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Pubblicazioni Scientifiche
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Spectral heterogeneity from the spaceborne imaging spectrometer EnMAP reveals biodiversity patterns in forest ecosystems
Torresani
,
Michele
,
Rossi
,
Christian
,
Mina
,
Marco
,
Menegaldo
,
Irene
,
Cappuccio
,
Matteo
,
Perrone
,
Michela
,
Hakkenberg
,
Christopher R.
,
Rocchini
,
Duccio
,
Puletti
,
Nicola
,
Stendardi
,
Laura
,
Montagnani
,
Leonardo
,
Tognetti
,
Roberto
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)
MASTREE+: Time-series of plant reproductive effort from six continents
Hacket-Pain
,
Andrew J.
,
Foest
,
Jessie J.
,
Pearse
,
Ian S.
,
LaMontagne
,
Jalene M.
,
Koenig
,
Walter D.
,
Vacchiano
,
Giorgio
,
Bogdziewicz
,
Michał
,
Caignard
,
Thomas
,
Celebias
,
Paulina
,
van Dormolen
,
Joep
,
Fernández-Martínez
,
Marcos
,
Moris
,
Jose V.
,
Palaghianu
,
Ciprian
,
Pesendorfer
,
Mario B.
,
Satake
,
Akiko
,
Schermer
,
Éliane
,
Tanentzap
,
Andrew J.
,
Thomas
,
Peter A.
,
Vecchio
,
Davide
,
Wion
,
Andreas P.
,
Wohlgemuth
,
Thomas
,
Xue
,
Tingting
,
Abernethy
,
Katharine A.
,
Aravena Acuña
,
Marie Claire
,
Barrera
,
Marcelo Daniel
,
Barton
,
Jessica H.
,
Boutin
,
Stan A.
,
Bush
,
Emma R.
,
Donoso Calderón
,
Sergio R.
,
Carevic
,
Felipe S.
,
Castilho
,
Carolina V.
,
Manuel Cellini
,
Juan
,
Chapman
,
Colin A.
,
Chapman
,
H. M.
,
Chianucci
,
Francesco
,
Costa
,
Patricia Da
,
Croisé
,
Luc
,
Cutini
,
Andrea
,
Dantzer
,
Ben J.
,
DeRose
,
Robert Justin
,
Dikangadissi
,
Jean Thoussaint
,
Dimoto
,
Edmond
,
da Fonseca
,
Fernanda Lopes
,
Gallo
,
Leonardo Ariel
,
Gratzer
,
Georg
,
Greene
,
David F.
,
Hadad
,
Martín Ariel
,
Huertas Herrera
,
Alejandro
,
Jeffery
,
Kathryn J.
,
Johnstone
,
Jill F.
,
Kalbitzer
,
Urs
,
Kantorowicz
,
Władysław
,
Klimas
,
Christie Ann
,
Lageard
,
Jonathan G.A.
,
Lane
,
Jeffrey E.
,
Lapin
,
Katharina
,
Ledwoń
,
Mateusz
,
Leeper
,
Abigail C.
,
Lencinas
,
María Vanessa
,
Lira-Guedes
,
Ana Cláudia
,
Lordon
,
Michael C.
,
Marchelli
,
Paula
,
Marino
,
Shealyn
,
Schmidt van Marle
,
Harald
,
McAdam
,
Andrew G.
,
Momont
,
Ludovic R.W.
,
Nicolas
,
Manuel
,
de Oliveira Wadt
,
Lúcia Helena
,
Panahi
,
Parisa
,
Martínez Pastur
,
Guillermo J.
,
Patterson
,
Thomas W.
,
Luis Peri
,
Pablo
,
Piechnik
,
Łukasz
,
Pourhashemi
,
Mehdi
,
Espinoza Quezada
,
Claudia
,
Roig
,
Fidel Alejandro
,
Peña-Rojas
,
Karen A.
,
Rosas
,
Yamina Micaela
,
Schueler
,
Silvio
,
Seget
,
Barbara
,
Soler
,
Rosina M.
,
Steele
,
Michael A.
,
Toro Manríquez
,
Mónica Del Rosario
,
Tutin
,
Caroline E.G.
,
Ukizintambara
,
Tharcisse
,
White
,
Lee J.T.
,
Yadok
,
Biplang Godwill
,
Willis
,
John L.
,
Zolles
,
Anita
,
Żywiec
,
Magdalena
,
Ascoli
,
Davide
Mostra abstract
Significant gaps remain in understanding the response of plant reproduction to environmental change. This is partly because measuring reproduction in long-lived plants requires direct observation over many years and such datasets have rarely been made publicly available. Here we introduce MASTREE+, a data set that collates reproductive time-series data from across the globe and makes these data freely available to the community. MASTREE+ includes 73,828 georeferenced observations of annual reproduction (e.g. seed and fruit counts) in perennial plant populations worldwide. These observations consist of 5971 population-level time-series from 974 species in 66 countries. The mean and median time-series length is 12.4 and 10 years respectively, and the data set includes 1122 series that extend over at least two decades (≥20 years of observations). For a subset of well-studied species, MASTREE+ includes extensive replication of time-series across geographical and climatic gradients. Here we describe the open-access data set, available as a.csv file, and we introduce an associated web-based app for data exploration. MASTREE+ will provide the basis for improved understanding of the response of long-lived plant reproduction to environmental change. Additionally, MASTREE+ will enable investigation of the ecology and evolution of reproductive strategies in perennial plants, and the role of plant reproduction as a driver of ecosystem dynamics. © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.