Using Multi-Source Geospatial Information to Reduce the Saturation Problem of DMSP/OLS Nighttime Light Data

Abstract

The DMSP/OLS Nighttime light (NTL) data directly reflect the spatial distribution and light intensity of artificial lighting from the Earth’s surface at night, and has become an emerging instrument for urbanization research, including in the monitoring of urban expansion, assessment of socio-economic vitality, and estimation of energy consumption and population. However, due to the imperfect sensor design of DMSP/OLS, the dynamic range of the digital number (DN) of NTL is limited (0, 63), leading to a significant saturation problem when describing the actual light intensity, especially in dense urban areas with high light intensity. This saturation problem masks spatial differences in light intensity and weakens the reliability of DMSP/OLS NTL data. Therefore, this study proposes a novel desaturation indicator that combines NDBI and POI, the Building and POI Density-Adjusted Nighttime Light Index (BPANTLI), to regulate the DMSP/OLS NTL saturation problem based on the spatial characteristics of urban structures and human activity intensity. The proposed method is applied to three urban agglomerations with the most severe light saturation issues in China. The geographical detector model is firstly utilized to quantify the effectiveness of NDBI and POI in reflecting the difference in light intensity distribution from the NTL potential saturation region (NTL DN value (53, 63)) and NTL unsaturation region (NTL DN value (0, 52)), so as to clarify the feasibility of developing the BPANTLI. The applicability of BPANTLI is validated through three aspects—comparison of the desaturation capacity and the performance of delineating light intensity; verification of the consistency of BPANTLI with radiometric calibration nighttime light product (RCNTL) and NPP/VIIRS data; and assessing the accuracy of the BPANTLI in estimating socio-economic parameters (GDP, electricity consumption, population density). The results indicate that the BPANTLI possesses superior capability in regulating the NTL saturation problem, achieving good performance in distinguishing inner-urban structures. The regulated results reveal a remarkably improved correspondence with the RCNTL and NPP/VIIRS data, providing a more realistic picture of the light intensity distribution. It is worth noting that, given the advantages of NDBI and POI vector data in spatial resolution, the BPANTLI established in this study can overcome the limitation of the spatial resolution of DMSP/OLS nighttime lighting data and achieve dynamic transformation of the spatial resolution. The higher spatial resolution desaturation results allow for a better characterization of the light intensity distribution. Moreover, the BPANTLI-regulated light intensity significantly improves the accuracy of estimating electricity consumption, GDP, and population density, which provides a valuable reference for urban socio-economic activity assessment. Thus, the BPANTLI proposed in this study can be considered as a reasonable desaturation method with a high application value.