Aerial LiDAR applications for environmental, urban, and cultural heritage studies

This thesis explores the potential and versatility of aerial Light Detection and Ranging (LiDAR) technology across diverse scientific applications, with particular emphasis on environmental monitoring, urban modeling, and cultural heritage documentation. Combining theoretical and empirical approaches, it evaluates LiDAR’s ability to generate high-resolution three-dimensional representations of both built and natural environments. Multiple case studies are used to assess methodological workflows, data processing efficiency, and analytical outcomes in relation to sustainable urban development and environmental resilience. The research first outlines the theoretical and technical foundations of LiDAR, describing its evolution, physical principles, and main configurations (airborne, terrestrial, and bathymetric). It reviews the data acquisition and processing chain, from raw point cloud generation to derivative products such as digital terrain and surface models. The study also examines LiDAR’s applications in environmental and archaeological research, highlighting its integration with artificial intelligence, UAV photogrammetry, and multispectral sensing. Empirical analyses compare software platforms for 3D urban modeling, showing that LASTools provides greater computational efficiency and classification accuracy, while ArcGIS Pro offers superior visualization and modeling flexibility. A further case study integrates LiDAR-derived morphological indicators with thermal remote sensing and meteorological data to investigate urban heat island dynamics in Bologna, identifying strong positive correlations between building intensity and temperature, and a mitigating effect of vegetation. Additional analyses focus on individual tree detection and segmentation using multi-source point clouds, demonstrating that performance depends on point density, canopy complexity, and data origin, with terrestrial LiDAR achieving the highest precision in stem detection and radius estimation. Overall, the findings confirm LiDAR’s robustness, scalability, and multidimensional analytical capacity. Its integration with GIS and remote sensing supports accurate 3D modeling, morphological analysis, and vegetation assessment, reinforcing its central role in sustainable geospatial research and urban-environmental planning.