The Spectral Variation Hypothesis (SVH) proposes that spectral heterogeneity (SH), derived from optical data, can serve as a proxy for estimating biodiversity. In this study, we tested the SVH across 42 forest plots in the Italian Alps using imaging spectroscopy data from the EnMAP satellite. We investigated the relationship between SH—quantified using two different metrics, Rao's Q and the coefficient of variation (CV)—and tree species diversity (using Shannon's H index and species richness). We applied three levels of spectral analysis: (1) SH calculated for each individual EnMAP band; (2) SH aggregated across broader spectral ranges (Visible -VIS-, Near Infrared -NIR-, and Shortwave Infrared -SWIR-) and (3) SH derived from vegetation indices (VIs). These analyses were performed under three spatial approaches: (A) a normal approach assigning equal weight to all four EnMAP pixels intersecting a plot; (B) a weighted approach based on the proportional overlap of each pixel with the plot area; and (C) a weighted canopy cover (CC)>70% approach, which included only plots with CC greater than 70% as derived from airborne laser scanning (ALS) LiDAR data. Weak to moderate correlations were observed when SH was derived from single bands, with the strongest relationships in the NIR (R² approaching 0.4), followed by the VIS and SWIR regions. A similar trend emerged when SH was aggregated across broader spectral ranges, with the highest correlations again found in the NIR (R² up to 0.35). In contrast, lower R² values were obtained when SH was computed from specific VIs. The weighted approaches, especially when restricted to plots with CC >70%, consistently yielded higher R² values than the equal-weight approach in all three the spectral analysis. Results were consistent across both SH metrics (Rao's Q and CV), with stronger correlations when species richness was used as the biodiversity metric. This work highlights how EnMAP hyperspectral data, despite inherent constraints, can provide valuable insights into forest biodiversity monitoring. © 2025 The Author(s)

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.

The global decline of biodiversity has affected European forests, involving many tree species and forest-dwelling threatened animals. An integrated approach linking forest structure and multi-taxon diversity is increasingly needed to maintain the multifunctionality of forest ecosystems. We investigated the relationship between forest structure, deadwood elements, canopy attributes, and tree-related microhabitats on bat and bird communities in the north-eastern Italian Alps. We collected forest attributes, bats, and bird data on 40 forest plots encompassing the diversity of forest types. To assess the different contributions of each forest attribute variables we performed a two-step statistical analysis using generalised and linear models, including bat and bird taxonomical and functional diversity indices as response variables. Our findings reveal that bats and birds respond differently to variation in forest structural characteristics. Specifically, bat species richness was higher in forests with both higher standing tree and lying deadwood volume. The Shannon diversity index for bird community was higher in forests with high volumes of coarse lying deadwood and stumps. Moreover, plots with mature trees, gaps, and heterogeneous diameter distribution fostered the presence of generalist species of bats and birds, while the abundance of tree-related microhabitats was not significant for these two taxa. This study demonstrates that the optimal habitat conditions for bats and birds in Alpine forests are multifaceted. Promoting distinctive elements within forest stands and a complex forest structure through adaptations in forest management interventions would enhance the conservation of multi-taxon forest biodiversity. © 2024 The Authors