Sources of variability in canopy reflectance and the convergent properties of plants

Scaling up functional traits for ecosystem services with remote sensing: concepts and methods

Ecosystem service‐based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot‐level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community‐weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.

Temporal dynamics of spectral bioindicators evidence biological and ecological differences among functional types in a cork oak open woodland

The application of spectral vegetation indices for the purpose of vegetation monitoring and modeling increased largely in recent years. Nonetheless, the interpretation of biophysical properties of vegetation through their spectral signature is still a challenging task. This is particularly true in Mediterranean oak forest characterized by a high spatial and temporal heterogeneity. In this study, the temporal dynamics of vegetation indices expected to be related with green biomass and photosynthetic efficiency were compared for the canopy of trees, the herbaceous layer, and two shrub species: cistus (Cistus salviifolius) and ulex (Ulex airensis). coexisting in a cork oak woodland. All indices were calculated from in situ measurements with a FieldSpec3 spectroradiometer (ASD Inc., Boulder, USA). Large differences emerged in the temporal trends and in the correlation between climate and vegetation indices. The relationship between spectral indices and temperature, radiation, and vapor pressure deficit for cork oak was opposite to that observed for the herbaceous layer and cistus. No correlation was observed between rainfall and vegetation indices in cork oak and ulex, but in the herbaceous layer and in the cistus, significant correlations were found. The analysis of spectral vegetation indices with fraction of absorbed PAR (fPAR) and quantum yield of chlorophyll fluorescence (ΔF/Fm') evidenced strongest relationships with the indices Normalized Difference Water Index (NDWI) and Photochemical Reflectance Index (PRI)512, respectively. Our results, while confirms the ability of spectral vegetation indices to represent temporal dynamics of biophysical properties of vegetation, evidence the importance to consider ecosystem composition for a correct ecological interpretation of results when the spatial resolution of observations includes different plant functional types.

Hemiparasite--host plant interactions in a fragmented landscape assessed via imaging spectroscopy and LiDAR

Species interactions are susceptible to anthropogenic changes in ecosystems, but this has been poorly investigated in a spatially explicit manner in the case of plant parasitism, such as the omnipresent hemiparasitic mistletoe-host plant interactions. Analyzing such interactions at a large spatial scale may advance our understanding of parasitism patterns over complex landscapes. Combining high-resolution airborne imaging spectroscopy and LiDAR, we studied hemiparasite incidence within and among tree host stands to examine the prevalence and spatial distribution of hemiparasite load in ecosystems. Specifically, we aimed to assess: (1) detection accuracy of mistletoes on their oak hosts; (2) hemiparasitism prevalence within host tree canopies depending on tree height, and (3) spatial variation in hemiparasitism across fragmented woodlands, in a low-diversity mediterranean oak woodland in California, USA. We identified mistletoe infestations with 55-96% accuracy, and detected significant differences in remote-sensed spectra between oak trees with and without mistletoe infestation. We also found that host canopy height had little influence on infestation degree, whereas landscape-level variation showed consistent; non-random patterns: isolated host trees had twice the infestation load than did trees located at the core of forest fragments. Overall, we found that canopy exposure (i.e., lower canopy density or proximity to forest edge) is more important than canopy height for mistletoe infestation, and that by changing landscape structure, parasitic prevalence increased with woodland fragmentation. We conclude that reducing fragmentation in oak woodlands will minimize anthropogenic impact on mistletoe infestation at the landscape level. We argue that advanced remote sensing technology can provide baselines to quantitatively analyze and monitor parasite-host trajectories in light of global environmental change, and that this is a promising approach to be further tested in other temperate and tropical forests.

Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species

The morphological and biochemical properties of plant canopies are strong predictors of photosynthetic capacity and nutrient cycling. Remote sensing research at the leaf and canopy scales has demonstrated the ability to characterize the biochemical status of vegetation canopies using reflectance spectroscopy, including at the leaf level and canopy level from air- and spaceborne imaging spectrometers. We developed a set of accurate and precise spectroscopic calibrations for the determination of leaf chemistry (contents of nitrogen, carbon, and fiber constituents), morphology (leaf mass per area, Marea), and isotopic composition (δ15N) of temperate and boreal tree species using spectra of dried and ground leaf material. The data set consisted of leaves from both broadleaf and needle-leaf conifer species and displayed a wide range in values, determined with standard analytical approaches: 0.7–4.4% for nitrogen (Nmass), 42–54% for carbon (Cmass), 17–58% for fiber (acid-digestible fiber, ADF), 7–44% for lignin (acid-digestible lignin, ADL), 3–31% for cellulose, 17–265 g/m2 for Marea, and −9.4‰ to 0.8‰ for δ15N. The calibrations were developed using a partial least-squares regression (PLSR) modeling approach combined with a novel uncertainty analysis. Our PLSR models yielded model calibration (independent validation shown in parentheses) R2 and the root mean square error (RMSE) values, respectively, of 0.98 (0.97) and 0.10% (0.13%) for Nmass, R2 = 0.77 (0.73) and RMSE = 0.88% (0.95%) for Cmass, R2 = 0.89 (0.84) and RMSE = 2.8% (3.4%) for ADF, R2 = 0.77 (0.69) and RMSE = 2.4% (3.9%) for ADL, R2 = 0.77 (0.72) and RMSE = 1.4% (1.9%) for leaf cellulose, R2 = 0.62 (0.60) and RMSE = 0.91‰ (1.5‰) for δ15N, and R2 = 0.88 (0.87) with RMSE = 17.2 g/m2 (22.8 g/m2) for Marea. This study demonstrates the potential for rapid and accurate estimation of key foliar traits of forest canopies that are important for ecological research and modeling activities, with a single calibration equation valid over a wide range of northern temperate and boreal species and leaf physiognomies. The results provide the basis to characterize important variability between and within species, and across ecological gradients using a rapid, cost-effective, easily replicated method.

Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (M(area)), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ15N) as well as pixel-wise uncertainties in their estimates based on error propagation in the analytical methods. Both M(area) and %N PLSR models had a R2 > 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with R2 > 0.65 and carbon and cellulose with R2 > 0.45. Although R2 of %C and cellulose were lower than M(area) and %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ15N was the lowest (R2 = 0.48, RMSE = 0.95 per thousand), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first-of-its-kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub-boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non-discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.

Antarctic moss stress assessment based on chlorophyll content and leaf density retrieved from imaging spectroscopy data

The health of several East Antarctic moss-beds is declining as liquid water availability is reduced due to recent environmental changes. Consequently, a noninvasive and spatially explicit method is needed to assess the vigour of mosses spread throughout rocky Antarctic landscapes. Here, we explore the possibility of using near-distance imaging spectroscopy for spatial assessment of moss-bed health. Turf chlorophyll a and b, water content and leaf density were selected as quantitative stress indicators. Reflectance of three dominant Antarctic mosses Bryum pseudotriquetrum, Ceratodon purpureus and Schistidium antarctici was measured during a drought-stress and recovery laboratory experiment and also with an imaging spectrometer outdoors on water-deficient (stressed) and well-watered (unstressed) moss test sites. The stress-indicating moss traits were derived from visible and near infrared turf reflectance using a nonlinear support vector regression. Laboratory estimates of chlorophyll content and leaf density were achieved with the lowest systematic/unsystematic root mean square errors of 38.0/235.2 nmol g(-1) DW and 0.8/1.6 leaves mm(-1) , respectively. Subsequent combination of these indicators retrieved from field hyperspectral images produced small-scale maps indicating relative moss vigour. Once applied and validated on remotely sensed airborne spectral images, this methodology could provide quantitative maps suitable for long-term monitoring of Antarctic moss-bed health.

Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses - a review

Current methods of in-house plant phenotyping are providing a powerful new tool for plant biology studies. The self-constructed and commercial platforms established in the last few years, employ non-destructive methods and measurements on a large and high-throughput scale. The platforms offer to certain extent, automated measurements, using either simple single sensor analysis, or advanced integrative simultaneous analysis by multiple sensors. However, due to the complexity of the approaches used, it is not always clear what such forms of plant phenotyping can offer the potential end-user, i.e. plant biologist. This review focuses on imaging methods used in the phenotyping of plant shoots including a brief survey of the sensors used. To open up this topic to a broader audience, we provide here a simple introduction to the principles of automated non-destructive analysis, namely RGB, chlorophyll fluorescence, thermal and hyperspectral imaging. We further on present an overview on how and to which extent, the automated integrative in-house phenotyping platforms have been used recently to study the responses of plants to various changing environments.

A global assessment of forest surface albedo and its relationships with climate and atmospheric nitrogen deposition

We present a global assessment of the relationships between the short-wave surface albedo of forests, derived from the MODIS satellite instrument product at 0.5° spatial resolution, with simulated atmospheric nitrogen deposition rates (Ndep ), and climatic variables (mean annual temperature Tm and total annual precipitation P), compiled at the same spatial resolution. The analysis was performed on the following five forest plant functional types (PFTs): evergreen needle-leaf forests (ENF); evergreen broad-leaf forests (EBF); deciduous needle-leaf forests (DNF); deciduous broad-leaf forests (DBF); and mixed-forests (MF). Generalized additive models (GAMs) were applied in the exploratory analysis to assess the functional nature of short-wave surface albedo relations to environmental variables. The analysis showed evident correlations of albedo with environmental predictors when data were pooled across PFTs: Tm and Ndep displayed a positive relationship with forest albedo, while a negative relationship was detected with P. These correlations are primarily due to surface albedo differences between conifer and broad-leaf species, and different species geographical distributions. However, the analysis performed within individual PFTs, strengthened by attempts to select 'pure' pixels in terms of species composition, showed significant correlations with annual precipitation and nitrogen deposition, pointing toward the potential effect of environmental variables on forest surface albedo at the ecosystem level. Overall, our global assessment emphasizes the importance of elucidating the ecological mechanisms that link environmental conditions and forest canopy properties for an improved parameterization of surface albedo in climate models.

A review of imaging techniques for plant phenotyping

Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.

Functional and biological diversity of foliar spectra in tree canopies throughout the Andes to Amazon region

Spectral properties of foliage express fundamental chemical interactions of canopies with solar radiation. However, the degree to which leaf spectra track chemical traits across environmental gradients in tropical forests is unknown. We analyzed leaf reflectance and transmittance spectra in 2567 tropical canopy trees comprising 1449 species in 17 forests along a 3400-m elevation and soil fertility gradient from the Amazonian lowlands to the Andean treeline. We developed quantitative links between 21 leaf traits and 400-2500-nm spectra, and developed classifications of tree taxa based on spectral traits. Our results reveal enormous inter-specific variation in spectral and chemical traits among canopy trees of the western Amazon. Chemical traits mediating primary production were tightly linked to elevational changes in foliar spectral signatures. By contrast, defense compounds and rock-derived nutrients tracked foliar spectral variation with changing soil fertility in the lowlands. Despite the effects of abiotic filtering on mean foliar spectral properties of tree communities, the spectra were dominated by phylogeny within any given community, and spectroscopy accurately classified 85-93% of Amazonian tree species. Our findings quantify how tropical tree canopies interact with sunlight, and indicate how to measure the functional and biological diversity of forests with spectroscopy.

On the use of leaf spectral indices to assess water status and photosynthetic limitations in Olea europaea L. during water-stress and recovery

Diffusional limitations to photosynthesis, relative water content (RWC), pigment concentrations and their association with reflectance indices were studied in olive (Olea europaea) saplings subjected to water-stress and re-watering. RWC decreased sharply as drought progressed. Following rewatering, RWC gradually increased to pre-stress values. Photosynthesis (A), stomatal conductance (gs), mesophyll conductance (gm), total conductance (gt), photochemical reflectance index (PRI), water index (WI) and relative depth index (RDI) closely followed RWC. In contrast, carotenoid concentration, the carotenoid to chlorophyll ratio, water content reflectance index (WCRI) and structural independent pigment index (SIPI) showed an opposite trend to that of RWC. Photosynthesis scaled linearly with leaf conductance to CO2; however, A measured under non-photorespiratory conditions (A1%O2) was approximately two times greater than A measured at 21% [O2], indicating that photorespiration likely increased in response to drought. A1%O2 also significantly correlated with leaf conductance parameters. These relationships were apparent in saturation type curves, indicating that under non-photorespiratory conditions, CO2 conductance was not the major limitations to A. PRI was significant correlated with RWC. PRI was also very sensitive to pigment concentrations and photosynthesis, and significantly tracked all CO2 conductance parameters. WI, RDI and WCRI were all significantly correlated with RWC, and most notably to leaf transpiration. Overall, PRI correlated more closely with carotenoid concentration than SIPI; whereas WI tracked leaf transpiration more effectively than RDI and WCRI. This study clearly demonstrates that PRI and WI can be used for the fast detection of physiological traits of olive trees subjected to water-stress.

Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

Fine-scale biodiversity is increasingly recognized as important to ecosystem-level processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.

Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset

Biochemical and structural leaf properties such as chlorophyll content (Chl), nitrogen content (N), leaf water content (LWC), and specific leaf area (SLA) have the benefit to be estimated through nondestructive spectral measurements. Current practices, however, mainly focus on a limited amount of wavelength bands while more information could be extracted from other wavelengths in the full range (400-2500nm) spectrum. In this research, leaf characteristics were estimated from a field-based multi-species dataset, covering a wide range in leaf structures and Chl concentrations. The dataset contains leaves with extremely high Chl concentrations (>100μgcm(-2)), which are seldom estimated. Parameter retrieval was conducted with the machine learning regression algorithm Gaussian Processes (GP), which is able to perform adaptive, nonlinear data fitting for complex datasets. Moreover, insight in relevant bands is provided during the development of a regression model. Consequently, the physical meaning of the model can be explored. Best estimates of SLA, LWC and Chl yielded a best obtained normalized root mean square error of 6.0%, 7.7%, 9.1%, respectively. Several distinct wavebands were chosen across the whole spectrum. A band in the red edge (710nm) appeared to be most important for the estimation of Chl. Interestingly, spectral features related to biochemicals with a structural or carbon storage function (e.g. 1090, 1550, 1670, 1730nm) were found important not only for estimation of SLA, but also for LWC, Chl or N estimation. Similar, Chl estimation was also helped by some wavebands related to water content (950, 1430nm) due to correlation between the parameters. It is shown that leaf parameter retrieval by GP regression is successful, and able to cope with large structural differences between leaves.

Remotely-sensed indicators of N-related biomass allocation in Schoenoplectus acutus

Coastal marshes depend on belowground biomass of roots and rhizomes to contribute to peat and soil organic carbon, accrete soil and alleviate flooding as sea level rises. For nutrient-limited plants, eutrophication has either reduced or stimulated belowground biomass depending on plant biomass allocation response to fertilization. Within a freshwater wetland impoundment receiving minimal sediments, we used experimental plots to explore growth models for a common freshwater macrophyte, Schoenoplectus acutus. We used N-addition and control plots (4 each) to test whether remotely sensed vegetation indices could predict leaf N concentration, root:shoot ratios and belowground biomass of S. acutus. Following 5 months of summer growth, we harvested whole plants, measured leaf N and total plant biomass of all above and belowground vegetation. Prior to harvest, we simulated measurement of plant spectral reflectance over 164 hyperspectral Hyperion satellite bands (350-2500 nm) with a portable spectroradiometer. N-addition did not alter whole plant, but reduced belowground biomass 36% and increased aboveground biomass 71%. We correlated leaf N concentration with known N-related spectral regions using all possible normalized difference (ND), simple band ratio (SR) and first order derivative ND (FDN) and SR (FDS) vegetation indices. FDN(1235, 549) was most strongly correlated with leaf N concentration and also was related to belowground biomass, the first demonstration of spectral indices and belowground biomass relationships. While S. acutus exhibited balanced growth (reduced root:shoot ratio with respect to nutrient addition), our methods also might relate N-enrichment to biomass point estimates for plants with isometric root growth. For isometric growth, foliar N indices will scale equivalently with above and belowground biomass. Leaf N vegetation indices should aid in scaling-up field estimates of biomass and assist regional monitoring.

The effect of differential growth rates across plants on spectral predictions of physiological parameters

Leaves of various ages and positions in a plant's canopy can present distinct physiological, morphological and anatomical characteristics, leading to complexities in selecting a single leaf for spectral representation of an entire plant. A fortiori, as growth rates between canopies differ, spectral-based comparisons across multiple plants--often based on leaves' position but not age--becomes an even more challenging mission. This study explores the effect of differential growth rates on the reflectance variability between leaves of different canopies, and its implication on physiological predictions made by widely-used spectral indices. Two distinct irrigation treatments were applied for one month, in order to trigger the formation of different growth rates between two groups of grapevines. Throughout the experiment, the plants were physiologically and morphologically monitored, while leaves from every part of their canopies were spectrally and histologically sampled. As the control vines were constantly developing new leaves, the water deficit plants were experiencing growth inhibition, resulting in leaves of different age at similar nodal position across the treatments. This modification of the age-position correlation was characterized by a near infrared reflectance difference between younger and older leaves, which was found to be exponentially correlated (R(2) = 0.98) to the age-dependent area of intercellular air spaces within the spongy parenchyma. Overall, the foliage of the control plant became more spectrally variable, creating complications for intra- and inter-treatment leaf-based comparisons. Of the derived indices, the Structure-Insensitive Pigment Index (SIPI) was found indifferent to the age-position effect, allowing the treatments to be compared at any nodal position, while a Normalized Difference Vegetation Index (NDVI)-based stomatal conductance prediction was substantially affected by differential growth rates. As various biotic and abiotic factors may form distinctions in growth, future precision agriculture studies should consider its spectral effect on physiological predictions.

Using leaf optical properties to detect ozone effects on foliar biochemistry

Efficient methods for accurate and meaningful high-throughput plant phenotyping are limiting the development and breeding of stress-tolerant crops. A number of emerging techniques, specifically remote sensing methods, have been identified as promising tools for plant phenotyping. These remote sensing methods can be used to accurately and rapidly relate variations in leaf optical properties with important plant characteristics, such as chemistry, morphology, and photosynthetic properties at the leaf and canopy scales. In this study, we explored the potential to utilize optical (λ = 500-2,400 nm) near-surface remote sensing reflectance spectroscopy to evaluate the effects of ozone pollution on photosynthetic capacity of soybean (Glycine max Merr.). The research was conducted at the Soybean Free Air Concentration Enrichment (SoyFACE) facility where we subjected plants to ambient (44 nL L(-1)) and elevated ozone (79-82 nL L(-1) target) concentrations throughout the growing season. Exposure to elevated ozone resulted in a significant loss of productivity, with the ozone-treated plants displaying a ~30 % average decrease in seed yield. From leaf reflectance data, it was also clear that elevated ozone decreased leaf nitrogen and chlorophyll content as well as the photochemical reflectance index (PRI), an optical indicator of the epoxidation state of xanthophyll cycle pigments and thus physiological status. We assessed the potential to use leaf reflectance properties and partial least-squares regression (PLSR) modeling as an alternative, rapid approach to standard gas exchange for the estimation of the maximum rates of RuBP carboxylation (V c,max), an important parameter describing plant photosynthetic capacity. While we did not find a significant impact of ozone fumigation on V c,max, standardized to a reference temperature of 25 °C, the PLSR approach provided accurate and precise estimates of V c,max across ambient plots and ozone treatments (r (2) = 0.88 and RMSE = 13.4 μmol m(-2) s(-1)) based only on the variation in leaf optical properties and despite significant variability in leaf nutritional status. The results of this study illustrate the potential for combining the phenotyping methods used here with high-throughput genotyping methods as a promising approach for elucidating the basis for ozone tolerance in sensitive crops.

Assessing leaf photoprotective mechanisms using terrestrial LiDAR: towards mapping canopy photosynthetic performance in three dimensions

Terrestrial laser scanning (TLS) data allow spatially explicit (x, y, z) laser return intensities to be recorded throughout a plant canopy, which could considerably improve our understanding of how physiological processes vary in three-dimensional space. However, the utility of TLS data for the quantification of plant physiological properties remains largely unexplored. Here, we test whether the laser return intensity of green (532-nm) TLS correlates with changes in the de-epoxidation state of the xanthophyll cycle and photoprotective non-photochemical quenching (NPQ), and compare the ability of TLS to quantify these parameters with the passively measured photochemical reflectance index (PRI). We exposed leaves from five plant species to increasing light intensities to induce NPQ and de-epoxidation of violaxanthin (V) to antheraxanthin (A) and zeaxanthin (Z). At each light intensity, the green laser return intensity (GLRI), narrowband spectral reflectance, chlorophyll fluorescence emission and xanthophyll cycle pigment composition were recorded. Strong relationships between both predictor variables (GLRI, PRI) and both explanatory variables (NPQ, xanthophyll cycle de-epoxidation) were observed. GLRI holds promise to provide detailed (mm) information about plant physiological status to improve our understanding of the patterns and mechanisms driving foliar photoprotection. We discuss the potential for scaling these laboratory data to three-dimensional canopy space.

Shrimp pond effluent dominates foliar nitrogen in disturbed mangroves as mapped using hyperspectral imagery

Conversion of mangroves to shrimp ponds creates fragmentation and eutrophication. Detection of the spatial variation of foliar nitrogen is essential for understanding the effect of eutrophication on mangroves. We aim (i) to estimate nitrogen variability across mangrove landscapes of the Mahakam delta using airborne hyperspectral remote sensing (HyMap) and (ii) to investigate links between the variation of foliar nitrogen mapped and local environmental variables. In this study, multivariate prediction models achieved a higher level of accuracy than narrow-band vegetation indices, making multivariate modeling the best choice for mapping. The variation of foliar nitrogen concentration in mangroves was significantly influenced by the local environment: (1) position of mangroves (seaward/landward), (2) distance to the shrimp ponds, and (3) predominant mangrove species. The findings suggest that anthropogenic disturbances, in this case shrimp ponds, influence nitrogen variation in mangroves. Mangroves closer to the shrimp ponds had higher foliar nitrogen concentrations.

Modelling C₃ photosynthesis from the chloroplast to the ecosystem

Globally, photosynthesis accounts for the largest flux of CO₂ from the atmosphere into ecosystems and is the driving process for terrestrial ecosystem function. The importance of accurate predictions of photosynthesis over a range of plant growth conditions led to the development of a C₃ photosynthesis model by Farquhar, von Caemmerer & Berry that has become increasingly important as society places greater pressures on vegetation. The photosynthesis model has played a major role in defining the path towards scientific understanding of photosynthetic carbon uptake and the role of photosynthesis on regulating the earth's climate and biogeochemical systems. In this review, we summarize the photosynthesis model, including its continued development and applications. We also review the implications these developments have on quantifying photosynthesis at a wide range of spatial and temporal scales, and discuss the model's role in determining photosynthetic responses to changes in environmental conditions. Finally, the review includes a discussion of the larger-scale modelling and remote-sensing applications that rely on the leaf photosynthesis model and are likely to open new scientific avenues to address the increasing challenges to plant productivity over the next century.

Effects of simulated drought on the carbon balance of Everglades short-hydroperiod marsh

Hydrology drives the carbon balance of wetlands by controlling the uptake and release of CO2 and CH4 . Longer dry periods in between heavier precipitation events predicted for the Everglades region, may alter the stability of large carbon pools in this wetland's ecosystems. To determine the effects of drought on CO2 fluxes and CH4 emissions, we simulated changes in hydroperiod with three scenarios that differed in the onset rate of drought (gradual, intermediate, and rapid transition into drought) on 18 freshwater wetland monoliths collected from an Everglades short-hydroperiod marsh. Simulated drought, regardless of the onset rate, resulted in higher net CO2 losses net ecosystem exchange (NEE) over the 22-week manipulation. Drought caused extensive vegetation dieback, increased ecosystem respiration (Reco ), and reduced carbon uptake gross ecosystem exchange (GEE). Photosynthetic potential measured by reflective indices (photochemical reflectance index, water index, normalized phaeophytinization index, and the normalized difference vegetation index) indicated that water stress limited GEE and inhibited Reco . As a result of drought-induced dieback, NEE did not offset methane production during periods of inundation. The average ratio of net CH4 to NEE over the study period was 0.06, surpassing the 100-year greenhouse warming compensation point for CH4 (0.04). Drought-induced diebacks of sawgrass (C3 ) led to the establishment of the invasive species torpedograss (C4 ) when water was resupplied. These changes in the structure and function indicate that freshwater marsh ecosystems can become a net source of CO2 and CH4 to the atmosphere, even following an extended drought. Future changes in precipitation patterns and drought occurrence/duration can change the carbon storage capacity of freshwater marshes from sinks to sources of carbon to the atmosphere. Therefore, climate change will impact the carbon storage capacity of freshwater marshes by influencing water availability and the potential for positive feedbacks on radiative forcing.

Antioxidant and photoprotective responses to elevated CO(2) and heat stress during holm oak regeneration by resprouting, evaluated with NIRS (near-infrared reflectance spectroscopy)

Photosynthetic, photoprotective and antioxidant responses during high temperature stress were determined in leaves of evergreen holm oak (Quercus ilex L.), the main species in Mediterranean forests, during resprouting under elevated CO(2) (750 μl·l(-1) ). Leaf chemicals, chloroplast pigments and non-enzymatic antioxidants were quantified in a single measurement using NIRS (near-infrared spectroscopy), a rapid and suitable method for ecophysiological purposes. Resprouts from plants grown under elevated CO(2) (RE) showed photosynthetic down-regulation, higher starch content and lower stomatal conductance, but similar stomatal density, than plants grown under current CO(2) concentrations (350 μl·l(-1) ) (RA). The photosynthetic sink reduction and need for more antioxidants and photoprotection in RE were reflected in an increased concentration of ascorbate (Asc) and phenolic compounds and in the contribution of the xanthophyll (Z/VAZ) and lutein epoxide cycles to excess energy dissipation as heat, and also reflected in chlorophyll fluorescence measurements. CO(2) assimilation parameters were stable from 25 to 35 °C in RE and RA, declining thereafter in RA in spite of a 2.3 °C lower leaf temperature. RE showed a more marked decline in photorespiration above 35 °C and less sensitive stomatal responses to high temperature stress than RA. During heat stress, RE had higher Asc, Z/VAZ and phenolics content, together with delayed enhancement of chloroplast lipophilic antioxidant compounds (carotenes and tocopherols). The high contribution of photoprotective systems and high temperature tolerance in resprouts developed under elevated CO(2) would mitigate the effect of photosynthesis acclimation during the regeneration of Q. ilex plants under climate change.

Hyperspectral remote sensing of foliar nitrogen content

A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact--it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423-855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710-790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.

Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis

Leaf water content is an important variable for understanding plant physiological properties. This study evaluates a spectral analysis approach, continuous wavelet analysis (CWA), for the spectroscopic estimation of leaf gravimetric water content (GWC, %) and determines robust spectral indicators of GWC across a wide range of plant species from different ecosystems. CWA is both applied to the Leaf Optical Properties Experiment (LOPEX) data set and a synthetic data set consisting of leaf reflectance spectra simulated using the leaf optical properties spectra (PROSPECT) model. The results for the two data sets, including wavelet feature selection and GWC prediction derived using those features, are compared to the results obtained from a previous study for leaf samples collected in the Republic of Panamá (PANAMA), to assess the predictive capabilities and robustness of CWA across species. Furthermore, predictive models of GWC using wavelet features derived from PROSPECT simulations are examined to assess their applicability to measured data. The two measured data sets (LOPEX and PANAMA) reveal five common wavelet feature regions that correlate well with leaf GWC. All three data sets display common wavelet features in three wavelength regions that span 1732-1736 nm at scale 4, 1874-1878 nm at scale 6, and 1338-1341 nm at scale 7 and produce accurate estimates of leaf GWC. This confirms the applicability of the wavelet-based methodology for estimating leaf GWC for leaves representative of various ecosystems. The PROSPECT-derived predictive models perform well on the LOPEX data set but are less successful on the PANAMA data set. The selection of high-scale and low-scale features emphasizes significant changes in both overall amplitude over broad spectral regions and local spectral shape over narrower regions in response to changes in leaf GWC. The wavelet-based spectral analysis tool adds a new dimension to the modeling of plant physiological properties with spectroscopy data.

Relation of chlorophyll fluorescence sensitive reflectance ratios to carbon flux measurements of montanne grassland and norway spruce forest ecosystems in the temperate zone

We explored ability of reflectance vegetation indexes (VIs) related to chlorophyll fluorescence emission (R₆₈₆/R₆₃₀, R₇₄₀/R₈₀₀) and de-epoxidation state of xanthophyll cycle pigments (PRI, calculated as (R₅₃₁- R₅₇₀)/(R₅₃₁-R₅₇₀) to track changes in the CO₂ assimilation rate and Light Use Efficiency (LUE) in montane grassland and Norway spruce forest ecosystems, both at leaf and also canopy level. VIs were measured at two research plots using a ground-based high spatial/spectral resolution imaging spectroscopy technique. No significant relationship between VIs and leaf light-saturated CO₂ assimilation (A(MAX)) was detected in instantaneous measurements of grassland under steady-state irradiance conditions. Once the temporal dimension and daily irradiance variation were included into the experimental setup, statistically significant changes in VIs related to tested physiological parameters were revealed. ΔPRI and Δ(R₆₈₆/R₆₃₀) of grassland plant leaves under dark-to-full sunlight transition in the scale of minutes were significantly related to A(MAX) (R² = 0.51). In the daily course, the variation of VIs measured in one-hour intervals correlated well with the variation of Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and LUE estimated via the eddy-covariance flux tower. Statistical results were weaker in the case of the grassland ecosystem, with the strongest statistical relation of the index R₆₈₆/R₆₃₀ with NEE and GPP.

High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge

Genomic selection (GS) and high-throughput phenotyping have recently been captivating the interest of the crop breeding community from both the public and private sectors world-wide. Both approaches promise to revolutionize the prediction of complex traits, including growth, yield and adaptation to stress. Whereas high-throughput phenotyping may help to improve understanding of crop physiology, most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and comparable to genomic selection. Despite the fact that the two methodological approaches represent the extremes of what is understood as the breeding process (phenotype versus genome), they both consider the targeted traits (e.g. grain yield, growth, phenology, plant adaptation to stress) as a black box instead of dissecting them as a set of secondary traits (i.e. physiological) putatively related to the target trait. Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology. This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy (NIRS) and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.