Mind the gaps: horizontal canopy structure affects the relationship between taxonomic and spectral diversity

ABSTRACT

Spectral diversity (SD) in reflectance can be used to estimate plant taxonomic diversity (TD) according to the Spectral Variation Hypothesis (SVH). However, contrasting relationships between SD and TD have been reported by different studies. Indeed, multiple factors may affect SD, including spatial and spectral scales, vegetation characteristics and the adopted SD computational method. Here, we tested the SVH over 171 plots within a large and heterogeneous forest area in North-Eastern Italy using Sentinel-2 data, aiming at identifying possible factors affecting the strength and direction of SD-TD relationship. SD was determined using ‘biodivMapR’ (BD) and ‘rasterdiv’ (RD) R packages and 38 possible combinations of SD indices, at both α (within a community) and β (among communities) levels, and computational parameters accounting for spatial and spectral scales. Information on vegetation structure was either retrieved from ground-based or LiDAR data. A Random Forest approach was used to disentangle the relationships between SD, TD and vegetation structure, and to identify the best combination of SD computational parameters. At the α-level, we found negative relationship between TD and RD SD indices, which was mainly driven by the presence of gaps within the forest canopy. As regards BD, we found that this algorithm reduced background contribution on SD and was able to differentiate major forest types (broadleaves vs conifers), but derived α-SD indices were marginally correlated with α-TD. At the β-level, we observed a statistically significant positive correlation between BD SD indices and TD (maximum r = 0.24). Finally, we found stronger correlations and R² when SD indices were calculated using smaller computation windows and over a larger pixels extraction area. Our findings suggest that vegetation cover and structure play a major role, with respect to inter-species spectral differences, in determining α-SD, and that SD might better capture differences in species composition at the landscape-level rather than the richness of individual communities.

KEYWORDS:

• Diversity
• canopy gaps
• multispectral
• classification
• forest structure

Funding details

Valentina Olmo was supported by a PhD scholarship funded by the Autonomous Region of Friuli Venezia Giulia, Central Directorate of territory and infrastructures, Service of land, landscape and strategic planning (Convenzione Quadro art. 23 L.R. 20 marzo 2000, n. 7 tra la Regione Autonoma Friuli Venezia Giulia e l’Università degli Studi di Trieste). Francesco Petruzzellis was supported by the funding PON Ricerca e Innovazione D.M. 1062/21 – Contratti di ricerca, from the Italian Ministry of University (MUR). This research activity was also supported by the Italian National Recovery Plan (PNRR) through the National Biodiversity Future Centre (NBFC - Missione 4 Componente 2, Investimento 1.4 – D.D. 1034 17/06/2022, CN00000033). Views and opinions expressed are those of the authors only and do not necessarily reflect those of donors nor can these lasts be held responsible for them. The Italian Forest Inventory data were released under the Creative Commons 4.0 and were downloaded from the INFC database at https://inventarioforestale.org/.

Acronyms

B	=	Buffer pixels extraction area
BD	=	biodivMapR
CC	=	Canopy Closure
CV	=	Coefficient of Variation
F	=	Forest pixels extraction area
FVG	=	Friuli Venezia Giulia Region
HVH	=	Height Variation Hypothesis
INFC	=	Inventory of Forests and forest Carbon pools
IRECI	=	Inverted Red-Edge Chlorophyll Index
NDVI	=	Normalized Difference Vegetation Index
PCA	=	Principal Component Analysis
RD	=	rasterdiv
RF	=	Random Forests
RMSE	=	Root Mean Square Error
SD	=	Spectral Diversity
SS	=	Spectral Species
SVH	=	Spectral Variation Hypothesis
TD	=	Taxonomic Diversity

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability statement

Data will be made available on request, but the authors do not have the permission to share the exact coordinates of INFC plots.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/01431161.2024.2334776.

Additional information

Funding

This work was supported by the Italian National Recovery Plan (PNRR) [National Biodiversity Future Centre (NBFC) - Missione 4 Componente 2, Investimento 1.4 – D.D. 1034 17/06/2022, CN00000033]; Italian Ministry of University (MUR) [PON Ricerca e Innovazione D.M. 1062/21]; Regione Autonoma Friuli Venezia Giulia [Convenzione Quadro art. 23 L.R. 20 marzo 2000, n. 7].