The understory is an essential ecological and structural component of forest ecosystems. The lack of efficient, accurate, and objective methods for evaluating and quantifying the spatial spread of understory characteristics over large areas is a challenge for forest planning and management, with specific regard to biodiversity and habitat governance. In this study, we used terrestrial and airborne laser scanning (TLS and ALS) data to characterize understory in a European beech and black pine forest in Italy. First, we linked understory structural features derived from traditional field measurements with TLS metrics, then, we related such metrics to the ones derived from ALS. Results indicate that (i) the upper understory density (5–10 m above ground) is significantly associated with two ALS metrics, specifically the mean height of points belonging to the lower third of the ALS point cloud within the voxel (HM<inf>1/3</inf>) and the corresponding standard deviation (SD<inf>1/3</inf>), while (ii) for the lower understory layer (2–5 m above ground), the most related metric is HM<inf>1/3</inf> alone. As an example application, we have produced a map of forest understory for each layer, extending over the entire study region covered by ALS data, based on the developed spatial prediction models. With this study, we also demonstrated the power of hand-held mobile-TLS as a fast and high-resolution tool for measuring forest structural attributes and obtaining relevant ecological data. © 2023 by the authors.

Forest phenology plays a key role in the global terrestrial ecosystem influencing a range of ecosystem processes such as the annual carbon uptake period, and many food webs and changes in their timing and progression. The timing of the start of the phenology season has been successfully determined at a range of scales, from the individual tree by in situ observations to landscape and continental scales by using remotely sensed vegetation indices (VIs). The spatial resolution of satellites is much coarser than traditional methods, creating a gap between space-borne and actual field observations, which brings limitations to phenological research at the ecosystem level. Several unconsidered methodological and observational-related limitations may lead to misinterpretation of the timing of the satellite-derived signals. The aim of this study is therefore to clarify the meaning of a set of spring phenology metrics derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series in beech forests distributed across Europe with respect to PEP725 in situ observations, from 2003 to 2020. To this aim, we (i) tested the differences between remotely sensed and in situ start-of-season (SOS) metrics and (ii) quantified the influence of latitude, elevation, temperature, and precipitation on such differences. Results demonstrated that there is a clear temporal gradient among the different SOS metrics, all of them occurring prior to the in situ observations. Furthermore, latitude and temperatures proved to be the main factors guiding the differences between remotely sensed and in situ SOS metrics. Evidence from this study may help in recognizing the actual meaning of what we see by means of remotely sensed phenology metrics. In this perspective, field observations are crucial in understanding phenology events and provide a reference base. Satellite data, on the other hand, complement field observations by filling in gaps in spatial and temporal coverage, thus enhancing the overall understanding. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.