Foliar Spectra Research Articles

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

Spectroscopy 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. Using 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. Spectral 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. Our 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.

Estimating cadmium-lead concentrations in rice blades through fractional order derivatives of foliar spectra

Heavy metal pollution in farmland harms the environment and poses a potential risk to human health. Visible and near-infrared reflectance (VNIR) spectroscopy is a promising tool for estimating heavy metal concentrations in plants. Integer-order derivatives (including the first and second) are commonly used to pre-process VNIR data and successfully detect certain spectral signals. However, they fail to detect gradual tilts or curvatures and useful target variable information. In this study, a greenhouse experiment covering 16 pre-treatments of Cd–Pb (cadmium-lead) cross-contamination was designed to collect the VNIR data of rice blades during the late booting stage. A fractional order derivative (FOD) algorithm with increments of 0.1 was utilised to pre-process the spectra of the rice blades to explore the model performance in building relationships between Cd and Pb concentrations and leaf spectra. The results indicated that the inversion with pre-processing of integer-order derivatives was not as good as the optimal results with pre-processing of fractional-order derivatives. The R 2 and RMSE of the Cd estimation reached 0.84 and 4.69 at 0.3 rd order pre-processing, while the R 2 and RMSE of Pb were 0.49 and 191.24 at 1.4 th order pre-processing. These optimal results were better than those with pre-processing of 1 st and 2 nd derivatives, resulting in an increase of R 2 and a decrease of RMSE. These results indicated that fractional-order derivatives outperformed integer-order derivatives for pre-processing the rice blades spectra to estimate Cd–Pb concentrations. Our results demonstrated that FOD is an effective spectral processing routine for heavy metal estimation for Cd–Pb cross-contamination. • VNIR spectra of rice blades under Cd–Pb cross-contamination were measured. • Fractional order derivative (FOD) was used to analyze the spectra of rice blades. • FOD can extract more spectral details than first and second derivatives. • PLSR with 0.3 rd (for Cd) and 1.4 th (for Pb) order yielded the best prediction.

Relationship Between Vegetation Indices, Nutrients Content, and the Biomass Production of Brachiaria (Brachiaria ruziziensis)

ABSTRACT The aim of this study was to evaluate the use of a hyperspectral sensor to generate vegetation indices (VIs), which were correlated with the content of macro and micronutrients and the production of dry biomass of Brachiaria ruziziensis submitted to treatments with mining coproducts. The treatments were arranged in double factorial scheme, randomly assigned with five repetitions and placed into pots inside a greenhouse. The factors evaluated were type of input and soil class (Arenosol and Ferralsol). The inputs were limestone mining coproducts applied in two different ways, isolated and composed. After incubation for 140 days, brachiaria seeds were sowed. 40 days after sowing, the foliar spectra were collected, and so were the leaves for further analysis of the contents of macro and micronutrients, as well as the production of shoot dry biomass (SDM) of the plants. The application of the mining coproducts allowed the availability of great part of the nutrients analyzed, which led to an increase in brachiaria SDM. The increase in SDM, consequently caused physiological and structural alterations in the plant, in which, affect the VIs. The use of broadband VIs allowed a strong correlation (>0.5) with SDM, calcium (Ca), and copper (Cu). The narrowband VIs showed a strong correlation (>0.5) with Ca and Cu. This study confirms that the VIs correlate with agronomic attributes, such as SDM and with macro and micronutrients content in brachiaria.

Jeannine Cavender-Bares.

Jeannine Cavender-Bares.

Phylogenetic signatures in reflected foliar spectra of regenerating plants in Neotropical forest gaps

Spectroscopy can be used to investigate ecosystem functioning and phylogenetic relationships, since it can provide information about the optical properties of a plant – a critical functional trait with regard to light usage. The link between phylogenetic relationships and foliar reflectance spectra of Neotropical species has rarely been considered and no previous studies have explored this in relation to forest regeneration following natural gap formation. We investigated phylogenetic signatures, i.e. evolutionary conservation, of foliar spectra in gap-regenerating woody species in conserved Neotropical forests. We collected foliar spectra of 53 regenerating woody species in the forest gaps at two sites of Atlantic Seasonal Semi-deciduous forest, and quantified the phylogenetic signal for spectral wavelengths using Blomberg's K, Pagel's lambda (λ) and LIPA (Local indicator of phylogenetic association) statistics. Analysis of foliar reflectance spectra showed that the largest portion of phylogenetic signature from both K and λ occurred within the first part of the shortwave infrared region (SWIR-1: 1551–1849 nm), followed by the near infrared (NIR: 701–1349 nm), and the lowest portion within the visible region (VIS: 400–700 nm). Most of the significant phylogenetic signal found for wavelengths of all spectral regions suggested convergent conservation, i.e. homoplasy. The largest portion of positive LIPA (i.e. phylogenetic similarity) was found for wavelengths within the second part of the SWIR (SWIR-2: 2051–2450 nm), while most of the negative LIPA (i.e. phylogenetic distinctiveness) occurred within the VIS. We conclude that there is a tendency towards evolutionary convergence in the phylogenetic signature of foliar spectra in certain parts of the spectrum. The extent of phylogenetic signal found in the SWIR allows us to conclude that this region carries the most potential for future investigations of phylogenetic influence on spectral community dynamics within Neotropical forests.

Vis–NIR spectroscopy: from leaf dry mass production estimate to the prediction of macro- and micronutrients in soybean crops

Our work aimed to evaluate the use of visible–near-infrared (Vis–NIR) spectroscopy for predicting the production of leaf dry mass (LDM), as well as macro- and micronutrients contents of soybean leaves grown after application of limestone-mining coproducts. The treatments were arranged within a triple factorial scheme (6 × 2 × 2 + 2) and placed into pots in a greenhouse. We evaluated the following factors: type of input (limestone-mining coproducts), input particle size (filler and powder), and soil class (Arenosol and Ferralsol). After inputs incubation, the soybean was sown. Then, 42 days after sowing, we collected the foliar spectra, as well as leaves, for further analysis of the contents of macro- and micronutrients in leaves and production of LDM. We managed to adjust models at the stage of prediction with R2p > 0.50 and RPDp > 1.50 for the variables LDM, P, K, Mg, S, and Zn, with emphasis on the first four, which presented R2p above 0.65. Therefore, we conclude that Vis–NIR spectroscopy has a potential for predicting LDM and the nutrients contents of soybean subjected to the application of limestone-mining coproducts, with advantages such as speed, low cost, and no use of reagents that are toxic to the environment.

Foliar Spectra and Traits of Bog Plants across Nitrogen Deposition Gradients

Bogs, as nutrient-poor ecosystems, are particularly sensitive to atmospheric nitrogen (N) deposition. Nitrogen deposition alters bog plant community composition and can limit their ability to sequester carbon (C). Spectroscopy is a promising approach for studying how N deposition affects bogs because of its ability to remotely determine changes in plant species composition in the long term as well as shorter-term changes in foliar chemistry. However, there is limited knowledge on the extent to which bog plants differ in their foliar spectral properties, how N deposition might affect those properties, and whether subtle inter- or intraspecific changes in foliar traits can be spectrally detected. The objective of the study was to assess the effect of N deposition on foliar traits and spectra. Using an integrating sphere fitted to a field spectrometer, we measured spectral properties of leaves from the four most common vascular plant species (Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum and Eriophorum vaginatum) in three bogs in southern Québec and Ontario, Canada, exposed to different atmospheric N deposition levels, including one subjected to a 18-year N fertilization experiment. We also measured chemical and morphological properties of those leaves. We found detectable intraspecific changes in leaf structural traits and chemistry (namely chlorophyll b and N concentrations) with increasing N deposition and identified spectral regions that helped distinguish the site-specific populations within each species. Most of the variation in leaf spectral, chemical, and morphological properties was among species. As such, species had distinct spectral foliar signatures, allowing us to identify them with high accuracy with partial least squares discriminant analyses (PLSDA). Predictions of foliar traits from spectra using partial least squares regression (PLSR) were generally accurate, particularly for the concentrations of N and C, soluble C, leaf water, and dry matter content (<10% RMSEP). However, these multi-species PLSR models were not accurate within species, where the range of values was narrow. To improve the detection of short-term intraspecific changes in functional traits, models should be trained with more species-specific data. Our field study showing clear differences in foliar spectra and traits among species, and some within-species differences due to N deposition, suggest that spectroscopy is a promising approach for assessing long-term vegetation changes in bogs subject to atmospheric pollution.

LiCHy: The CAF’s LiDAR, CCD and Hyperspectral Integrated Airborne Observation System

We describe the design, implementation and performance of a novel airborne system, which integrates commercial waveform LiDAR, CCD (Charge-Coupled Device) camera and hyperspectral sensors into a common platform system. CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System is a unique system that permits simultaneous measurements of vegetation vertical structure, horizontal pattern, and foliar spectra from different view angles at very high spatial resolution (~1 m) on a wide range of airborne platforms. The horizontal geo-location accuracy of LiDAR and CCD is about 0.5 m, with LiDAR vertical resolution and accuracy 0.15 m and 0.3 m, respectively. The geo-location accuracy of hyperspectral image is within 2 pixels for nadir view observations and 5–7 pixels for large off-nadir observations of 55° with multi-angle modular when comparing to LiDAR product. The complementary nature of LiCHy’s sensors makes it an effective and comprehensive system for forest inventory, change detection, biodiversity monitoring, carbon accounting and ecosystem service evaluation. The LiCHy system has acquired more than 8000 km2 of data over typical forests across China. These data are being used to investigate potential LiDAR and optical remote sensing applications in forest management, forest carbon accounting, biodiversity evaluation, and to aid in the development of similar satellite configurations. This paper describes the integration of the LiCHy system, the instrument performance and data processing workflow. We also demonstrate LiCHy’s data characteristics, current coverage, and potential vegetation applications.

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.

NASA Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager

The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (~1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT’s data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA’s Data and Information policy.

Indication of the Expression of Transgene in Rice Plant Based on Hyperspectral Remote Sensing Technique II——Growth Monitoring of Samples in the Contrast Experiment

Since the complication of monitoring and evaluating the problems about the transgenic expression and its impacts on the receptor in the transgenic crop breeding and other relevant evaluated works, the authors in the present work tried to assess the differences of spectral parameters of the transgenic rice in contrast with its parent group quantitatively and qualitatively, fulfilling the growth monitoring of the transgenic samples. The spectral parameters (spectral morphological characteristics and indices) chosen are highly related to internal or external stresses to the receipts, and thus could be applied as indicators of biophysical or biochemical processes changes of plant. By ASD portable field spectroradiometer with high-density probe, fine foliar spectra of 8 groups were obtained. By analyzing spectral angle and continuum removal, the spectral morphological differences and their locations of sample spectra were found which could be as auxiliary priori knowledge for quantitative analysis. By investigating spectral indices of the samples, the quantitative differences of spectra were revealed about foliar chlorophyll a+b and carotenoid content. In this study both the spectral differences between transgenic and parent groups and among transgenic groups were investigated. The results show that hyperspectral technique is promising and a helpful auxiliary tool in the study of monitoring the transgenic crop and other relevant researches. By this technique, quantitative and qualitative results of sample spectra could be provided as prior knowledge, as certain orientation, for laboratory professional advanced transgenic breeding study.

Foliar spectral properties following leaf clipping and implications for handling techniques

After leaves are clipped their reflectance properties change over time at variable rates. Spectral change can in part be attributed to the changing water content of the leaf, which affects absorption in the VIS, NIR and the SWIR. Maintaining water volume within samples has been the motivation behind many leaf handling techniques. This study has assessed the time constraints between leaf collection and spectral measurement. Specifically the relationship between leaf water content and foliar spectra (350–2500 nm) was examined over time for five tropical trees (common guava ( Psidium guajava), purple guava ( Psidium littorale), weeping fig ( Ficus benjamina), floss silk ( Chorisia speciosa), and coffee ( Coffea arabica)). This investigation was carried for leaves wrapped with moist gauze around their petiole ( treatment leaves) and leaves with no treatment. Spectral measurements and mass measurements were repeated for each leaf once every hour for the first 12 h, then every 4–6 h for 18 h, followed by one measurement after 12 h, and finally once a day until the control samples became air-dry. Foliar reflectance in the visible spectrum was not immediately responsive to water content changes and did not change until wilting of the leaf was observed. The NIR and SWIR wavelength regions were affected immediately by small changes in water content. Thus, by the time wilting was first observed the NIR and SWIR foliar reflectance differed considerably from corresponding fresh leaf reflectance. No common time limit could be observed for leaf clipping and reflectance measurement. Leaves have a variety of water contents and dehydration rates hence measurement time constraints are dependent on the properties of the leaf or species. Rather than using a time limit it is recommended that leaf handling techniques be based upon managing leaf water content and leaf structure. The results of this study indicate that leaves with petioles wrapped in moist paper towel and placed within plastic bags will maintain leaf reflectance longer than equivalent leaves without treatment; samples tested here lasted a minimum of 7 days. θ and D indices (“angle difference” and “root mean square difference”, respectively) revealed a stronger relationship between leaf water content and spectral shape than between leaf water and raw reflectance magnitude. The ratio of 1187/1096 nm, when compared with θ and D indices and individual reflectance bands, showed the highest coefficient of determination with leaf water content ( r 2 = 0.952).

Analyses of Forest Foliage II: Measurement of Carbon Fraction and Nitrogen Content by End-Member Analysis

Chemical constituents of forest canopies are accepted as key indicators of ecosystem state and function. Remote sensing of these parameters offers the potential for rapid and accurate assessment of rates of key biogeochemical processes over large regions. NASA's Accelerated Canopy Chemistry Program was established to test the accuracy and generality of high-spectral resolution remote sensing in the measurement of lignin, cellulose and nitrogen concentrations in forest canopies. One of the most straightforward methods for extracting constituent concentration information from spectra is through simple linear mixing models of pre-determined end-member or pure compound spectra. While there are ample reasons to expect that linear mixing models will not work in foliar and whole-canopy samples, end-member analysis might still provide important information in support of standard and emerging statistical techniques, such as linear regression and partial least squares regression on first and second difference spectra, by indicating which parts of the spectrum should contain information on the concentrations of important constituents. The purpose of this paper is to present the results of two approaches to determining the value of end-member spectra for estimating constituent concentrations in foliage of temperate zone forest species. The first examines the spectral changes accompanying each step in the chemical proximate analysis method used to determine concentrations in the laboratory, and from them to infer the spectra of the fractions removed at each step. The second is the combination of known materials which approximate these same fractions in foliage into well-mixed samples to determine whether a simple linear mixing model can be used to predict the spectrum of the resulting mixture. Results confirm that the combination of linear mixing models and end-member analysis is not an appropriate technique for obtaining quantitative estimates of constituent concentrations for the major components of foliage of native woody plants, nor do we expect that more detailed analyses of plant ultrastructure or foliar spectra will correct the deficiencies identified. However, these results do suggest that the wet chemical procedures used to extract different carbon fractions produce consistent results with regard to the location of spectral features associated with the compounds removed at each step, and that the spectra of the cellulose and lignin isolated by this technique are very similar to those from pure materials. This in turn suggests that statistical techniques such as linear regression on first and second difference spectra and partial least squares methods which allow or correct for non-linear mixing, should be successful.