Multi-Scale LiDAR Analysis of Urban Vegetation, Built Morphology, and Population Density in Zagreb

Understanding how built structures and urban vegetation jointly relate to population patterns is increasingly important for sustainable urban planning. This study used airborne LiDAR data to analyze three-dimensional urban morphology across Zagreb, Croatia, at neighborhood and district scales. A 1 m nDSM and classified rasters were used to derive canopy, building, height, volumetric, and built–vegetation balance metrics. Spatial clustering was assessed using Moran’s I, LISA, and Getis–Ord Gi*, while relationships with population density were evaluated using correlation and spatial regression models. At the neighborhood level, canopy cover ranged from 4.5% to 84.7%, while UMI ranged from 0.002 to 10.368. UMI showed significant spatial clustering (Moran’s I = 0.457, p = 0.001), with 24 high–high and 64 low–low clusters. Composite balance metrics outperformed individual vegetation or building indicators; logUMI provided the strongest performance for log-transformed population-density models, with SLM pseudo-R2 = 0.903. Agreement assessment showed high consistency for canopy cover (R2 = 0.997), while building height agreement was weaker for global datasets. Results indicate that transformed built–vegetation balance metrics provide useful complementary indicators for describing urban morphology and population density patterns.