A methodology is presented to accurately estimate electric power consumption from saturated night-time Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) imagery using a stable light correction. An area correction for the stable light image of DMSP/OLS for the year 1999 was performed and the build-up area rate data were used to clarify the intensity distribution characteristics of the stable light. Based on the spatial distribution characteristics of the stable light, the saturation light of the electric power supply area of Japan was corrected using a cubic regression equation. The regression between the correction calculations by the cubic regression equation and the statistical electric power consumption data was applied in Japan and also in China, India and 10 other Asian countries. The correction method was then evaluated. This study confirms that electric power consumption can be estimated with high precision from the stable light.

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The Noise Reduction Filter (NRF) that is developed by the authors is applied to extract artificial nightlight components of a time series DMSP/OLS-VIS dataset. High frequency components from the time series DMSP/OLS-VIS dataset are exhausted and a direct current component is extracted by the NRF that is one of the Fourier analysis techniques. The inference of cloud and other disturbance noise are also removed, and a stable artificial nightlight is extracted by the NRF filtration. The intensity value in high power light areas observed by DMSP/OLS-VIS is saturated because of narrow dynamic range of the sensor gain. A simple model called "Deltaic Model" developed by authors corrected those saturated value. Verification of the accuracy of correction methods above described is carried out by comparison with electric power consumption of the calculated values from the model and statistical ones of each prefecture in Japan. Correlation of the values is satisfactory as shown R2 = 0.725. The results of this work shows the remote sensing method by using the DMSP/OLS-VIS nighttime imagery with the correction methods above described is useful to estimate the electric power consumption through a year of fixed areas. Keyword: DMSP/OLS-VIS, NRF filtration, Deltaic Model.

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Nighttime lights of the human settlements (hereafter, "stable lights") are seen as a valuable proxy of social economic activity and greenhouse gas emissions at the subnational level. In this study, we propose an improved method to generate the stable lights from Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) daily nighttime light data for 1999. The study area includes Japan, China, India, and other 10 countries in East Asia. A noise reduction filter (NRF) was employed to generate a stable light from DMSP/OLS time-series daily nighttime light data. It was found that noise from amplitude of the 1-year periodic component is included in the stable light. To remove the amplitude of the 1-year periodic component noise included in the stable light, the NRF method was improved to extract the periodic component. Then, new stable light was generated by removing the amplitude of the 1-year periodic component using the improved NRF method. The resulting stable light was evaluated by comparing it with the conventional nighttime stable light provided by the National Oceanic and Atmosphere Administration/National Geophysical Data Center (NOAA/NGDC). It is indicated that DNs of the NOAA stable light image are lower than those of the new stable light image. This might be attributable to the influence of attenuation effects from thin warm water clouds. However, due to overglow effect of the thin cloud, light area in new stable light is larger than NOAA stable light. Furthermore, the cumulative digital numbers (CDNs) and number of light area pixels (NLAP) of the generated stable light and NOAA/NGDC stable light were applied to estimate socioeconomic variables of population, electric power consumption, gross domestic product, and CO2 emissions from fossil fuel consumption. It is shown that the correlations of the population and CO2FF with new stable light data are higher than those in NOAA stable light data; correlations of the EPC and GDP with NOAA stable light data are higher those in the new stable light data.

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In this study, we estimated the CO 2 emission by fossil fuel consumption from electric power plant using DMSP stable light image for 1999 after correction for saturation effect. Digital number (DNs) of the stable light image in center of city areas are saturated for the strong nighttime intensity and characteristic of the OLS satellite sensor. To estimate the CO 2 emission using stable light image, saturation light correction method was developed by using DMSP radiance calibration image, which has not included saturation pixel in city areas. Then, regression analysis was performed with cumulative DNs of the corrected stable light image, electric power consumption, electric power generation and CO 2 emission by fossil fuel consumption from electric power plant each other. Results indicated that there are good relationship (R 2 >90%) between DNs of the corrected stable light image and other parameters. Finally, we estimated the CO 2 emission from electric power plant using corrected stable light image.