UAV data fusion approach to assess vegetation recovery dynamics after pipeline construction

Abstract. Post-construction vegetation monitoring along linear infrastructures is increasingly required to support restoration assessment, yet conventional ground surveys remain spatially sparse and difficult to scale over narrow, heterogeneous corridors. This limitation is critical in recently replanted pipeline clearings, where plant-level restoration outcomes must be inferred under operational constraints and where satellite-based monitoring cannot reliably resolve early post-restoration signals at the scale of individual saplings. This study addresses the problem by developing a UAV data-fusion workflow that integrates UAV laser scanning (ULS), UAV multispectral imagery (UAV-MS), and ultra-high-resolution UAV-RGB observations for sapling-level vitality assessment. The workflow was tested in two restored pipeline corridor sites in the Apennines (Italy), Ponte Baffoni (4.6 ha) and Ca’ Romano (1.4 ha), surveyed in May 2025. ULS data were used to detect and geolocate saplings, UAV-MS data were used to extract vegetation-index metrics (NDVI, GNDVI, NDRE), and UAV-RGB imagery supported plot-level expert validation. A PCA-based soft-labelling strategy generated proxy vitality labels, which were then used to train a Random Forest classifier to derive corridor-scale probabilistic maps of sapling vitality, subsequently expressed as ALIVE, DEAD, and UNCERTAIN classes. The proposed workflow detected 5427 saplings in Ponte Baffoni and 801 in Ca’ Romano. Random Forest classification achieved balanced accuracies of 0.78 and 0.83, respectively. The resulting corridor-scale maps suggested mortality rates of 48.9% in Ponte Baffoni and 40.0% in Ca’ Romano. These results suggest that multi-sensor UAV fusion can provide spatially explicit, sapling-level indicators of restoration performance, complementing field surveys and supporting operational post-construction assessment in restoration corridors.