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• W4313405563 abstract "Abstract Artificial light at night is one significant threat to protected areas from urbanization (ALAN). As urbanization continues to grow in Indonesia, there is a need for forecasting future light pollution and ALAN for the protected areas in Indonesia. To fulfill this need, we are aimed to design a computational model and its implementation to forecast light pollution in nine protected areas in Indonesia via spatiotemporal modeling using linear models. In this study, in general, there are four steps to predict spatial-temporal light pollution, namely (i) data collection, (ii) data pre-processing, (iii) model and prediction of population, and (iv) model and prediction of light pollution. Two critical data must be provided: population data from the review area and light pollution data generated by the Earth Observations Group (EOG) at NOAA/NCEI. We are using nine conservation areas in Indonesia, namely the Kuningan Botanical Gardens, Bosscha Observatory, Timau Kupang National Observatory, Sermo Reservoir, Mount Batur Geopark, Sewu Mountains Geopark, Mount Rinjani Geopark, Lake Toba Geopark, and Belitong Geopark. The developed model involves a linear model to predict artificial light at night with spatial-temporal modeling. Through the fitting stage, the average RMSE in 2020, 2021, and 2021 was obtained, namely 0.079, 0.097, 0.071, respectively. Moreover, long-term predictions for the next 20 years have been conducted as well." @default.
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• W4313405563 date "2023-01-02" @default.
• W4313405563 modified "2023-10-01" @default.
• W4313405563 title "Spatiotemporal Prediction of Light Pollution Using Remote Sensing Datasets on Conservation Areas" @default.
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• W4313405563 doi "https://doi.org/10.21203/rs.3.rs-2414381/v1" @default.
• W4313405563 hasPublicationYear "2023" @default.
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