Abstract
Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicators at the national and regional scales to study regional economic development. However, most previous studies only chose a single measurement indicator (such as GDP) and adopted simple regression methods to investigate the economic development of a certain area based on DMSP-OLS or NPP-VIIRS stable NTL data. The status quo shows the problems of using a single evaluation index—it has a low evaluation precision. The LJ1-01 satellite is the first dedicated NTL remote sensing satellite in the world, launched in July 2018. The data provided by LJ1-01 have a higher spatial resolution and fewer blooming phenomena. In this paper, we compared the accuracy of the LJ1-01 data and NPP-VIIRS data in detecting county-level multidimensional economic development. In three provinces in China, namely, Hubei, Hunan and Jiangxi, 20 socioeconomic parameters were selected from the following five perspectives: economic conditions, people’s livelihood, social development, public resources and natural vulnerability. Then, a County-level Economic Index (CEI) was constructed to evaluate the level of multidimensional economic development, with the spatial pattern of the multidimensional economic development also identified across the study area. The present study adopted the random forest (RF) and linear regression (LR) algorithms to establish the regression model individually, and the results were evaluated by cross-validation. The results show that the RF algorithm greatly improves the accuracy of the model compared with the LR algorithm, and thus is suitable for the study of NTL data. In addition, a better determinate coefficient (R2) based on the LJ1-01 data (0.8168) was obtained than that from the NPP-VIIRS data (0.7245) in the RF model, which reflects that the LJ1-01 data offer better potential in the evaluation of socioeconomic parameters and can be used to identify, both accurately and efficiently, multidimensional economic development at the county level.
Highlights
The availability of artificial lights is often associated with wealth and a modern society due to the increased intensity of urban lights caused by the rapid increase in human activities, traffic construction and urban expansion [1,2]
In the random forest (RF) model, the R2 value for the LJ1-01 data (0.8168) was higher than that for the NPP/VIIRS data (0.7245); the results show that the correlation between the LJ1-01 data and the County-level Economic Index (CEI) is obviously higher than the equivalent NPP/VIIRS data
The results show that the R2 of LJI-01 (0.7920 in the RF model) is higher than that of NPP-VIIRS (0.7054 in the RF model)
Summary
The availability of artificial lights is often associated with wealth and a modern society due to the increased intensity of urban lights caused by the rapid increase in human activities, traffic construction and urban expansion [1,2]. Previous studies have shown that, compared to the DMSP-OLS data, NPP-VIIRS images have a higher accuracy in urban built-up area mapping and socioeconomic parameter estimation [10,11]. The LJ1-01 data do not suffer the same problems of saturation and blooming as the DMSP-OLS data, because the gap in sensor capabilities, lighting sources, are less averaged by the surrounding areas in an LJ1-01 image, which possesses a higher spatial resolution [15]. The specifications of these three types of NTL data are shown in data in urban development analysis and socioeconomic studies
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