Abstract. National LiDAR programs are increasingly adopted worldwide to support land management, infrastructure planning, and environmental monitoring. Italy launched its most extensive airborne LiDAR operation in July 2025 as part of the Integrated Monitoring System (SIM) project, funded by the National Recovery and Resilience Plan (PNRR). This effort represents the most extensive airborne LiDAR campaign ever conducted in the country, covering over 302,000 km2, including coastal zones and major islands. The acquisition plan is designed to ensure a minimum point density of 10 points/m2 and produce high-resolution DTMs and DSMs at a 0.25 m grid spacing. Given the unprecedented spatial and data volume, a robust, standardised, and fully automated quality assurance framework is essential. This paper presents the methodology used to evaluate geometric consistency and spatial accuracy across the national dataset. Congruence between overlapping flight strips is assessed by automatically extracting 100 × 100 m patches at regular intervals and computing point-to-point distances and cross-section profiles to detect horizontal and vertical discrepancies. Plano-altimetric accuracy is further evaluated through comparisons with terrestrial laser scanning (TLS) data collected in dedicated control areas, where robust plane fitting enables rigorous three-dimensional error estimation. Results from two control areas acquired with different sensors demonstrate the robustness and scalability of the proposed automated framework. The presented approach provides a reliable foundation for delivering high-precision national LiDAR products and offers a framework applicable to future large-scale geospatial acquisition programs.
V. Casella, M. Franzini, Davide Lodigiani· The International Archives o...· 0 citations
Abstract. This paper examines how to align PlanetScope and Sentinel-2 vegetation indices, focusing on the Normalized Difference Red Edge (NDRE) index, which is commonly used in precision agriculture for prescription maps. While Sentinel-2 is popular for crop monitoring, its low spatial resolution limits use in small or irregular fields. PlanetScope provides higher-resolution, more frequent imagery, but its sensor differs from the Sentinel-2, limiting compatibility with current research and tools. By testing three adjustment methods, the study shows that it is possible to align PlanetScope NDRE values with Sentinel-2: M1 (Linear Regression + Histogram Shifting + Histogram Matching), M2 (Histogram Matching), and M3 (per-band linear regression before index calculation). Two dates from 2022 were selected as representative seasonal extremes from the broader 2021–2023 dataset of 56 image pairs (Baldin, 2025), which was further analyzed through time-series methods. Resampling direction (PS→10 m, S2→3 m) minimally affects RMSE/MAE but significantly alters spatial structure and Moran’s I values; downscaling PS to 10 m decreases Moran’s I. M2 is suitable for standard applications, whereas M3 is preferable when preservation of spatial structure is important. Across the four examined scenarios, all methods reduce RMSE below the 0.07 agronomic threshold, with calibrated RMSE ranging from 0.02 to 0.05 (up to 0.06 across the full 56-pair dataset). M3’s advantage lies in how effectively it reduces spatial autocorrelation mismatch: a 43.4% reduction in Moran’s I (versus ~18.2% with M1 and M2) in the four example scenarios, and 39.5% versus 28.4% (M1) and 28.2% (M2) reduction over the full dataset.
Christian Massimiliano Baldin, V. Casella· The International Archives o...· 0 citations