In this study, the DMSP/OLS nighttime satellite data of China from 1992 to 2013 were used to find the relationships between GDP and nighttime satellite data. The nighttime satellite imageries were corrected by mutual correction, saturation correction, and continuity correction based on the invariant target region method. Then the lighting information of Mainland China and 31 provincial regions were extracted and models between GDP and light information, including linear, quadratic polynomial, power function, and exponential regression models, were tested to find the optimal ones. The results are showed as follows. 1) The corrected DMSP/OLS nighttime satellite data are more stable and continuous than the uncorrected data. 2) There is a strong correlation between the corrected DMSP/OLS night light dataset and GDP. 3) Exponential model was the most suitable one for predicting GDP of Mainland China, with the R2 value of 0.97 and MARE of 11.32%.4)Provincial models of long time series are better than the annual provincial administrative region models. The exponential function models were optimal for the four municipalities and the top six provincial economic entities, and the quadratic polynomial models were optimal for the other administrative regions, whose R2 values are above 0.95 and MARE's are about 10%.
GU Pengcheng
,
WANG Shixin
,
ZHOU Yi
,
LIU Wenliang
,
SHANG Ming
. Estimation of GDP based on long time series of DMSP/OLS nighttime light images[J]. Journal of University of Chinese Academy of Sciences, 2019
, 36(2)
: 188
-195
.
DOI: 10.7523/j.issn.2095-6134.2019.02.006
[1] 王金南,逯元堂,周劲松,等.基于GDP的中国资源环境基尼系数分析[J].中国环境科学,2006,26(1):111-115.
[2] 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(7):111-119.
[3] 李峰, 米晓楠, 王粉鸽,等. 基于DMSP-OLS和Landsat8数据的省域GDP空间化方法:以北京市为例[J]. 重庆师范大学学报(自然科学版), 2016(1):138-142.
[4] 何洋, 程辉, 唐亮. 基于DMSP/OLS数据的我国省级经济发展水平研究[J]. 地理空间信息, 2014, 12(2):79-82.
[5] 杨眉, 王世新, 周艺,等. 基于DMSP/OLS影像的城市化水平遥感估算方法[J]. 遥感信息, 2011(4):100-106.
[6] 李峰, 卫爱霞, 米晓楠,等. 基于NPP-VⅡRS夜间灯光数据的河北省GDP空间化方法[J]. 信阳师范学院学报(自然科学版), 2016(1):152-156.
[7] Elvidge C D, Sutton P C, Tuttle B T, et al. Change detection in satellite observed nighttime lights:1992-2003[C]//Urban Remote Sensing Joint Event. IEEE, 2007:1-4.
[8] 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.
[9] Ghosh T. Estimation of Mexico's informal economy and remittances using nighttime imagery[C]//Urban Remote Sensing Event. IEEE, 2009:1-10.
[10] 韩向娣, 周艺, 王世新. 基于夜间灯光和土地利用数据的GDP空间化[J]. 遥感技术与应用, 2012, 27(3):396-405.
[11] 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.
[12] 何洋, 程辉, 唐亮. 基于DMSP/OLS数据的我国省级经济发展水平研究[J]. 地理空间信息, 2014, 12(2):79-82.
[13] 马丹, 程辉, 毛艳玲,等. 基于DMSP/OLS夜间灯光影像的省际经济发展水平评估模型研究[J]. 应用科学学报, 2017, 35(5):647-657.
[14] 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.
[15] 曹子阳. 基于夜间灯光影像的GDP空间分布模拟研究及其与PM2.5浓度的相关分析[D]. 广州:中国科学院广州地球化学研究所, 2016.
[16] 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.
[17] 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.
[18] Zhu X, Ma M, Yang H, et al. Modeling the spatiotemporal dynamics of gross domestic product in China using extended temporal coverage nighttime light data[J]. Remote Sensing, 2017, 9(6):626.
