Successional and Intraspecific Variations in Leaf Traits, Spectral Reflectance Indices and Herbivory in a Brazilian Tropical Dry Forest

Leaf traits are good indicators of ecosystem functioning and can affect herbivory and leaf reflectance patterns, allowing a better understanding of changes in environmental conditions, such those observed during forest natural regeneration. The aim of this study was to evaluate the intraspecific variation in leaf traits and their influence on the pattern of herbivory and leaf reflectance in three species distributed along a successional gradient (early, intermediate and late stages) in a tropical dry forest (TDF) in northern Minas Gerais, Brazil. We sampled individuals of the following abundant tree species that occurred in multiple successional stages: Cenostigma pluviosum, Handroanthus ochraceus, and Tabebuia reticulata. We collected 10 leaves from each tree to determine the contents of chlorophyll a, b, and total, carotenoids and water, as well as the percentage of leaf area removed by herbivores and leaf specific mass (LSM). We also measured five spectral reflectance indices (Normalized Difference Vegetation Index-NDVI, Simple Ratio-SR, modified Normalized Difference-nND, modified SR-mSR and Water Index-WI) using a portable spectrometer. Our results showed intraspecific differences in most leaf traits along the successional gradient, suggesting that local adaptation may play an important role in plant community assembly. However, herbivory only differed for H. ochraceus in early and intermediate stages, but it was not affected by the leaf traits considered here. Spectral reflectance indices also differed among successional stage for all species together and for each species separately, except for T. reticulata in intermediate and late stages. Thus, leaf spectral signatures may be an important tool to the remote detection of different successional stages in TDFs, with implications for forest management.

Leaf reflectance and functional traits as environmental indicators of urban dust deposition

Background How to quickly predict and evaluate urban dust deposition is the key to the control of urban atmospheric environment. Here, we focus on changes of plant reflectance and plant functional traits due to dust deposition, and develop a prediction model of dust deposition based on these traits. Results The results showed that (1) The average dust deposition per unit area of Ligustrum quihoui leaves was significantly different among urban environments (street (18.1001 g/m2), community (14.5597 g/m2) and park (9.7661 g/m2)). Among different urban environments, leaf reflectance curves tends to be consistent, but there were significant differences in leaf reflectance values (park (0.052–0.585) > community (0.028–0.477) > street (0.025–0.203)). (2) There were five major reflection peaks and five major absorption valleys. (3) The spectral reflectances before and after dust removal were significantly different (clean leaves > dust-stagnant leaves). 695 ~ 1400 nm was the sensitive range of spectral response. (4) Dust deposition has significant influence on slope and position of red edge. Red edge slope was park > community > street. After dust deposition, the red edge position has obviously “blue shift”. The moving distance of the red edge position increases with the increase of dust deposition. The forecast model of dust deposition amount established by simple ratio index (y = 2.517x + 0.381, R2 = 0.787, RMSE (root-mean-square error) = 0.187. In the model, y refers to dust retention, x refers to simple ratio index.) has an average accuracy of 99.98%. (5) With the increase of dust deposition, the specific leaf area and chlorophyll content index decreased gradually. The leaf dry matter content, leaf tissue density and leaf thickness increased gradually. Conclusion In the dust-polluted environment, L. quihoui generally presents a combination of characters with lower specific leaf area, chlorophyll content index, and higher leaf dry matter content, leaf tissue density and leaf thickness. Leaf reflectance spectroscopy and functional traits have been proved to be effective in evaluating the changes of urban dust deposition.

Leaf reflectance can surrogate foliar economics better than physiological traits across macrophyte species

Background Macrophytes are key players in aquatic ecosystems diversity, but knowledge on variability of their functional traits, among and within species, is still limited. Remote sensing is a high-throughput, feasible option for characterizing plant traits at different scales, provided that reliable spectroscopy models are calibrated with congruous empirical data, but existing applications are biased towards terrestrial plants. We sampled leaves from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400–2500 nm) and leaf traits (dealing with photophysiology, pigments, and structure). We explored optimal spectral band combinations and established non-parametric reflectance-based models for selected traits, eventually showing how airborne hyperspectral data could capture spatial–temporal macrophyte variability. Results Our key finding is that structural—leaf dry matter content, leaf mass per area—and biochemical—chlorophyll-a content and chlorophylls to carotenoids ratio—traits can be surrogated by leaf reflectance with normalized error under 17% across macrophyte species. On the other hand, the performance of reflectance-based models for photophysiological traits substantively varies, depending on macrophyte species and target parameters. Conclusions Our main results show the link between leaf reflectance and leaf economics (structure and biochemistry) for aquatic plants, thus envisioning a crucial role for remote sensing in enhancing the level of detail of macrophyte functional diversity analysis to intra-site and intra-species scales. At the same time, we highlighted some difficulties in establishing a general link between reflectance and photosynthetic performance under high environmental heterogeneity, potentially opening further investigation directions.

Aerial high-throughput phenotyping of peanut leaf area index and lateral growth

Leaf area index (LAI) is the ratio of the total one-sided leaf area to the ground area, whereas lateral growth (LG) is the measure of canopy expansion. They are indicators for light capture, plant growth, and yield. Although LAI and LG can be directly measured, this is time consuming. Healthy leaves absorb in the blue and red, and reflect in the green regions of the electromagnetic spectrum. Aerial high-throughput phenotyping (HTP) may enable rapid acquisition of LAI and LG from leaf reflectance in these regions. In this paper, we report novel models to estimate peanut (Arachis hypogaea L.) LAI and LG from vegetation indices (VIs) derived relatively fast and inexpensively from the red, green, and blue (RGB) leaf reflectance collected with an unmanned aerial vehicle (UAV). In addition, we evaluate the models’ suitability to identify phenotypic variation for LAI and LG and predict pod yield from early season estimated LAI and LG. The study included 18 peanut genotypes for model training in 2017, and 8 genotypes for model validation in 2019. The VIs included the blue green index (BGI), red-green ratio (RGR), normalized plant pigment ratio (NPPR), normalized green red difference index (NGRDI), normalized chlorophyll pigment index (NCPI), and plant pigment ratio (PPR). The models used multiple linear and artificial neural network (ANN) regression, and their predictive accuracy ranged from 84 to 97%, depending on the VIs combinations used in the models. The results concluded that the new models were time- and cost-effective for estimation of LAI and LG, and accessible for use in phenotypic selection of peanuts with desirable LAI, LG and pod yield.

Leaf Surface Reflectance Does Not Affect Biophysical Traits Modelling from VIS-NIR Spectra in Plants with Sparsely Distributed Trichomes

In this study, we examine leaf reflectance as the main optical property used in remote sensing of vegetation. The total leaf reflectance consists of two main components: a diffuse component, originating from the leaf interior, and a component reflected directly from the leaf surface. The latter contains specular (mirror-like) reflectance (SR) and surface particle scattering, driven by the surface roughness. Our study aimed to (1) reveal the effects of key leaf structural traits on SR in 400–2500 nm, and (2) compare the performance of PLSR models of leaf biophysical properties based on the total reflectance and SR removal reflectance. Four Arabidopsis thaliana structural surface mutants and six Hieracium species differing in trichome properties were studied. PCA did not reveal any systematic effect of trichome density, length, and morphology on SR. Therefore, the results do not support the hypothesis that leaves with denser and longer trichomes have lower SR and higher total reflectance than the smooth leaves. SR removal did not remarkably improve PLSR models of biophysical traits (up to 2% of RMSE). Thus, in herbaceous dorsiventral leaves with relatively sparse trichomes of various morphology and without apparent waxy surface, we cannot confirm that SR removal significantly improves biophysical trait prediction.

Determination of the Optical Properties of Thylakoid Membranes from Whole Leaf Reflectance Measurements Using a Capacitor and Unbound Electron Model

This paper demonstrates that a capacitor equivalent along with unbound electrons can be used to model thylakoid membranes in grana stacks. From whole leaf reflectance measurements at normal incidences at 660nm wavelength and taken from the literature, refractive indices are obtained from the Fresnel equation for transverse electric (TE) and transverse magnetic (TM) polarization. The TE and TM polarizations for external reflectance depict the Brewster angle at which the magnitude of the reflected electric vector is zero; the internal reflections show that there is a narrow angle window of about 10 degrees before the internally refracted light goes into critical angle. The clustering and separation of reflection measurements with angle of incidence is explained using Fresnel equation; the cross-over angle is located beyond the Brewster angle for internal reflection. The predicted relaxation times from a capacitor and unbound electron model gave favorable comparisons against commonly reported fluorescence times in the 0.1 to 1 ns range (our results gave 0.5-0.8 ns). The di-electric constant for the membrane is estimated to be 5. The stacking number (number of grana layers) is consistent with the light penetration depth (skin depth). The magnetic permeability was shown to be close to that of vacuum and therefore the thylakoid lacks any magnetic properties as would be expected for such a transparent media. An in-vivo estimate based on thermal equilibrium of molecules for the permanent dipole moment of the chlorophyll molecule gave 2,025D (Debye).