Reflectance spectroscopy allows rapid, accurate and non‐destructive estimates of functional traits from pressed leaves

Kothari, Shan; Beauchamp‐Rioux, Rosalie; Laliberté, Étienne; Cavender‐Bares, Jeannine

doi:10.1111/2041-210x.13958

Cited by 18 publications

(27 citation statements)

References 75 publications

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“…1390 and 1880 nm; Figs S14–S16). The general pattern of high VIP along the green hump and at the red edge is shared by many traits across studies (Yang et al ., 2016; Ely et al ., 2019; Yan et al ., 2021; Kothari et al ., 2023). In general, visible and red edge reflectance are dominated by pigments and leaf structure (Richardson et al ., 2002).…”

Section: Discussionmentioning

confidence: 99%

“…The third source is an expanded version of the Dessain dataset previously described in Kothari et al . (2023), which mainly includes sunlit broadleaf trees, shrubs, and herbs from southern Québec and has a large overlap in species with the CABO data. Unlike LOPEX and ANGERS, most of the Dessain trait data were measured using the same protocols as the core CABO datasets.…”

Section: Methodsmentioning

confidence: 99%

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Predicting leaf traits across functional groups using reflectance spectroscopy

et al. 2023

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Summary Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non‐destructive estimates of leaf traits, but it remains unclear whether general trait‐spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least‐squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2 = 0.55–0.85; %RMSE = 12.7–19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting leaf traits across functional groups using reflectance spectroscopy

et al. 2023

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“…Researchers have often found that leaf chemical traits are estimated better from ground‐leaf spectra than from fresh‐leaf spectra (Couture et al, 2016; Kothari, Beauchamp‐Rioux, Laliberté, et al, 2022; Wang et al, 2020). On some CABO samples, we had also measured spectra after the samples were pressed like herbarium specimens, and again after they were dried and ground (Kothari, Beauchamp‐Rioux, Laliberté, et al, 2022). Among these samples, we again chose no more than ten samples per species ( n = 228; species = 66), and z‐standardized and subsampled bands.…”

Section: Dimensionality Of Spectramentioning

confidence: 99%

“…Curran, 1989) for interpretation. For example, across studies, many traits show high VIP across the green hump and the red edge (Ely et al, 2019; Kothari, Beauchamp‐Rioux, Laliberté, et al, 2022; Yan et al, 2021), which suggests that their estimation may be aided by their covariance with pigment contents or leaf structure. Nevertheless, assigning unambiguous interpretations to these patterns is often challenging because many components' features are shallow and overlapping.…”

Section: Estimating Plant Traits From Spectramentioning

confidence: 99%

“…But leaf‐level PLSR models can also let us predict (with varying accuracy) nutrients or isotopes (e.g. K, Ca, Mg; δ 15 N) that have little direct, measurable impact on leaf absorption within the VIS to SWIR range (Asner et al, 2011; Kothari, Beauchamp‐Rioux, Laliberté, et al, 2022; Nunes et al, 2017; Wang et al, 2020). Similarly, PLSR models applied to remotely sensed imagery can yield estimates of non‐leaf traits like wood density (Jucker et al, 2018), or even make reasonably accurate predictions of forest plots' dominant mycorrhizal associations (Fisher et al, 2016; Sousa et al, 2021).…”

Section: Estimating Plant Traits From Spectramentioning

confidence: 99%

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Plant spectra as integrative measures of plant phenotypes

Kothari

Schweiger

2022

Journal of Ecology

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1. Spectroscopy at the leaf or canopy scales is becoming one of the core tools of plant functional ecology. Remotely sensed reflectance spectra can allow ecologists to infer plant traits and strategies-and the community-or ecosystem-level processes they correlate with-continuously over unprecedented spatial scales.2. Because of the complex entanglement of structural and chemical factors that generate spectra, it can be tricky to understand exactly what phenotypic information they contain. We discuss common approaches to estimating plant traits from spectra-radiative transfer models and multivariate empirical models-and elaborate on their strengths and limitations in terms of the causal influences of various traits on the spectrum. Many chemical traits have broad, shallow, and overlapping absorption features, and we suggest that covariance among traits may have an important role in giving empirical models the flexibility to estimate such traits.3. While trait estimates from reflectance spectra have been used to test ecological hypotheses over the past 20 years, we review a growing body of research that uses spectra directly, without estimating specific traits. By treating positions of species in multidimensional spectral space as analogous to trait space, researchers can infer processes that structure plant communities using the information content of the full spectrum, which may be greater than any standard set of traits. We illustrate this power by showing that co-occurring grassland species are more separable in spectral space than in trait space and that the intrinsic dimensionality of spectral data is comparable to fairly comprehensive trait data sets. Nevertheless, using spectra this way may make it harder to interpret patterns in terms of specific biological processes. 4. Synthesis. Plant spectra integrate many aspects of plant function. The information in the spectrum can be distilled into estimates of specific traits, or the spectrum can be used in its own right. These two approaches may be complementary-the former being most useful when specific traits of interest are known in advance and reliable models exist to estimate them, and the latter being most useful in taking advantage of the information in the full spectrum under uncertainty about which aspects of function matter most.

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Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal

Blanchard,

Bruneau,

Laliberté

2024

American J of Botany

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PremiseSpectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data.MethodsUsing over 3500 leaf‐level spectral measurements, we evaluated whether foliar reflectance spectra (400–2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model.ResultsSpectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species.ConclusionsOur results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.

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Reflectance spectroscopy allows rapid, accurate and non‐destructive estimates of functional traits from pressed leaves

Cited by 18 publications

References 75 publications

Predicting leaf traits across functional groups using reflectance spectroscopy

Predicting leaf traits across functional groups using reflectance spectroscopy

Plant spectra as integrative measures of plant phenotypes

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal

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