Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements

Chavana‐Bryant, Cecilia; Malhi, Yadvinder; Wu, Jin; Asner, Gregory P.; Αναστασίου, Αθανάσιος; Enquist, Brian J.; Cosio, Eric G.; Doughty, Christopher E.; Saleska, S. R.; Martin, Robert E.

doi:10.1111/nph.13853

Cited by 150 publications

(156 citation statements)

References 88 publications

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“…Some of most accurately predicted traits have no absorption features in the visible-to-near-infrared, but were instead estimated indirectly via constellation effects. LMA is consistently among the more accurately predicted traits using spectroscopy (Asner and Martin, 2008;Serbin et al, 2014;Chavana-Bryant et al, 2016), but is measured indirectly via its close coupling with water content and structural traits of leaves (Asner et al, 2011b). Silicon (Si) concentrations were well predicted by field spectroscopy, as recently reported by Smis et al (2014).…”

Section: Measuring Interspecific Variation In Leaf Traitsmentioning

confidence: 58%

On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

2017

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Abstract. Understanding the causes of variation in functional plant traits is a central issue in ecology, particularly in the context of global change. Spectroscopy is increasingly used for rapid and non-destructive estimation of foliar traits, but few studies have evaluated its accuracy when assessing phenotypic variation in multiple traits. Working with 24 chemical and physical leaf traits of six European tree species growing on strongly contrasting soil types (i.e. deep alluvium versus nearby shallow chalk), we asked (i) whether variability in leaf traits is greater between tree species or soil type, and (ii) whether field spectroscopy is effective at predicting intraspecific variation in leaf traits as well as interspecific differences. Analysis of variance showed that interspecific differences in traits were generally much stronger than intraspecific differences related to soil type, accounting for 25 % versus 5 % of total trait variation, respectively. Structural traits, phenolic defences and pigments were barely affected by soil type. In contrast, foliar concentrations of rock-derived nutrients did vary: P and K concentrations were lower on chalk than alluvial soils, while Ca, Mg, B, Mn and Zn concentrations were all higher, consistent with the findings of previous ecological studies. Foliar traits were predicted from 400 to 2500 nm reflectance spectra collected by field spectroscopy using partial least square regression, a method that is commonly employed in chemometrics. Pigments were best modelled using reflectance data from the visible region (400-700 nm), while all other traits were best modelled using reflectance data from the shortwave infrared region (1100-2500 nm). Spectroscopy delivered accurate predictions of species-level variation in traits. However, it was ineffective at detecting intraspecific variation in rock-derived nutrients (with the notable exception of P). The explanation for this failure is that rock-derived elements do not have absorption features in the 400-2500 nm region, and their estimation is indirect, relying on elemental concentrations covarying with structural traits that do have absorption features in that spectral region ("constellation effects"). Since the structural traits did not vary with soil type, it was impossible for our regression models to predict intraspecific variation in rock-derived nutrients via constellation effects. This study demonstrates the value of spectroscopy for rapid, non-destructive estimation of foliar traits across species, but highlights problems with predicting intraspecific variation indirectly. We discuss the implications of these findings for mapping functional traits by airborne imaging spectroscopy.

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Section: Measuring Interspecific Variation In Leaf Traitsmentioning

confidence: 58%

On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

2017

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“…Some studies [62] have found a dramatic increase in leaf NIR reflectance between July and August, which might be caused by differences in the leaf characteristics. Low-wax leaves display a continuous increase in NIR reflectance until maturity, while high-wax leaves show little increase in NIR reflectance [63]. Epicuticular waxes and thick cuticles could mask the effect of NIR increases caused by inter-cellular development in young high-wax leaves.…”

Section: Spectral Changes and Leaf Agingmentioning

confidence: 99%

Impacts of Leaf Age on Canopy Spectral Signature Variation in Evergreen Chinese Fir Forests

Song

et al. 2018

Remote Sensing

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Significant gaps exist in our knowledge of the impact of leaf aging on canopy signal variability, which limits our understanding of vegetation status based on remotely sensed data. To understand the effects of leaf aging at the leaf and canopy scales, a combination of field, remote-sensing and physical modeling techniques was adopted to assess the canopy spectral signals of evergreen Cunninghamia forests. We observed an approximately 10% increase in Near-Infrared (NIR) reflectance for new leaves and a 35% increase in NIR transmittance for mature leaves from May to October. When variations in leaf optical properties (LOPs) of only mature leaves, or both new and mature leaves were considered, the Geometric Optical and Radiative Transfer (GORT) model-simulated canopy reflectance trajectory was more consistent with Landsat observations (R 2 increased from 0.37 to 0.82~0.89 for NIR reflectance, and from 0.35 to 0.67~0.88 for EVI2, with a small RMSE (0.01 to 0.02)). This study highlights the importance of leaf age on leaf spectral signatures, and provides evidence of age-dependent LOPs that have important impacts on canopy reflectance in the NIR band and EVI2, which are used to monitor canopy dynamics and productivity, with important implications for RS and forest ecosystem ecology.

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“…In order to promote spectral based monitoring of similar kinds of vegetation systems, it may be useful to collect data throughout different phenological stages and identify time periods when the discrimination between key species is clearest. Characterizing the spectral changes through time would also be useful to interpret associated changes in vegetation biophysical/chemical properties; for example Chavana-Bryant et al [80] proposed a statistical model to monitor leaf age and related traits by tracing changes in their reflectance.…”

Section: Functional Traits As Ecosystem Monitoring Toolmentioning

confidence: 99%

Characterization of a Highly Biodiverse Floodplain Meadow Using Hyperspectral Remote Sensing within a Plant Functional Trait Framework

Punalekar¹,

Verhoef²,

Tatarenko³

et al. 2016

Remote Sensing

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Abstract:We assessed the potential for using optical functional types as effective markers to monitor changes in vegetation in floodplain meadows associated with changes in their local environment. Floodplain meadows are challenging ecosystems for monitoring and conservation because of their highly biodiverse nature. Our aim was to understand and explain spectral differences among key members of floodplain meadows and also characterize differences with respect to functional traits. The study was conducted on a typical floodplain meadow in UK (MG4-type, mesotrophic grassland type 4, according to British National Vegetation Classification). We compared two approaches to characterize floodplain communities using field spectroscopy. The first approach was sub-community based, in which we collected spectral signatures for species groupings indicating two distinct eco-hydrological conditions (dry and wet soil indicator species). The other approach was "species-specific", in which we focused on the spectral reflectance of three key species found on the meadow. One herb species is a typical member of the MG4 floodplain meadow community, while the other two species, sedge and rush, represent wetland vegetation. We also monitored vegetation biophysical and functional properties as well as soil nutrients and ground water levels. We found that the vegetation classes representing meadow sub-communities could not be spectrally distinguished from each other, whereas the individual herb species was found to have a distinctly different spectral signature from the sedge and rush species. The spectral differences between these three species could be explained by their observed differences in plant biophysical parameters, as corroborated through radiative transfer model simulations. These parameters, such as leaf area index, leaf dry matter content, leaf water content, and specific leaf area, along with other functional parameters, such as maximum carboxylation capacity and leaf nitrogen content, also helped explain the species' differences in functional dynamics. Groundwater level and soil nitrogen availability, which are important factors governing plant nutrient status, were also found to be significantly different for the herb/wetland species' locations. The study concludes that spectrally distinguishable species, typical for a highly biodiverse site such as a floodplain meadow, could potentially be used as target species to monitor vegetation dynamics under changing environmental conditions.

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Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements

Cited by 150 publications

References 88 publications

On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

Impacts of Leaf Age on Canopy Spectral Signature Variation in Evergreen Chinese Fir Forests

Characterization of a Highly Biodiverse Floodplain Meadow Using Hyperspectral Remote Sensing within a Plant Functional Trait Framework

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