Fan Yang

Nighttime light (NTL) data is widely used to monitor urban development, economic activity and population distribution, supporting socio-economic analysis and sustainable development planning. SDGSAT-1 NTL data, with its multiple bands and high resolution, has become an important source for studying human activity patterns. However, directly summing radiance values on geographic grids assumes equal contributions from all grid cells, regardless of the differences in the ground area represented by each cell. As a result, regional nighttime light totals may be systematically distorted, particularly in cross-latitude analyses where pixel areas differ substantially. To address this aggregation issue, this study proposes a physically explicit area-weighting framework that converts SDGSAT-1 radiance to radiant flux by incorporating the actual surface area represented by each grid cell. The framework is validated using 25 cities spanning different latitudes and development levels in the Northern Hemisphere. Results show that the radiant flux model, which captures total emitted power, demonstrates improved performance over radiance-based aggregation (a density-based measure) in population estimation (R² = 0.85 vs. 0.82). By shifting from light intensity to integrated total energy, this approach improves cross-latitude comparability and enhances the methodological robustness of NTL-based analyses.

Urban nighttime lighting extends human activity hours and enhances safety but also wastes energy and causes light pollution. Influenced by building obstructions and surface reflections, light emissions exhibit significant anisotropy. Remote sensing can be used to observe nighttime lighting from high altitudes, but ground lighting anisotropy introduces angle-related errors. This study constructed a 3D urban nighttime lighting model using virtual simulations and conducted multi-angle observations to investigate anisotropy and its influencing factors. The results show that the illuminance distribution in urban functional areas is typically uneven, with ground-level illuminance varying linearly or exponentially with zenith angle and quadratically with azimuth angle. Some areas exhibit uniform illuminance without significant anisotropy. Nighttime light anisotropy is closely linked to urban geometry and light distribution, with building height, layout, and light source arrangement significantly influencing the anisotropic characteristics. The findings enhance our understanding of nighttime light anisotropy, provide a basis for developing angular effect models of complex scenarios, and quantify the upward light emission angles and intensities. These insights can be used to support corrections for multi-angle spaceborne nighttime lighting observations, contributing to more accurate data for urban planning and light pollution mitigation.

Spatial population distribution data is the discretization of demographic data into spatial grids, which has vital reference significance for disaster emergency response, disaster assessment, emergency rescue resource allocation, and post-disaster reconstruction. The random forest (RF) model, as a prominent method for modeling the spatial distribution of population, has been studied by many scholars, both domestically and abroad. Specifically, research has focused on aspects such as multi-source data fusion, feature selection, and data accuracy evaluation within the modeling process. However, discussions about parameter optimization methods during the modeling process and the impact of different optimization methods on modeling accuracy are relatively limited. In light of the above circumstances, this paper employs the RF model to conduct research on population spatialization with multi-source spatial information data. The study primarily explores the differences in model parameter optimization achieved through random search algorithms, grid search algorithms, genetic algorithms, simulated annealing algorithms, Bayesian optimization based on Gaussian process algorithms, and Bayesian optimization based on gradient boosting regression tree algorithms. Additionally, the study investigates the influence of different optimization algorithms on the accuracy of population spatialization modeling. Subsequently, the model with the highest accuracy is selected as the prediction model for population spatialization. Based on this model, a spatial population distribution dataset of Sichuan Province at a 1 km resolution is generated. Finally, the population dataset created in this paper is compared and validated with open datasets such as GPW, LandScan, and WorldPop. Experimental results indicate that the spatial population distribution dataset produced by the Bayesian optimization-based random forest model proposed in this paper exhibits a higher fitting accuracy with real data. The Coefficient of Determination (R²) is 0.6628, the Mean Absolute Error (MAE) is 12,459, and the Root Mean Squared Error (RMSE) is 25,037. Compared to publicly available international datasets, the dataset generated in this paper more accurately represents the spatial distribution of the population.