Spectral reflectance and leaf internal structure changes of barley plants due to phytoextraction of zinc and cadmium

The focus of our research is to seek spectral signatures that indicate the impact and content of heavy metals in the leaves and canopies of living plants during the process of phytoremediation. Potted plants of barley (Hordeum vulgare) were grown for 5–6 weeks before being subjected to metal treatments of Zn and Cd. Diffuse reflectance spectra (350–2500 nm) of the plant canopies were collected daily using a portable spectroradiometer throughout the treatment period. Foliar structural changes of Zn‐treated plants included a decrease in intercellular space, palisade and epidermal cell size while Cd‐treated plants displayed fewer structural changes in leaf. Spectral analysis revealed that the band ratios at 1110 nm to that at 810 nm might be used as an indicator of the accumulation of certain metals in plant shoots. Normalized Difference Vegetation Index (NDVI) and leaf‐water‐content indices examined as part of our spectral analysis were not able to distinguish plants treated with different metals. Our ratio index R₁₁₁₀/R_810, on the other hand, correlates closely with the magnitude of leaf structural changes. This study suggests that the infrared reflectance spectrum (800–1300 nm) of plant canopy might provide a non‐intrusive monitoring method for the physiological status of plants grown on heavy metal contaminated soil.     

Quantifying arsenic-induced morphological changes in spinach leaves: implications for remote sensing

Arsenic (As) is a common soil contaminant that can be accumulated into plant parts. The ability to detect As in contaminated plants is an important tool to minimize As-induced health risks in humans. Near-infrared (NIR) spectra are strongly affected by leaf structural characteristics. Therefore, quantitative analyses of structural changes in the arrangement of mesophyll cells caused by As will help to explain spectral responses to As. The objectives of this study were to use stereological methods to quantify internal structural changes in leaves with As treatment in spinach plants, and to relate these changes to leaf spectral properties in NIR spectra. Hydroponically grown spinach was treated with 0, 5, 10 and 20 μmol l^?1 for four weeks in a growth chamber. Spectral properties of leaves were obtained for visible and infrared frequencies. Leaf structural properties, such as mesophyll thickness and mesophyll surface area, were measured using stereological methods. Quantitative analysis of leaf structure showed that total leaf thickness and intercellular spaces in spongy mesophyll cells decreased with increasing As treatment. Changes in leaf reflectance in NIR wavelengths were strongly correlated with leaf As concentration and leaf structural changes. Multiple linear regression of leaf reflectance values at the highest correlated wavelengths (1048, 1098 and 1080 nm) generated an R ² value of 0.69. Results from this study support the hypothesis that relationships between leaf structure and reflectance may be useful in the interpretation of spectral data to detect plant leaf As concentration.     

Associated changes in physiological parameters and spectral reflectance indices in olive (Olea europaea L.) leaves in response to different levels of water stress

This study aimed to determine whether modification of physiological parameters could be detected remotely by monitoring the spectral reflectance of olive leaves in response to different degrees of drought. Three different drought intensities were simulated: (a) a mild drought by feeding abscisic acid to detached branches; (b) a rapid and severe drought by detaching leaves and letting them dry over several hours; (c) a relatively slow drought caused by withholding water to potted olive plants. The three degrees of stress affected gas exchange and chlorophyll fluorescence. When the inhibition of photosynthesis occurred within an hour it was not accompanied by a parallel reduction in chlorophyll concentration in the carotenoid to chlorophyll ratio. Consequently, changes in spectral reflectance in the visible region, e.g. in PRI (photochemical reflectance index) and FRI (fluorescence reflectance indices) were not significantly induced. In contrast, when the inhibition of photosynthesis caused by slow developing drought was prolonged (i.e. more than 24 hours) and led to a decrease in chlorophyll concentration and to a simultaneous increase in carotenoid to chlorophyll ratio, there were significant changes in the visible region of the leaf spectral reflectance and, in turn, in PRI and FRI. We defined 16 new reference wavelengths, from visible to SWIR regions, which are sensitive to both fast‐developing and slow‐developing stresses. These reference wavelengths were used to develop an algorithm, the Relative Reflectance Increment (RRI), that was linearly related to changes in relative water content (RWC, r ² = 0.733). This algorithm showed that the 1455 nm wavelength is highly affected by drought. This wavelength was therefore used to elaborate the water content reflectance index that was inversely related to RWC (r ² = 0.702).     

Exploring the potential of leaf reflectance spectra for retrieving the leaf maximum carboxylation rate

The maximum carboxylation rate (V_cmax) is a key photosynthetic parameter that is determined by the leaf biochemistry and environmental conditions. Numerous studies have shown that plant biochemical, physiological and structural parameters can be estimated from reflectance spectra. Therefore, it is reasonable to assume that V_cmax can be spectrally determined. Here, we investigate the potential of leaf reflectance spectra for retrieving the maximum carboxylation rate of leaves. Measurements of leaf reflectance, carbon dioxide (CO₂) response curves, leaf chlorophyll-a + b (chl-a + b) etc., were made on 80 crop, shrub and tree leaves. Then, the leaf V_cmax,25 was linked to leaf biochemistry and spectral reflectance. A reliable relationship, with a coefficient of determination (R²) value of 0.75, was found between the leaf chl-a + b content and V_cmax,25. The leaf V_cmax,25 values were also significantly correlated with chl-a + b-sensitive spectral indices with the highest R² value that was found being 0.83 for the ratio spectral index (RSI) using reflectances at 1089 nm and 695 nm. Finally, multiple stepwise regression (MSR) and a partial least-squares regression (PLSR) modelling approach were used to estimate V_cmax,25 from leaf reflectances. The results confirmed that V_cmax,25 can be reliably estimated from leaf reflectance spectra and give an R² value >0.80. These findings show that leaf chl-a + b can be used as a proxy for leaf V_cmax,25 and that leaf V_cmax,25 can be spectrally determined using leaf reflectance data.     

Estimating leaf nitrogen concentration in heterogeneous crop plants from hyperspectral reflectance

The estimation of leaf nitrogen concentration (LNC) in crop plants is an effective way to optimize nitrogen fertilizer management and to improve crop yield. The objectives of this study were to (1) analyse the spectral features, (2) explore the spectral indices, and (3) investigate a suitable modelling strategy for estimating the LNC of five species of crop plants (rice (Oryza sativa L.), corn (Zea mays L.), tea (Camellia sinensis), gingili (Sesamum indicum), and soybean (Glycine max)) with laboratory-based visible and near-infrared reflectance spectra (300–2500 nm). A total of 61 leaf samples were collected from five species of crop plant, and their LNC and reflectance spectra were measured in laboratories. The reflectance spectra of plants were reduced to 400–2400 and smoothed using the Savitzky–Golay (SG) smoothing method. The normalized band depth (NBD) values of all bands were calculated from SG-smoothed reflectance spectra, and a successive projections algorithm-based multiple linear regression (SPA-MLR) method was then employed to select the spectral features for five species. The SG-smoothed reflectance spectra were resampled using a spacing interval of 10 nm, and normalized difference spectral index (NDSI) and three-band spectral index (TBSI) were calculated for all wavelength combinations between 400 and 2400 nm. The NDSI and TBSI values were employed to calibrate univariate regression models for each crop species. The leave-one-out cross-validation procedure was used to validate the calibrated regression models. Study results showed that the spectral features for LNC estimation varied among different crop species. TBSI performed better than NDSI in estimating LNC in crop plants. The study results indicated that there was no common optimal TBSI and NDSI for different crop species. Therefore, we suggest that, when monitoring LNC in heterogeneous crop plants with hyperspectral reflectance, it might be appropriate to first classify the data set considering different crop species and then calibrate the model for each species. The method proposed in this study requires further testing with the canopy reflectance and hyperspectral images of heterogeneous crop plants.     

Plant spectral responses to gas leaks and other stresses

The study aimed to assess the ability of remote sensing to differentiate between plant stress caused by natural gas leakage and other stresses. In order to use satellite remote sensing to detect gas leaks it is necessary to determine whether the cause of the stress can be identified in the spectral response and distinguished from other stress factors. Field plots of oilseed rape (Brassica napus) were stressed using elevated levels of natural gas in the soil, dilute herbicide solution and extreme shade. Visible stress response, spectral stress response and chlorophyll content of plants from these three treatments were compared to control plants receiving no treatment. The reflectance from isolated leaves was measured in the laboratory. Spectral responses to stress included increased reflectance in the visible wavelengths and decreased reflectance in the near‐infrared. A shift of the red edge position towards shorter wavelengths was observed as a result of all three stresses, although the shift was greatest when stressed via extreme shade. Red edge position was strongly correlated with chlorophyll content across all the treatments. The ratio of reflectances centred on the wavelengths 670 and 560 nm was used to detect increases in red pigmentation in gassed and herbicide‐stressed leaves. Stress due to extreme shade could be distinguished from stress caused by natural gas or herbicide by changes in the reflectance spectra, however, stress caused by herbicide or natural gas could not be distinguished from one another in the spectra although symptoms of stress caused by elevated gas levels were identified earlier than symptoms caused by herbicide‐induced stress.     

Estimating leaf nitrogen concentration from similarities in fresh and dry leaf spectral bands using a model population analysis framework

Fresh leaf spectral reflectance is primarily influenced by leaf water content and structural aspects such as the inter-cellular spaces within the spongy mesophyll, which also interfere with the estimation of the leaf nitrogen content. It is therefore essential to identify spectral bands that are least affected by the above perturbing factors for improving leaf nitrogen estimation for fresh leaves across any landscape. Wavelengths selection plays a vital role in identifying the best spectral features for assessing leaf nitrogen concentration from hyperspectral data of dry and fresh leaves. The primary objective of this study was to determine typical optimal bands for leaf nitrogen estimation from spectra (400–2500 nm) of whole fresh and dry leaves for the same specimens of Eucalyptus grandis. This was achieved via the use of competitive adaptive re-weighted sampling (CARS), and Monte Carlo cross-validation-competitive adaptive re-weighted sampling (MCCV-CARS) band selection approaches. Bands selected (931 nm, 1003 nm, 1027 nm, 1036 nm, 1177 nm, and 1180 nm) via the MCCV-CARS approach yielded the highest estimation accuracy for both fresh predicted coefficient of determination (R²_cal) = 0.82 and predicted root mean square error (RMSE_P) = 0.14) and dry leaves (R²_P = 0.88 and RMSE_P = 0.13) when compared to CARS (2044 nm, 2107 nm, and 2188 nm) only. The identified spectral features could be relevant for assessing leaf nitrogen concentration for different seasons, for example, wet to dry season.     

Effects of adaxial and abaxial surface on the estimation of leaf chlorophyll content using hyperspectral vegetation indices

Vegetation indices are frequently used for the non-destructive assessment of leaf chemistry, especially chlorophyll content. However, most vegetation indices were developed based on the statistical relationship between the spectral reflectance of the adaxial leaf surface and chlorophyll content, even though abaxial leaf surfaces may influence reflectance spectra because of canopy structure or the inclination of leaves. In the present study, reflectance spectra from both adaxial and abaxial leaf surfaces of Populus alba and Ulmus pumila var. pendula were measured. The results showed that structural differences of the two leaf surfaces may result in differences in reflectance and hyperspectral vegetation indices. Among 30 vegetation indices tested, R₆₇₂/(R₅₅₀ × R₇₀₈) had the smallest difference (4.66% for P. alba, 2.30% for U. pumila var. pendula) between the two blade surfaces of the same leaf in both species. However, linear regression analysis showed that several vegetation indices (R₈₅₀ ? R₇₁₀)/(R₈₅₀ ? R₆₈₀), VOG₂, D₇₃₀, and D₇₄₀, had high coefficients of determination (R² > 0.8) and varied little between the two leaf surfaces of the plants we sampled. This demonstrated that these four vegetation indices had relatively stable accuracy for estimating leaf chlorophyll content. The coefficients of determination (R²) for the calibration of P. alba leaves were 0.92, 0.98, 0.93, and 0.95 on the adaxial surfaces, and 0.88, 0.87, 0.88, and 0.92 on the abaxial surfaces. The coefficients of determination (R²) for the calibration of U. pumila var. pendula leaves were 0.85, 0.91, 0.86, and 0.90 on adaxial surface, and 0.80, 0.80, 0.84, and 0.88 on abaxial surface. These four vegetation indices were readily available and were little influenced by the differences in the two leaf surfaces during the estimation of leaf chlorophyll content.     

Spectral identification of ozone‐damaged pine needles

Needles were collected from ponderosa and Jeffrey pine trees at three sites in the Sierra Nevada, and were assembled into 504 samples and grouped according to five dominant live needle conditions – green, winter fleck, sucking insect damage, scale insect damage, and ozone damage – and a random mixture. Reflectance and transmittance measurements of abaxial and adaxial surfaces were obtained at ca 0.3 nm spectral resolution from 400–800 nm, and binned to simulate Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data. There were no significant differences in optical properties between the two surfaces. Ozone‐damaged needles were collected from Jeffrey pine trees at one site, and exhibited significantly different (family‐wise α = 0.01) reflectance and transmittance signatures – and significantly different signature slopes – at both spectral resolutions, from green and winter fleck needles from the same site. Ozone‐damaged needles had significantly different (family‐wise α = 0.01) abaxial surface reflectance and reflectance slope signatures from all other groups of needles, at both spectral resolutions. In comparison with three chlorophyll reflectance indices, a new red fall index (RFI) provides high classification accuracies for ozone‐damaged and non‐ozone‐damaged pine needles (overall acc. = 94%; κ = 59%). Thus, ozone‐damaged Jeffrey pine needles have a unique spectral signature in relation to dominant needle conditions of ponderosa and Jeffrey pine trees.     

Estimation of chlorophyll‐a concentration in Lake Tai,China using in situ hyperspectral data

Reflectance spectra of water in Lake Tai of East China were measured at 28 monitoring stations with an ASD FieldSpec spectroradiometer at an interval of 1.58 nm over five days in each month from June to August of 2004. Water samples collected at these stations were analyzed in the laboratory to determine chlorophyll‐a (chl‐a) concentration. Twenty‐eight spectral reflectance curves were standardized and correlated with chl‐a concentration. Examination of these curves reveals a peak reflectance at 719 nm. Chl‐a concentration level in the Lake was most closely correlated with the reflectance near 700 nm. If regressed against the reflectance at the wavelength of 667 nm (R ₆₆₇), chl‐a concentration was not accurately estimated at R ² = 0.494. Accuracy of estimation was improved to R ² = 0.817 using the maximum reflectance. A higher accuracy of 0.837 was achieved using the peak reflectance at 719 nm (R ₇₁₉) because it does not drift with the level of chl‐a concentration. The highest accuracy of estimation was achieved at R ² = 0.868 using R ₇₁₉/R ₆₆₇.     

A MODIS-based perpendicular moisture index to retrieve leaf moisture content of forest canopies

Moisture dictates vegetation susceptibility to fire ignition and propagation. Various spectral indices have been proposed for the estimation of equivalent water thickness (EWT), which is defined as the mass of liquid water per unit of leaf surface. However, fire models use live fuel moisture content (LFMC) as a measure of vegetation moisture. LFMC is defined as the ratio of the mass of the liquid water in a leaf over the mass of dry matter, and traditional spectral indices are not as effective as with EWT in capturing LFMC variability. The aim of this research was to explore the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra and Aqua satellites in retrieving LFMC from top of the canopy reflectance, and to develop a new spectral index sensitive to this parameter. All the analyses were based on synthetic canopy spectra constructed by coupling the PROSPECT (leaf optical properties model) and SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer models. Simulated top of the canopy spectra were then convolved to MODIS ‘land’ channels 1–7 spectral response functions. All band pairs were evaluated to determine the subspace of MODIS measurements where the separability of points based on their value of LFMC was the highest. This led to the identification of isolines of LFMC in the plane defined by MODIS reflectance measurements in channels 2 and 5; the isolines are straight and parallel, and ordered from lower to higher values of LFMC. This observation allowed the construction of a novel spectral index that is directly related to LFMC – the perpendicular moisture index (PMI). This index measures the distance of a point in the plane spanned by reflectance measurements in MODIS channels 2 and 5 from a reference line, that of completely dry vegetation. Validation against simulated data showed that PMI exhibits a linear relationship with LFMC. When the vegetation cover is dense, the LFMC explains most of the variability in the PMI (R² = 0.70 when LAI > 2; R² = 0.87 when LAI > 4). When the LAI is lower, the contribution of soil background to the measured reflectance increases, and the index underestimates LFMC. The PMI was also validated against the LOPEX93 (Leaf Optical Properties Experiment 1993) data set of leaf optical and biophysical measurements, scaled to canopy reflectance with SAIL, showing acceptable results (R² = 0.56 when LAI > 2; R² = 0.63 when LAI > 4).     

Use of spatial structure analysis of hyperspectral data cubes for detection of insect‐induced stress in wheat plants

Wheat plants were experimentally infested with wheat stem sawflies, and hyperspectral images (reflectance range from 402.8–838.7 nm) were collected from leaves of infested and non‐infested plants. Mean and variance reflectance per leaf were calculated in five of 213 spectral bands (452, 553, 657, 725, and 760 nm) and compared with vegetation indices (NDVI, SI and PRI), and standard variogram parameters (nugget, sill and range values). Mean reflectance values and their variance values and vegetation indices showed significant effects of sawfly infestation in one dataset but not in another. Based on directional variogram analyses, we showed that: (1) better separation of leaf type and infested/non‐infested wheat plants was seen in variograms in longitudinal direction compared to transverse; (2) mainly spectral bands in the red edge and NIR showed consistent effect of sawfly infestation; (3) range values were not affected significantly by either sawfly infestation or leaf type; and (4) sawfly‐induced stress was most likely to be detected about three weeks after infestation. Variogram analysis is one of the key standards in quantitative spatial ecology, and this study supports further research into its use in remote sensing with particular emphasis on detection of biotic stress.     

Effect of leaf and spike morphological traits on the relationship between spectral reflectance indices and yield in wheat

While certain spectral reflectance indices have been shown to be sensitive to the expression of a range of performance-related traits in crops, knowledge of the potentially confounding effects associated with plant anatomy could help improve their application in phenotyping. Morphological traits (leaf and spike wax content, leaf and spike orientation, and awns on spikes) were studied in 20 contrasting advanced wheat lines to determine their influence on spectral indices and in their association with grain yield under well-irrigated conditions. Canopy reflectance (400–1100 nm) was determined at heading and grain filling during two growing seasons and three vegetation indices (VIs; red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), and simple ratio (SR)), and five water indices (WIs; one simple WI and four normalized WIs (NWI-1, NWI-2, NWI-3, and NWI-4)) were calculated. The major reflectance fluctuations caused by the differences in leaf and spike morphology mainly occurred in the infrared region (700–1100 nm) and little variation in the visible region (400–700 nm). The NWI-3 ((R₉₇₀ – R₈₈₀)/(R₉₇₀ + R₈₈₀)) consistently showed a stronger association with yield than the RNDVI by using uncorrected canopy reflectance (original raw data) and data adjusted by scattering and smoothing. When canopy reflectance was corrected by a scattering method, the NWI-3 and a modified RNDVI with 958 nm showed the strongest correlations with grain yield by grouping lines for waxy leaves and spikes, curved leaves, and erect and awnless spikes. The results showed that the relationship between the spectral indices and grain yield can be improved (higher correlations) by correcting canopy reflectance for confounding effects associated with differences in leaf and spike morphology.     

Hyperspectral reflectance features of water hyacinth growing under feeding stresses of Neochetina spp. and different heavy metal pollutants

Spectral signatures of water hyacinth grown with biocontrol agents (Neochetina eichornia and N. bruchi) and various heavy metal pollutants were collected at the plant canopy level using a hand-held spectrometer to detect the biocontrol agent and heavy metal-induced plant stresses and the interaction between the two stressors. Water hyacinth was grown in 65l tubs, each with a single element from one of: As (1 mg l^?1), Au (1 mg l^?1), Cu (2 mg l^?1), Fe (0.5, 2 and 4 mg l^?1), Hg (1 mg l^?1), Mn (0.5, 2, and 4 mg l^?1), U (1 mg l^?1), and Zn (4 mg l^?1), with the exception of the control treatment. Three weeks after the metal treatments, the weevils were added to each tub, including those of the control treatment. Spectral measurements were taken before and after the addition of the weevils. Several spectral indicators of plant stress including red edge normalized difference vegetation index (RE-NDVI), modified red edge NDVI (mNDVI₇₀₅), modified simple ratio (mSR), photochemical reflectance index (PRI), and red edge position (REP) calculated using first derivative and linear extrapolation and water band index (WBI) were used to identify the plant stresses of water hyacinth. The spectral indicators of both metal and weevil plant stressors were correlated with the leaf chlorophyll content from the SPAD-502 readings at the end of the experiment. Correlations of mNDVI₇₀₅ with SPAD-502 readings were the highest followed by the indicators of REP. Cu-, Hg-, and Zn-treated plants showed significantly lower chlorophyll contents compared with the control treatment. A similar trend with four additional treatments (As, Fe-M, Mn-L, and Mn-H) was seen after the release of the weevils, indicating plant stress due to feeding by the biocontrol agent. However, adult and larval feeding was significantly reduced by Cu, Hg, and Zn elements, of which Cu was the most stressful. These results indicate that hyperspectral remote sensing has potential as a tool to determine the health status of water hyacinth from a remote location, to inform management interventions in control of the weed. However, its usage at a larger scale requires further studies.     

Spectral reflectance at sub‐leaf scale including the spatial distribution discriminating NPK stress characteristics in barley using multiway partial least squares regression

This paper introduces a methodology able to discriminate between non‐stressed plants and N, P and K stress symptoms in spring barley grown under controlled conditions, utilizing the spectral and spatial dimensions simultaneously. Nine spectral measurements in the range 450–1000 nm were taken for each plant. The measuring points were spatially located at the tip, middle and base of the last three fully developed leaves. This design generated a four‐way data set consisting of measurements as a function of (i) the specific plant, (ii) the spectral wavelength, (iii) the plant leaf position and (iv) the position on the leaf. Multiway partial least squares regression analysis with dummy variables was able correctly to classify the four nutrient conditions with 92% accuracy regardless of the respective growth stages within a time window of 2 weeks. The addition of the spatial dimension to the spectral dimension proved to be a promising nutrient diagnostic tool. Without performance loss it was possible to reduce the hyperspectral resolution to a resolution of three wavelengths. The three selected 2 nm wide bands were R450, R700 and R810, which agrees well with the literature on plant spectral reflectance in relation to nutritional stress.     

Distinguishing mangrove species with laboratory measurements of hyperspectral leaf reflectance

As a first step in developing classification procedures for remotely acquired hyperspectral mapping of mangrove canopies, we conducted a laboratory study of mangrove leaf spectral reflectance at a study site on the Caribbean coast of Panama, where the mangrove forest canopy is dominated by Avicennia germinans, Laguncularia racemosa, and Rhizophora mangle. Using a high‐resolution spectrometer, we measured the reflectance of leaves collected from replicate trees of three mangrove species growing in productive and physiologically stressful habitats. The reflectance data were analysed in the following ways. First, a one‐way ANOVA was performed to identify bands that exhibited significant differences (P value<0.01) in the mean reflectance across tree species. The selected bands then formed the basis for a linear discriminant analysis (LDA) that classified the three types of mangrove leaves. The contribution of each narrow band to the classification was assessed by the absolute value of standardised coefficients associated with each discriminant function. Finally, to investigate the capability of hyperspectral data to diagnose the stress condition across the three mangrove species, four narrow band ratios (R ₆₉₅/R ₄₂₀, R ₆₀₅/R ₇₆₀, R ₆₉₅/R ₇₆₀, and R ₇₁₀/R ₇₆₀ where R ₆₉₅ represents reflectance at wavelength of 695nm, and so on) were calculated and compared between stressed and non‐stressed tree leaves using ANOVA.

Results indicate a good discrimination was achieved with an average kappa value of 0.9. Wavebands at 780, 790, 800, 1480, 1530, and 1550 nm were identified as the most useful bands for mangrove species classification. At least one of the four reflectance ratio indices proved useful in detecting stress associated with any of the three mangrove species. Overall, hyperspectral data appear to have great potential for discriminating mangrove canopies of differing species composition and for detecting stress in mangrove vegetation.     

An in situ study of the effects of surface anisotropy on the remote sensing of burned savannah

This Letter presents field‐based evidence of the perturbing effects of surface anisotropy on the remote sensing of burned savannah. The analysis is based on bidirectional spectral reflectance data collected at different solar illumination angles and convolved to Moderate‐resolution Imaging Spectroradiometer (MODIS) reflective bands. Results from a grass savannah site show that burning reduces the anisotropy of the surface compared to its pre‐burn state. In contrast, at a shrub savannah site, burning reduces or increases surface anisotropy. Spectral indices defined from 1.240 µm and 2.130 µm reflectance, and 1.640 µm and 2.130 µm reflectance, provided stronger diurnal separation between burned and unburned areas than individual reflectance bands but do not eliminate anisotropic effects. The Normalized Difference Vegetation Index (NDVI) provides weak diurnal separation relative to these near‐ and mid‐infrared based indices. Implications of the findings are discussed for burned area mapping.     

Effects of heavy metals on the absorbance and reflectance spectra of plants

Abstract.

Published work on the reflectance of vegetation growing over soil mineralizations is reviewed. Experimental work was carried out on several species grown in a glasshouse and was extended to a pilot field study. In the laboratory studies, the most general effects of Cd, Cu, Pb or Zn were growth inhibition. A detailed study of the leaf pigments of pea plants showed that the chl a/chl b ratio (chl=chlorophyll) decreased under conditions of Cd or Cu stress but showed little effect with Pb or Zn. However, the absorption spectra of chloroplast pigments were not found to show any wavelength shifts with metal treatments, indicating that new spectral forms of chlorophyll were not produced as a stress response. A decrease in the total chlorophyll content of leaf tissue (fresh weight basis) was correlated with an increase in visible-wavelength leaf reflectance (R) of pea plants. R at infrared wavelengths of 0.85 μm, 1.65 μm and 2.20 μm decreased in metal-treated plants, compared with controls. Although experiments with other species, and a review of published literature, indicate that reflectance effects are dependent on species, phase of growth cycle and environment, the existence of correlations between R at certain wavelengths and the metal concentrations to which roots are exposed was confirmed using oak trees growing naturally in the area of a copper-arsenic mineralization in south-west England. Metal (Cu or As) concentrations in the soil were strongly negatively correlated (p > 99 per cent) with R at 1.65 μm and 2.20 μm, and positively correlated (p > 95 per cent) with R at 0.660 μm, in close agreement with the experiments on pea plants. The inclusion of the relevant infrared bands on Earth resource survey instruments is likely to enhance their usefulness for detecting heavy metal stress in plants.     

Potential signatures of heavy metal complexes in lichen reflectance spectra

Lichens are sensitive to atmospheric pollutants emitted from anthropogenic activities and are thus effective biomonitors. A variety of heavy metals, such as nickel (Ni), iron (Fe), lead (Pb), copper (Cu), and cadmium (Cd), can be emitted by metal smelters. The purpose of this study was twofold: (1) to measure the spectral reflectance properties (350–2500 nm) of expected heavy metal complexes in lichens (oxalates and sulphides); and (2) to determine whether these complexes contribute features to reflectance spectra of lichens from the vicinity of a heavy metal smelter. Some metal oxalate spectra are characterized by crystal field transition absorption bands in the 500–1300 nm region, which are specific to the particular metal cation they contain and its oxidation state. The 1900–2500 nm region exhibits multiple absorption bands attributable to the oxalate molecule. The metal sulphide reflectance spectra are characterized by generally low reflectance and few if any strong or diagnostic spectral features; those that are found can be related to a specific cation and its oxidation state. These spectra were used to determine whether reflectance spectra of a diverse suite of lichens collected downwind of a smelter showed spectral evidence indicative of heavy metal oxalates or sulphides. The lichen spectra, coupled with the oxalate and sulphide spectra and independently determined heavy metal concentration, failed to reveal spectral features that could be unambiguously related to heavy metal complexes. This was likely due to a number of causes: lichen reflectance spectra have absorption bands that overlap those of oxalates; oxalate and sulphide concentrations may have been too low to allow for their unambiguous identification, and lichen spectra are naturally diverse in the region below 1300 nm. There were no strong or significant linear trends between metal concentrations and distance from the smelter (coefficient of determination (R²) values <0.05), or between absorption band depths in the lichen spectra and distance from the smelter (R² values <0.06). This was likely due to the inclusion of multiple lichen species in the analysis, which may interact with airborne pollutants in different ways, and microenvironmental effects.