Spatial Data Thickening-Based Carbon Nephogram Using UAV Real-Time Monitoring

This study explores the combination of unmanned aerial vehicle (UAV) and ground monitoring for real-time carbon footprint mapping technology in low-carbon smart parks. Addressing the conflict between sampling rate and monitoring efficiency in UAV monitoring, we propose a spatial data thickening algorithm based on moving window spline interpolation and time series fusion with inverse distance weighting. Meanwhile, a comprehensive carbon monitoring algorithm is proposed by combining ground and spatial carbon emission calculation methods. Furthermore, the concept of carbon nephogram is introduced to depict the spatial distribution and intensity of carbon emissions. By establishing an integrated monitoring system and conducting simulation experiments in a small-scale smart park scenario, the results show that the collaborative monitoring system has superiority in improving monitoring efficiency and affording intuitive insights. It provides an effective methodology for the intelligent design and maintenance of smart parks.