Monitoring treeline dynamics is crucial for assessing the impacts of global change and ecosystem shifts in mountainous environments. This study integrates field and remote sensed data collected along altitudinal gradients to analyze vegetation patterns and structural characteristics of the upper treeline ecotones. We sampled at two distinct sites: i) in the Western Alps, above a larch (Larix decidua) and Swiss stone pine (Pinus cembra) forests, ii) in the Central Apennines, above an European black pine (Pinus nigra) high-elevation plantation. During the summer of 2024, we conducted ground-based measurements along altitudinal transects from the mountain peak down to the closed forest margin, providing detailed information on species composition, tree structure features and environmental parameters. In the field, we recorded tree positions using a GNSS receiver and measured tree basal diameter and height. We collected tree cores at the stem base with a Pressler borer to determine the tree cambial age, the encroachment date and the radial growth pattern. These field data served as ground truth to validate and extend the analysis to the entire slope surveys with UAVs. On the same slopes hosting the altitudinal transects, we used drones to cover areas of approximately 20 ha each. With a LiDAR sensor we recorded the tree positions along the entire slope, detected the spatial patterns of the tree recolonization process, measured tree parameters with high accuracy, and extracted microtopography features, a potential driver for seedling establishment. Additionally, we used multispectral camera to detect tree species distribution. Point-pattern analyses aimed to determine the spatio-temporal patterns of the tree recolonization process, highlighting facilitation or limitation processes between species, between cohorts or individuals of different ages, and with respect to the presence of shrubs or microtopographic features. This integrated approach, within the PRIN-OLYMPUS Italian project, will contribute to improve the accuracy of treeline mapping and the detection of key drivers of spatial variability. It will also enhance the efficiency to monitor high altitude ecosystem changes over time and to develop predictive models for future treeline shifts.