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Priyatikanto, R., Mayangsari, L., Prihandoko, R. A., & Admiranto, A. G. (2020). Classification of Continuous Sky Brightness Data Using Random Forest. Advances in Astronomy, 2020, 1–11.
Abstract: Sky brightness measuring and monitoring are required to mitigate the negative effect of light pollution as a byproduct of modern civilization. Good handling of a pile of sky brightness data includes evaluation and classification of the data according to its quality and characteristics such that further analysis and inference can be conducted properly. This study aims to develop a classification model based on Random Forest algorithm and to evaluate its performance. Using sky brightness data from 1250 nights with minute temporal resolution acquired at eight different stations in Indonesia, datasets consisting of 15 features were created to train and test the model. Those features were extracted from the observation time, the global statistics of nightly sky brightness, or the light curve characteristics. Among those features, 10 are considered to be the most important for the classification task. The model was trained to classify the data into six classes (1: peculiar data, 2: overcast, 3: cloudy, 4: clear, 5: moonlit-cloudy, and 6: moonlit-clear) and then tested to achieve high accuracy (92%) and scores (F-score = 84% and G-mean = 84%). Some misclassifications exist, but the classification results are considerably good as indicated by posterior distributions of the sky brightness as a function of classes. Data classified as class-4 have sharp distribution with typical full width at half maximum of 1.5 mag/arcsec2, while distributions of class-2 and -3 are left skewed with the latter having lighter tail. Due to the moonlight, distributions of class-5 and -6 data are more smeared or have larger spread. These results demonstrate that the established classification model is reasonably good and consistent.
Keywords: Skyglow
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Omar, N. S., & Ismal, A. (2019). Night Lights and Economic Performance in Egypt. Advances in Economics and Business, 7(2), 69–81.
Abstract: This paper, to the best of my knowledge, is the first to estimate the association between Nighttime Lights (NTL) and real Gross Domestic Product (GDP) at the national level, using sub-national GDP data for the 27 Egyptian governorates over FY08-FY13. The study finds that NTL has a positive and statistically significant
correlation with GDP at the sub-national and national levels. Hence, NTL can measure and predict GDP in Egypt, at the national and sub-national levels. These findings affirm most previous research that NTL could be a good proxy for GDP when official data are unavailable or time infrequent in developing countries. Keywords: Economics; Remote Sensing
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Asanuma, I., Hasegawa, D., Yamaguchi, T., Park, J. G., & Mackin, K. J. (2018). Island Activities Detected by VIIRS and Validation with AIS. Ars, 07(03), 171–182.
Abstract: A possibility to monitor the reclamation activities by remote sensing was discussed. The lights observed in the night time by Day Night Band (DNB) of Visible Infrared Imaging Radiometer Suite (VIIRS), ocean color observed in the day time by visible bands of VIIRS were the tools to monitor the surface activities, and the Automated Information System (AIS) was used to verify the types and number of vessels associated with the reclamation activities. The lights as the radiance from the surface were monitored by the object based analysis, where the object was defined as a radius of 5 km from the center of the Mischief Reef in the South China Sea (SCS). The time history of surface lights exhibited the increase of the radiance from January to May 2015 and the radiance was kept in the certain level to December 2016 with some variations. The ocean color, chlorophyll-a concentration as a proxy of sediments, showed an increase from February to June 2015 and returned to a low concentration in August 2015. According to the historical data of AIS, the number of dredgers has increased from February to August 2015 and the maximum number of dredgers was recorded in June 2015. The timing of increase of lights from surface, increase of chlorophyll-a concentration, and increase of number of vessels are consistent.
Keywords: Remote Sensing
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Liang, H., Guo, Z., Wu, J., & Chen, Z. (2019). GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression. Advances in Space Research, in press, S0273117719307136.
Abstract: Accurate spatial distribution information on gross domestic product (GDP) is of great importance for the analysis of economic development, industrial distribution and urbanization processes. Traditional administrative unit-based GDP statistics cannot depict the detailed spatial differences in GDP within each administrative unit. This paper presents a study of GDP spatialization in Ningbo City, China based on National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL) data and town-level GDP statistical data. The Landsat image, land cover, road network and topographic data were also employed as auxiliary data to derive independent variables for GDP modelling. Multivariate linear regression (MLR) and random forest (RF) regression were used to estimate GDP at the town scale and were assessed by cross-validation. The results show that the RF model achieved significantly higher accuracy, with a mean absolute error (MAE) of 109.46 million China Yuan (CNY)·km-2 and a determinate coefficient (R2=0.77) than the MLR model (MAE=161.8 million CNY·km-2, R2=0.59). Meanwhile, by comparing with the estimated GDP data at the county level, the town-level estimated data showed a better performance in mapping GDP distribution (MAE decreased from 115.1 million CNY·km-2 to 74.8 million CNY·km-2). Among all of the independent variables, NTL, land surface temperature (Ts) and plot ratio (PR) showed higher impacts on the GDP estimation accuracy than the other variables. The GDP density map generated by the RF model depicted the detailed spatial distribution of the economy in Ningbo City. By interpreting the spatial distribution of the GDP, we found that the GDP of Ningbo was high in the northeast and low in the southwest and formed continuous clusters in the north. In addition, the GDP of Ningbo also gradually decreased from the urban centre to its surrounding areas. The produced GDP map provides a good reference for the future urban planning and socio-economic development strategies.
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Otchia, C. S. & A., S. A. (2019). Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images. African Governance and Development Institute, .
Abstract: This study uses nightlight time data and machine learning techniques to predict industrial development in Africa. The results provide the first evidence on how machine learning techniques and nightlight data can be used to predict economic development in places where subnational data are missing or not precise. Taken together, the research confirms four groups of important determinants of industrial growth: natural resources, agriculture growth, institutions, and manufacturing imports. Our findings indicate that Africa should follow a more
multisector approach for development, putting natural resources and agriculture productivity growth at the forefront. Keywords: Remote Sensing
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