Research on saturation correction for long-time series of DMSP-OLS nighttime light dataset in China
WU Jiansheng;LI Shuang;ZHANG Xiwen;Key Laboratory for Urban Habitat Environmental Science and Technology, School of Urban Planning and Design,Peking University;Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences,Peking University;China Academy of Urban Planning & Design;
Night Time Light(NTL) data have been verified to be a favorable proxy for socioeconomic activities. However, saturation correction is necessary to make the results credible and reliable when detecting the multitemporal socioeconomic changes by using time-series analysis of the NTL data. This study is aimed at presenting a new method for correcting the saturation effects of the Defense Meteorological Satellite Program-Operational Linescan System(DMSP-OLS) stable light images based on normalized differential vegetation index(NDVI)data. First, the different regions and years are selected as references for conducting intercalibration, which is different from the conventional invariant region method. A TNDVI indicator, which shows a significant positive correlation with the Digital Numbers(DNs) of the NTL data, is built based on the original NDVI data after the intercalibration. Second, a K-mean value is utilized to divide the cities into four types and then correct the saturation effects based on the various characteristics of the NTL and NDVI data of various regions. Third, the saturation threshold of the NTL dataset is accurately identified during the saturation correction. Furthermore, the saturated and unsaturated portions are analyzed to construct a saturation correction model. Finally, the relationship between the sum of the NTL brightness and Gross Domestic Product(GDP) before and after the saturation correction is compared to verify the effect of this new saturation correction method. In this research, unsaturated and saturated portions can be found in the NTL dataset, with a saturation threshold of 30, that is, 0—30 are unsaturated, whereas 31—63 are saturated. The functions between the DN values of the two portions and the corresponding original data are different, that is, the unsaturated portions comply with the linear model, whereas the saturated portions comply with the growth model. The various cluster partitions must be distinguished, and the saturation effects, especially the saturation correction for NTL dataset in large areas with remarkable regional differences, must be corrected. On the basis of the TNDVI data, the DN values of the NTL images after the saturation correction by using the growth model for the saturated portions are remarkable, that is, the DNs of F182013 are from 2.717 to 245.673 after the saturation correction, the spatial heterogeneity is enhanced, the fitting relationship with the regional GDP is improved, and the saturation effects caused by the satellite sensor set attributes are appropriately removed. Therefore, the data can appropriately reflect the intensity and spatial distribution of human socioeconomic activities without the saturation effects. Hence, the new saturation correction method is confirmed to be effective.
【CateGory Index】： TP79
【CateGory Index】： TP79