Integrating IoT-UAV Sensing with Sentinel-2 NDVI-NDBI Indices for Urban Air Quality Monitoring in Makassar - Putri Samad; Dewiani; Hamdan Omar (Academic Bright Collaboration, 2026)
Purpose - Urban air pollution in rapidly developing cities like Makassar requires high-resolution monitoring through IoT-UAV sensing and Remote Sensing to capture localized dynamics effectively. This study proposes and evaluates a multi-scale framework that integrates real-time mobile aerial sensing with Sentinel-2 satellite imagery and NDVI-NDBI indices to analyze how urban morphology modulates air pollutant distribution within the urban canopy layer. Methods - Air quality metrics (CO₂, PM2.5, and VOCs) were monitored at five urban sites in Makassar at three time intervals (08:00, 12:00, 16:00). Data were collected using vertical profiling from 1–20 m with 30-second sampling at each meter, generating over 5,000 data points. NDVI and NDBI were derived from Sentinel-2 L2A imagery (September 2024) using QGIS 3.34, and spatially validated by overlaying UAV coordinates to assess the influence of infrastructure density. Findings - The results identify the KIMA industrial estate and Jl. A.P. Pettarani highway as primary pollutant hotspots (ANOVA, p<0.001). These zones correlate strongly with high urban density (NDBI > 0.24) and minimal green canopy (NDVI < 0.13), confirming that dense infrastructure and the lack of vegetative carbon sinks drive localized pollutant accumulation. Vertical profiles demonstrate a negative concentration gradient, identifying atmospheric stability as a critical factor in surface-level pollutant stagnation. Research Implication - This multi-scale approach provides urban planners with a robust diagnostic tool for prioritizing green infrastructure. However, this study is limited by its single-city scope and a specific temporal window in September 2024. Future research should incorporate seasonal variations across monsoon cycles to evaluate long-term dispersion and washout patterns. Originality – This study contributes a novel synthesis bridging mobile aerial sensing with macro-level satellite indices, revealing a direct spatial correlation between urban structural density and pollutant stagnation that independent ground sensors or macro-satellites cannot detect independently