh K, Hobson V, et al.Satellite inventory of human settlements using nocturnal radiation emissions: A contribution for the global toolchest. Global Change Biology, 1997, 3(5): 387-395. doi: 10.1046/j.1365-2486.1997.00115.x
[3]	Silvana A, Gilberto C, Antonio M, et al.Estimating population and energy consumption in Brazilian Amazonia using DMSP night-time satellite data. Computers, Environment and Urban Systems, 2005, 29(2): 179-195. doi: 10.1016/j.compenvurbsys.2003.09.004
[16]	Liu Huiqing, Huete A.A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(2): 457-465. doi: 10.1109/36.377946
[17]	Zhang Qingling, Schaaf C, Seto K C.The vegetation adjusted NTL urban index:A new approach to reduce saturation and increase variation in nighttime luminosity. Remote Sensing of Environment, 2013, 129(2): 32-41. doi: 10.1016/j.rse.2012.10.022
[4]	Alejandro Sánchez de Miguel, Zamorano J, Pascual S. Evolution of the energy consumed by street lighting in Spain estimated with DMSP-OLS data. Journal of Quantitative Spectroscopy & Radiative Transfer, 2014, 139(5): 109-117. doi: 10.1016/j.jqsrt.2013.11.017
[5]	李通, 何春阳, 杨洋, 等. 1995-2008年中国大陆电力消费量时空动态. 地理学报, 2011, 66(10): 1403-1412.
[18]	Forbes D J.Multi-scale analysis of the relationship between economic statistics and DMSP-OLS night light images. GIScience & Remote Sensing, 2013, 50(5): 483-499.
[19]	袁涛. DMSP/OLS数据支持的贫困地区测度方法研究. 北京: 中国地质大学(北京)硕士学位论文, 2013.
[5]	[Li Tong, He Chunyang, Yang Yang, et al.Understanding electricity consumption changes in Chinese Mainland from 1995 to 2008 by using DMSP/OLS stable nighttime light time series data. Acta Geographica Sinica, 2011, 66(10): 1403-1412.]
[6]	杨续超, 康丽莉, 张斌, 等. 基于多源遥感信息的电力消费量估算与影响因素分析: 以浙江省为例. 地理科学, 2013, 33(6): 718-723.
[19]	[Yuan Tao.Monitoring methods for poor areas supported by DMSP/OLS night-time light imgery. Beijing: Master Dissertation ofChina University of Geosciences (Beijing), 2013.]
[20]	徐建华. 现代地理学中的数学方法(第2版). 北京: 高等教育出版社, 1996.
[6]	[Yang Xuchao, Kang Lili, Zahng Bin, et al.Electricity consumption estimation using multi-sensor remote sensing data: A case study of Zhejiang province. Scientia Geographica Sinica, 2013, 33(6): 718-723.]
[7]	吴健生, 牛妍, 彭建, 等. 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. 地理研究, 2014, 33(4): 625-634. Wu Jiansheng, Niu Yan, Peng Jian, et al. Research on energy consumption dynamic among prefecture-level cities in China based on DMSP/OLS nighttime light. Geographical Research, 2014, 33(4): 625-634.] doi: 10.11821/dlyj201404003
[20]	[Xu Jianhua.Mathematical Methods in Contemporary Geography(2nd ed). Beijing: Higher Education Press, 1996.]
[21]	Anselin L.Interactive techniques and exploratory spatial data analysis. Geographic Information System Principles Techniques Manegement & Applications, 1999, 47(2): 415-421.
[22]	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. 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, et al.Correction of DMSP/OLS night-time light images and its application in China. Journal of Geo-information Science, 2015, 17(9): 1092-1102.] doi: 10.3724/SP.J.1047.2015.01092
[8]	Cao X, Wang J, Chen J, et al.Spatialization of electricity consumption of China using saturation-corrected DMSP-OLS data. International Journal of Applied Earth Observation and Geoinformation, 2014, 28(5): 193-200. doi: 10.1016/j.jag.2013.12.004
[9]	杨眉, 王世新, 周艺, 等. DMSP/OLS夜间灯光数据应用研究综述. 遥感技术与应用, 2011, 26(1): 45-51.

年份	一元线性拟合方程	决定系数R²	年份	一元线性拟合方程	决定系数R²
2000	y =0.0845 x +1×10⁶	0.8988	2007	y =0.1825 x +0.47×10⁶	0.8890
2001	y =0.0841 x +1×10⁶	0.8749	2008	y =0.1554 x +1×10⁶	0.8931
2002	y =0.0918 x +1×10⁶	0.8971	2009	y =0.1751 x +2×10⁶	0.8705
2003	y =0.1496 x +0.46×10⁶	0.9178	2010	y =0.1512 x +3×10⁶	0.8724
2004	y =0.1589 x +0.72×10⁶	0.9271	2011	y =0.2196 x +2×10⁶	0.9028
2005	y =0.1743 x +0.54×10⁶	0.9447	2012	y =0.2598 x +4066.1	0.9052
2006	y =0.1556 x +1×10⁶	0.9031