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Pubblicazioni Scientifiche

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Spectral heterogeneity from the spaceborne imaging spectrometer EnMAP reveals biodiversity patterns in forest ecosystems
Mostra abstract
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 (R<sup>2</sup> 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 (R<sup>2</sup> up to 0.35). In contrast, lower R<sup>2</sup> values were obtained when SH was computed from specific VIs. The weighted approaches, especially when restricted to plots with CC >70%, consistently yielded higher R<sup>2</sup> 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)
Where are we now with European forest multi-taxon biodiversity and where can we head to?
Burrascano , Sabina , Chianucci , Francesco , Trentanovi , Giovanni , Kepfer-Rojas , Sebastian , Sitzia , Tommaso , Tinya , Flóra , Doerfler , Inken , Paillet , Yoan , Nagel , Thomas A. , Mitić , Božena , Morillas , Lourdes , Munzi , Silvana , Van Der Sluis , Theo , Alterio , Edoardo , Balducci , Lorenzo , de Andrade , Rafael Barreto , Bouget , Christophe , Giordani , P. , Lachat , Thibault , Matošević , Dinka , Napoleone , Francesca , Nascimbene , Juri , Paniccia , Chiara , Roth , Nicolas , Aszalós , Réka , Brazaitis , Gediminas , Cutini , Andrea , D'Andrea , Ettore , de Smedt , Pallieter , Heilmann-Clausen , Jacob , Janssen , Philippe , Kozák , Daniel , Mårell , Anders , Mikoláš , Martin , Nordén , Björn , Matula , Radim , Schall , Peter , Svoboda , Miroslav , Ujházyová , Mariana , Vandekerkhove , Kris , Wohlwend , Michael Rudolf , Xystrakis , Fotios , Aleffi , Michele , Ammer , Christian , Archaux , Frédéric , Asbeck , Thomas , N Avtzis , Dimitrios N. , Ayasse , Manfred , Bagella , Simonetta , Balestrieri , Rosario , Barbati , Anna , Basile , Marco , Bergamini , Ariel , Bertini , Giada , Biscaccianti , Alessandro Bruno , Boch , Steffen , Bölöni , János , Bombi , Pierluigi , Boscardin , Yves , Brunialti , Giorgio , Bruun , Hans Henrik , Buscot , François , Byriel , David Bille , Campagnaro , Thomas , Campanaro , Alessandro , Chauvat , Matthieu , Ciach , Michał , Čiliak , Marek , Cistrone , Luca , Pereira , Joaò Manuel Cordeiro , Daniel , Rolf , de Cinti , Bruno , de Filippo , Gabriele , Dekoninck , Wouter , Di Salvatore , Umberto , Dumas , Yann , Elek , Zoltán , Ferretti , Fabrizio , Fotakis , Dimitrios G. , Frank , Tamás , Frey , Julian , Giancola , Carmen , Gömöryová , Erika , Gosselin , Marion , Gosselin , Frédéric , Goßner , Martin M. , Götmark , Frank , Haeler , Elena , Hansen , Aslak Kappel , Hertzog , Lionel R. , Hofmeister , Jeňýk , Hošek , Jan , Johannsen , Vivian Kvist , Justensen , Mathias Just , Korboulewsky , Nathalie , Kovács , Bence , Lakatos , Ferenc , Landivar , Carlos Miguel , Lens , Luc , Lingua , Emanuele
Mostra abstract
The European biodiversity and forest strategies rely on forest sustainable management (SFM) to conserve forest biodiversity. However, current sustainability assessments hardly account for direct biodiversity indicators. We focused on forest multi-taxon biodiversity to: i) gather and map the existing information; ii) identify knowledge and research gaps; iii) discuss its research potential. We established a research network to fit data on species, standing trees, lying deadwood and sampling unit description from 34 local datasets across 3591 sampling units. A total of 8724 species were represented, with the share of common and rare species varying across taxonomic classes: some included many species with several rare ones (e.g., Insecta); others (e.g., Bryopsida) were represented by few common species. Tree-related structural attributes were sampled in a subset of sampling units (2889; 2356; 2309 and 1388 respectively for diameter, height, deadwood and microhabitats). Overall, multi-taxon studies are biased towards mature forests and may underrepresent the species related to other developmental phases. European forest compositional categories were all represented, but beech forests were over-represented as compared to thermophilous and boreal forests. Most sampling units (94%) were referred to a habitat type of conservation concern. Existing information may support European conservation and SFM strategies in: (i) methodological harmonization and coordinated monitoring; (ii) definition and testing of SFM indicators and thresholds; (iii) data-driven assessment of the effects of environmental and management drivers on multi-taxon forest biological and functional diversity, (iv) multi-scale forest monitoring integrating in-situ and remotely sensed information. © 2023 The Authors