Partitioning plant spectral diversity into alpha and beta components

Abstract

Plant spectral diversity – how plants differentially interact with solar radiation – is an integrator of plant chemical, structural, and taxonomic diversity that can be remotely sensed. We propose to measure spectral diversity as spectral variance, which allows the partitioning of the spectral diversity of a region, called spectral gamma (γ) diversity, into additive alpha (α; within communities) and beta (β; among communities) components. Our method calculates the contributions of individual bands or spectral features to spectral γ‐, β‐, and α‐diversity, as well as the contributions of individual plant communities to spectral diversity. We present two case studies illustrating how our approach can identify 'hotspots’ of spectral α‐diversity within a region, and discover spectrally unique areas that contribute strongly to β‐diversity. Partitioning spectral diversity and mapping its spatial components has many applications for conservation since high local diversity and distinctiveness in composition are two key criteria used to determine the ecological value of ecosystems.

Quantitative Airborne Inventories in Dense Tropical Forest Using Imaging Spectroscopy

“…For the same number of crowns in the learning set (34 crowns, Table 1), the F-measure for Vouacapoua americana reached of 72.8% while it was only 36.7% for Symphonia sp.1 (Table 3, RDA). In addition, because classification results were so variable across species (Figure 4), we examined correlations between the predictability of the species (F-measure), the size of the focal species training set, the intra-specific spectral variance-sensu [60]-and the dispersion of the Mahalanobis distance values. The only significant correlation detected was with the size of the training set (number of crowns or number pixels) highlighting that the detectability of a species is difficult to predict (Appendix F, Table A5).…”

“…Number of pixels is given per species. Intra-group variance was computed as proposed by [60]. Species mean Mahalanobis distance was computed as described in Section 2.6.3.…”

Applying Remote Sensing to Biodiversity Science

“…Like taxonomic, functional, and phylogenetic diversity, spectral diversity can be calculated in many different ways. Spectral alpha diversity metrics include the coefficient of variation of spectral indices (Oindo and Skidmore 2002) or spectral bands among pixels (Hall et al 2010;Gholizadeh et al 2018Gholizadeh et al , 2019Wang et al 2018, the convex hull volume (Dahlin 2016) and the convex hull area (Gholizadeh et al 2018) of pixels in spectral feature space, the mean distance of pixels from the spectral centroid (Rocchini et al 2010), the number of spectrally distinct clusters or "spectral species" in ordination space (Féret and Asner 2014), and spectral variance (Laliberté et al 2019). Schweiger et al (2018) applied q D(TM) to species mean spectra and to individual pixels extracted at random from high-resolution proximal RS data.…”

“…In contrast to traditional diversity metrics, spectral diversity (alpha and beta) is only beginning to receive attention in biodiversity studies (Rocchini et al 2018). Although different approaches have been proposed (Schmidtlein et al 2007;Féret and Asner 2014;Rocchini et al 2018;Laliberté et al 2019), the estimation and mapping of dissimilarities in spectral composition (i.e., the variation among pixels) is similar to traditional estimations of beta diversity. For example, Laliberté et al (2019) adapted the total community composition variance approach (Legendre and De Cáceres 2013) to estimate spectral diversity as spectral variance, partitioning the spectral diversity of a region (gamma diversity) into additive alpha and beta diversity components.…”

Foliar sampling with an unmanned aerial system (UAS) reveals spectral and functional trait differences within tree crowns

“…Spectral profiles thus capture key differences in foliar chemistry, morphology, life-history strategies, and responses to environmental variation, which have evolved over time and reflect ecological strategies (Cavender-Bares et al 2017;Ustin and Gamon 2010). Ecological applications of imaging spectroscopy include mapping of functional traits (e.g., Asner et al 2011;Singh et al 2015;; the differentiation of plant communities (e.g., Foster and Townsend 2004;Schweiger et al 2017), species (e.g., Asner and Martin 2009;Clark et al 2005;Lopatin et al 2017), and genotypes (e.g., Madritch et al 2014); the detection of disease (Herrmann et al 2018; e.g., Pontius et al 2005) and stress symptoms (e.g., Asner et al 2016;Singh et al 2016); and the estimation of other dimensions of plant biodiversity based on spectral diversity (e.g., Draper et al 2019;Féret and Asner 2014;Laliberté et al 2020;Palmer et al 2002;Rocchini et al 2010;Schweiger et al 2018;Wang et al 2018). Imaging spectrometers are regularly mounted on airplanes, including the National Aeronautics and Space Administration's (NASA) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; Green et al 1998) and the European Space Agency's (ESA) Airborne Prism Experiment (APEX; Schaepman et al 2015) instruments, experimental platforms, including mobile and stationary tram systems (Gamon et al 2006), and flux towers (Gamon 2015).…”