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Method to reduce saturation of DMSP/OLS nighttime light data based on UNL

  • role： First author第一作者
  • Affiliation：

    School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China

  • Email：zhengzh@email.com
  • Introduction：郑子豪(1992—　)，男，硕士研究生，研究方向为GIS与RS应用技术。E-mail：zhengzh@email.com
Zihao ZHENG,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China

  • Email：gzhuchenyb@126.com
  • Introduction：陈颖彪(1969—　)，男，教授，研究方向为GIS与RS应用技术。E-mail：gzhuchenyb@126.com
Yingbiao CHEN*,  
  • Affiliation：

    School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China

Zhifeng WU,  
  • Affiliation：

    School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China

Qifei ZHANG

Resümee

DMSP/OLS nighttime light data have been widely applied to reveal the intensity of human activities and the process of urbanization.However, the limitation of sensors leads to a serious saturation problem in the NTL data,especially in the city center area of high light intensity. Saturated nighttime light data cover the differences and details of light intensity in the central area of a city and reduce the application scenarios of DMSP/OLS data.The method for alleviating the saturation of light data is mainly divided into radiometric calibration and non-radiometric calibration. Although the radiometric calibration method achieves high precision, the complex calibration algorithm and interference factors lead to a low data output rate. The non-radiometric calibration method uses auxiliary data (including natural factors, social indicators, and economic indicators) to construct the function model and thereby establish correct saturation data. We use GeoDetector to identify the factors of Normalized Difference Vegetation Index (NDVI), Enhancel Vegetation Index (EVI), and Unit Network Length (UNL) and propose the unit network length-adjusted (UNLI) NTL Index after quantifying the explanatory power of stable light data. Furthermore, we evaluated the capabilities of EANTLI and UNLI to eliminate saturation through the “detailed description of the degree of light intensity in the saturation area,” “correlation analysis of UNLI, EANTLI, and (RCNTL) radiometric calibration data,” and “goodness of fit of power consumption and GDP”. Both UNLI and EANTLI yield good results in the modified saturation data,but UNLI achieves greater accuracy in describing the difference in the light intensities of the saturation area. UNLI can break through the limitations of spatial resolution and increase the intensity of light detail differences. In terms of the light of the highest intensity saturation region, UNLI is closely correlated with RCNTL. However, with the decrease of light intensity, the linear regression coefficientR2 of the UNLI, EANTLI and RCNTL gradually approached, and the fitting advantages of UNLI and RCNTL is lost. In the fitting analysis of power consumption and GDP, the piecewise calibration model of “EANTLI&UNLI” can maximize the advantages of EVI and UNL, which has the best fitting effect (adjustedR2=0.873). In this study, we proposed a new method to correct the saturation of night light data. The new method can effectively eliminate the saturation of native NTL in urban central areas. Compared with previous correction methods, the new method is highly advantageous in terms of light intensity detail and spatial resolution. Thus, the proposed method can accurately reflect the characteristics of urban structure from the perspective of night light luminance.

Schlüsselwort

DMSP/OLS;nighttime light;Geo detector;saturation;EANTLI;UNLI

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