• green laser return intensity (GLRI);
  • leaf pigments;
  • light detection and ranging (LiDAR);
  • non-photochemical quenching (NPQ);
  • photochemical reflectance index (PRI);
  • remote sensing;
  • terrestrial laser scanning (TLS);
  • xanthophyll cycle


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • 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.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

When green plants experience environmental stress (e.g. non-optimal availability of water, light or nutrients), photosynthetic carbon dioxide (CO2) assimilation generally decreases. Even under optimal conditions, plants in all but deeply shaded environments are routinely exposed to light intensities that exceed their capacities for photosynthetic light utilization (Kok, 1956). Environmental stress generally exacerbates excess light stress, as the level of excess light is a function of light intensity and photosynthetic light use (e.g. Logan et al., 1998a,b; for a review, see Björkman, 1981). In cases in which excess light is incident on a leaf, a range of photoprotective mechanisms work to minimize light-mediated cellular damage (for a review, see Melis, 1999).

Thermal energy dissipation, a ubiquitous photoprotective mechanism, depends on the conversion of constituents of the xanthophyll cycle from violaxanthin (V) to antheraxanthin (A) and zeaxanthin (Z) via successive, enzyme-catalyzed de-epoxidations (Yamamoto, 1979). Thermal energy dissipation converts absorbed excitation energy to heat, which can then be lost to the environment across the leaf lamina. Levels of thermal energy dissipation generally increase during exposure to environmental stress (for a review, see Demmig-Adams & Adams, 1996, 2006), and can be regulated on short time scales (i.e. tens of seconds to minutes) via alterations of the xanthophyll cycle de-epoxidation state ([Z + A]/[V + A + Z]) (e.g. Hartel et al., 1999; Peguero-Pina et al., 2013). Chlorophyll fluorescence emission can be used to measure ‘non-photochemical quenching’ (NPQ), which is a common method for the quantification of the level of thermal energy dissipation (Schreiber et al., 1986; Bilger & Björkman, 1990; Müller et al., 2001). Although strong evidence supports Z as the primary pigment responsible for thermal energy dissipation in most plant taxa (Demmig-Adams, 1990; Demmig-Adams & Adams, 1992, 2006), Z-independent forms of thermal energy dissipation have also been described (Bungard et al., 1999; Ruban et al., 2007).

De-epoxidation of the xanthophyll cycle results in a decrease in leaf reflectance between c. 510 and 550 nm centered at 531 nm (Gamon et al., 1990, 1992; Peñuelas et al., 1995; Gamon & Surfus, 1999). This decrease can be detected using remote sensing devices capable of the passive measurement of narrowband (c. 1–10 nm bandwidth) spectral reflectance. Typically, the absorption at 531 nm is used in conjunction with a reference band at 570 nm that is insensitive to changes in xanthophyll cycle de-epoxidation state to calculate a normalized index, known as the photochemical reflectance index (PRI) (Eqn 1) (Gamon et al., 1992):

  • display math(Eqn 1)

Strong correlations have been reported between PRI and metrics associated with the xanthophyll cycle (de-epoxidation state, NPQ) at the leaf and canopy scales (e.g. Gamon et al., 1990; Gamon & Surfus, 1999; Richardson & Berlyn, 2002; Filella et al., 2004b; Rahimzadeh-Bajgiran et al., 2012). Thus, owing to their spectral reflectance signal, changes in xanthophyll cycle composition can be tracked via passive remote sensing platforms from the leaf to ecosystem scale (see reviews by Garbulsky et al., 2011; Peñuelas et al., 2011).

Although much progress has been made on the passive, remote detection of plant responses to environmental stress over the last several decades (Barton, 2012), many challenges associated with the use of the PRI persist. These challenges include accounting for canopy structural differences and changes (e.g. in leaf area, leaf orientation and leaf angle distribution; Barton & North, 2001; Garrity et al., 2010), background effects (e.g. woody or dead canopy material, soil, shadows; Barton & North, 2001) and differences in leaf chlorophyll or carotenoid pools (Filella et al., 1996; Sims & Gamon, 2002; Stylinski et al., 2002; Garrity et al., 2011) – although advances at a variety of spatial scales have been reported. These recent advances have allowed for the inference of photosynthetic performance of forest canopies by accounting for viewing and illumination geometry through multi-angle measurements that relate the rate of change in PRI to canopy shadow fraction (e.g. Hall et al., 2008, 2011, 2012; Hilker et al., 2008, 2009, 2010, 2011, 2012). By building on these recent advances, some of the challenges associated with the passive remote sensing of PRI may be further overcome with the use of active remote sensing systems, such as terrestrial laser scanning (TLS, see the 'Discussion' section).

Traditionally, TLS technology has been used to map and model plant structural properties (e.g. Lovell et al., 2003; Hopkinson et al., 2004; Watt & Donoghue, 2005; Clawges et al., 2007) by the quantification of the x, y, z locations of plant canopy components at very fine (sub-centimeter) resolution. Locations are calculated using the angle-specific round-trip time-of-flight (t) of a laser pulse between the sensor and the target (distance = (ct)/2, where c is the speed of light). In addition, the TLS measures the return intensity of the returning laser pulse. Because the 532-nm laser is centered within the spectral region known to be affected by de-epoxidation of the xanthophyll cycle, we hypothesize that this wavelength could tease apart the three-dimensional (3-D) variability in the levels of thermal energy dissipation at the leaf and canopy scale. Following the theoretical work of Morsdorf et al. (2009) and Woodhouse et al. (2011) – who demonstrated the potential for measuring detailed forest structural and physiological status using light detection and ranging (LiDAR) through modeling exercises – we present the first attempt to characterize changes in the xanthophyll cycle using green scanning LiDAR.

Although research to determine vegetation structure with terrestrial LiDAR systems is rapidly expanding (e.g. Keightley & Bawden, 2010; Moorthy et al., 2011; Sanz-Cortiella et al., 2011; Vierling et al., 2012; Fernández-Sarría et al., 2013), research examining the potential of LiDAR systems to determine plant biochemical properties is just emerging (Eitel et al., 2010, 2011; Gaulton et al., 2013). Recent research by Eitel et al. (2010, 2011) suggests that laser scanning technology could be a useful tool to accurately map biochemical properties at both the leaf and canopy scale. They showed that the laser return intensity of a green (532-nm) scanning laser is significantly correlated with foliar chlorophyll content (Eitel et al., 2010) and total foliar nitrogen (Eitel et al., 2011). In addition, work by Gaulton et al. (2013) has found that dual-wavelength laser scanning using near-infrared (1063-nm) and middle-infrared (1545-nm) wavelength lasers can be employed to estimate leaf moisture content. However, there has yet to be any published research demonstrating the use of a TLS system to measure dynamic changes in leaf physiological state.

The objective of this work was to test our hypothesis that variations in reflected green laser return intensity (GLRI) at 532 nm, measured by TLS, can track temporal changes in thermal energy dissipation at the leaf scale. We assessed the relative abilities of GLRI and PRI to predict NPQ and the xanthophyll cycle de-epoxidation state. This study was conducted at the leaf scale under controlled laboratory conditions to obtain a fundamental understanding of the suitability of GLRI for the assessment of leaf photoprotective mechanisms. Following a report on our findings at the leaf scale, we discuss prospects for scaling the TLS intensity data to a 3-D canopy space.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Plant material

A total of 19 winter wheat plants (Triticum aestivum L.), 12 broadleaf saplings (four bur oak (Quercus macrocarpa Michx.), four paper birch (Betula papyrifera Marshall), four aspen (Populus tremuloides Michx.)) and nine sunflowers (Helianthus annuus L.) were grown in a glasshouse over a 4-month span (22 September 2011 to 14 December 2011). To achieve a wide range of leaf pigment compositions (e.g. Logan et al., 1999), plants were grown under different nitrogen fertilization and light regimes. For wheat, a randomized complete block design with three nitrogen fertilization treatments (low (none), medium (50 ml of 20-20-20 NPK diluted in 300 ml of water, once per month) and high (50 ml of 20-20-20 NPK diluted in 300 ml of water, once per week)) was used. Sunflowers and all tree saplings received fertilizer (50 ml of 20-20-20 NPK diluted in 300 ml of water) every third week.

The sunflower and broadleaf saplings were chosen because of their large leaf area, providing increased sample laser scan area, ease of instrument attachment and adequate leaf material for pigment samples to be harvested during the experiment (see Gamon et al., 1990; Eitel et al., 2010). Wheat was chosen because previous research by Eitel et al. (2011) demonstrated strong correlations between GLRI and biochemical properties (chlorophyll and nitrogen). All plants received replete water and were grown under 200–800 μmol photons m−2 s−1 of light for 8–10 h daily. The wheat was sampled during Zadkos growth stage 3.2 (stem elongation), the sunflowers immediately before flowering and the saplings during their ninth month of growth.

Experimental protocol

The experiment was designed to induce NPQ and thus initiate a range of xanthophyll cycle de-epoxidation states. Plants were transferred from the glasshouse to an unilluminated room (< 2 μmol photons m−2 s−1) for acclimation of > 1 h to allow relaxation of thermal energy dissipation consequent with the epoxidation of xanthophyll cycle carotenoids (i.e. return to V). Before measurement, some background leaves were removed and the leaf under study was placed in the measurement position (see Figs 1, 2). One sample leaf from each plant was chosen on the basis of leaf color homogeneity and leaf size. For wheat, four leaves were aligned in parallel and affixed to each other, creating a ‘flat’ sampling plane.


Figure 1. Top–down view of experimental design. Computer 1 operated the terrestrial laser scanner (TLS). Computer 2 operated the spectroradiometer. Computer 3 operated the pulse-amplitude modulation (PAM) fluorometer.

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Figure 2. Instrumentation set-up. Sunflower (Helianthus annuus) after fourth light regime. PAR, photosynthetically active radiation.

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The leaf lamina was placed at an angle perpendicular to the laser (Fig. 1). Transparent thermoplastic barriers between lamps and the plant under study acted as thermal buffers (Fig. 1). At the onset of a measurement, maximal chlorophyll fluorescence emission was measured in darkness, followed immediately by a laser scan, and collection of a leaf disk. The leaf under study was then exposed to three increasing light intensities (150, 500 and 1000 μmol photons m−2 s−1) generated by a bank of high-intensity discharge metal halide lamps (Fig. 1). During exposure to each of the three light intensities, chlorophyll fluorescence emission, reflectance spectra, incident photosynthetically active radiation (PAR), a TLS scan and leaf temperature were measured – and leaf tissue was harvested for xanthophyll cycle pigment composition, in that order.

Chlorophyll fluorescence emission

The analysis of chlorophyll fluorescence emission was performed using an FMS2 fluorometer (Hansatech Instruments, Kings Lynn, Norfolk, UK). Plants were acclimated to low light (< 2 μmol photons m−2 s−1) for 1 h before the measurement of the maximal fluorescence emission (Fm) during exposure to a 0.8-s saturating pulse of light (> 3000 μmol photons m−2 s−1) generated by the instrument. After a period of acclimation sufficient to allow fluorescence during illumination (i.e. Fs) to achieve steady state (c. 2–5 min), the maximal fluorescence (Fm′) during exposure to a saturating pulse of light was recorded. The duration of the acclimation period varied depending on species, treatment and light intensity. NPQ of chlorophyll fluorescence was calculated as [(Fm/Fm′) – 1] according to Bilger & Björkman (1990).

Pigment analysis

After the dark > 1-h time period and at each light intensity, a 0.25-cm2 disk of leaf tissue was removed using a cork borer and immediately frozen in liquid N2 for the determination of xanthophyll cycle pigment composition. Leaf disks were collected immediately following the measurement of chlorophyll fluorescence emission to capture the xanthophyll cycle de-epoxidation state associated with NPQ values calculated from fluorescence analysis. Leaf disks were stored at −80°C until extraction in acetone according to Adams & Demmig-Adams (1992) with the following modification: disks were ground using a ball mill (8000D, Specs Certiprep, Metuchen, NJ, USA) modified to accommodate 20-ml micro-centrifuge tubes. Pigment separation and quantification were achieved by high-performance liquid chromatography, as described in Gilmore & Yamamoto (1991), using an Agilent 1100 series HPLC (Agilent Technologies, Palo Alto, CA, USA) equipped with a YMC Carotenoid™ C-30 reverse phase column (YMC Co., Ltd, Kyoto, Japan) at 35°C with the following modification to the solvent gradient: 0–4 min (72 : 8 : 3, acetonitrile : methanol : 0.1 M Tris-HCl (pH 8.0)) followed by a linear gradient to 80% (4 : 1, methanol : hexanes) from 4 to 40 min, and the completion of the quantification with the latter mobile phase. The level of de-epoxidation of the xanthophyll cycle was expressed as the conversion state as a fraction of the total xanthophyll cycle pool (Z + A)/(V + A + Z), because of the involvement of Z and A in the energy dissipation process (Gilmore & Yamamoto, 1993).

Spectral readings

Leaf radiance was detected using a narrow-bandwidth (8 nm full width at half-maximum) ASD FieldSpec Pro spectroradiometer (Analytical Spectral Devices, Boulder, CO, USA). The fiber optic probe (field of view of 20°) was placed in a nadir orientation, 2 cm from the adaxial side of the leaf, and was mounted atop the fluorometer clip (see Fig. 2). Before the first spectral measurement at each light intensity, a dark current measurement and a white reference measurement using a white Spectralon (Labsphere Inc., North Sutton, NH, USA) disk were collected. Four spectral measurements were used from each leaf, the first of which was taken immediately (within seconds) after the dark-acclimated leaf had been exposed to the first light regime. The following three readings were taken after the leaf had acclimated to the new light regime (c. 2–5 min, depending on species, treatment and light intensity). The acclimation period was determined via steady-state fluorescence emission as described above. Leaf spectral radiances were converted to reflectance using the white reference panel radiance as a standard, and then used to calculate PRI (Eqn 1).

PAR sensor and leaf temperature

For each sample, PAR and leaf temperature were recorded at each light intensity (data not shown). A PAR sensor (LI-COR, Lincoln, NE, USA) was placed directly adjacent to the leaf, parallel to its adaxial side (Fig. 2), with PAR recorded at every light regime. A range of ± 20 μmol photons m−2 s−1 around each target light intensity was observed. Leaf temperature was frequently measured by a non-contact infrared thermometer (LIT11TC Duo Therm, Supco Inc., Allenwood, NJ, USA) to ensure that heat from the lamps did not unduly affect leaf physiology or cause cuticle damage and potentially skew the spectral reflectance data (e.g. Serbin et al., 2012). A range of no more than ± 1°C was observed between the first and last light regimes on any given leaf (data not shown).

Terrestrial laser scanning

The terrestrial laser scanner used in this experiment was the Leica ScanStation 2 with a 532-nm-wavelength laser (Leica Geosystems Inc., Heerbrugg, Switzerland). This scanner has a beam diameter of 4 mm at a range of 0–50 m from the target, a scan rate of up to 50 kHz, a maximum sampling density of < 1 mm and a maximum range of 134 m at 18% albedo (Leica Geosystems Inc.). Position accuracy is ± 6 mm and distance accuracy is ± 4 mm. The GLRI, or the amount of laser light reflected back to the sensor, is recorded with a dynamic range of possible digital numbers (DNs) from −2047 to 2047.

A second white reference panel (Labsphere Inc.) was mounted on a tripod directly above and parallel to the leaf sample (see Fig. 1). Each leaf and panel were scanned from a single position at a distance of 1.5 m. The laser point spacing was set at 1 mm, resulting in a scan duration of c. 10 s. After scanning, the reference panel and each leaf sample were visually identified in Cyclone software (Leica Geosystems Inc.), and a point cloud was created and exported as an ASCII file for further analysis. The laser scanning areas of interest included the entire white reference panel and as much of the leaf as could be exported without including areas of pigment extraction or the fluorometer clip (see ‘area of interest’ in Fig. 2). To minimize the effect of the inverse distance square law of radiation and leaf angle effects on the laser return intensity (e.g. Häckel, 1999), both the leaf angle and distance between the laser and leaf were kept constant.

Preprocessing of TLS data

Computer code written in the open-source software package R (version 2.11.1, R Development Core Team, 2010) was used to preprocess each leaf and reference panel point cloud employing a multi-step process (the program is freely available on request from the authors). Return intensity values for the leaf laser points were first normalized by dividing the mean average laser return intensity of the white reference panel point cloud. The mean of the normalized leaf returns within the area of interest was then calculated. A total of four intensity return values (one for each light regime) for each leaf was therefore utilized for further analyses. Because the aim of this paper is towards the eventual evaluation of plant photoprotective mechanisms at the canopy scale, opposed to site-specific stress responses at the leaf scale, the average GLRI response for an entire leaf was deemed to be most relevant.

Statistical analysis

Pearson's correlation coefficient (r) was computed to assess the magnitude and direction of the association between each predictor (GLRI and PRI) and dependent (NPQ and (Z + A)/(V + A + Z)) variable for each leaf using MATLAB (2012, The MathWorks, Natick, MA, USA). In addition, the adjusted coefficient of determination (r²) from a simple linear regression model was used to determine the strength of relationships between each predictor (GLRI and PRI) and dependent (NPQ and (Z + A)/(V + A + Z)) variable for individual leaves. Individual regression models were computed on a single leaf basis to account for the variance of light energy conversion between plants (Runyon et al., 1994). An example of how these correlations were derived is shown in Fig. 3.


Figure 3. Correlations between predictor and explanatory variables for a single sunflower leaf. The colors associated with green laser return intensity (GLRI) values are coincident with light intensity regimes 1, 2, 3 and 4 for the same leaf (represented in the data). The images of the leaf were created by increasing the size of the ‘pixel’ for visual appeal and are ‘non-normalized’, as the intensity return value of the white reference panel was consistent throughout the four light regimes. The leaf shape appears slightly different in the visual representations because software limitations and user ‘cropping’ techniques limit the exact visual three-dimensional replication of the same leaf. NPQ, non-photochemical quenching; PRI, photochemical reflectance index; A, antheraxanthin; V, violaxanthin; Z, zeaxanthin.

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If our hypothesis is supported, a strong (r < 0, r² > 0.5) negative correlation (negative r, high r²) should be observed between predictor and dependent variables. However, a strong positive correlation could also yield a high r² value. Thus, we assigned an r² value of zero to any positive correlations (indicated by Pearson's r > 0) in our cumulative assessment of individually derived correlations (= 2, as seen in Fig. 5). Cumulative representations of the PRI and GLRI predictive power were demonstrated using a paired boxplot (Fig. 5).

The statistically significant difference between PRI and GLRI was examined using a two-sample t-test of the cumulative r² populations reported in the boxplot (Fig. 5). P < 0.05 was used as the threshold of statistical significance.

No statistical test was used to assess the direction of the trends of GLRI and PRI with (Z + A)/(V + A + Z) or NPQ (Figs 6, 7). The trends provide visual insight into the comparison between PRI and GLRI with respect to their utility to predict NPQ and (Z + A)/(V + A + Z). These trends were drawn using the mean value and one standard deviation of all within-species samples. Maximum GLRI was normalized to the maximum PRI value associated with each leaf. In some instances, the number of leaves used for the analysis of a given parameter was less than the number of leaves sampled because data collection errors led us to omit (n < 4).


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Qualitative changes in GLRI

Because TLS can be used to produce laser return intensity maps for individual leaves, marked changes in the de-epoxidation of the xanthophyll cycle, coincident with an increase in PAR, were detectable in most cases (e.g. Figs 3, 4). Examples of correlations between GLRI/PRI and NPQ/(Z + A)/(V + A + Z) are also shown in Fig. 3. As hypothesized, an increase in PAR yielded an increase in NPQ, an increase in conversion of the xanthophyll cycle to Z + A, and a decrease in GLRI for this particular leaf (Fig. 3). This decrease in GLRI is qualitatively observable on the leaf surface with an increase in oranges and reds as PAR increases (Fig. 3), and is quantitatively shown in Fig. 3(a,c) with coefficients of determination of 0.91 (NPQ) and 0.99 ((Z + A)/(V + A + Z)).


Figure 4. Green laser return intensity (GLRI) color maps for aspen (Populus tremuloides; a, b), birch (Betula papyrifera; c, d), oak (Quercus macrocarpa; e, f) and wheat (Triticum aestivum; g, h), highlighting the spatially explicit changes on the leaf face from darkness to high light stress. The images of the leaf were created by increasing the size of the ‘pixel’ for visual appeal and are ‘non-normalized’, as laser return intensity values of the white reference panel were consistent throughout the four light regimes.

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In addition, a color map of GLRI from the other four species under study in darkness and after exposure to the fourth light regime (c. 1000 μmol photons m−2 s−1; Fig. 4) highlights the variation in GLRI values at the high spatial resolution afforded by TLS. The quantified average GLRI values were 0.099 (Fig. 4a) and 0.052 (Fig. 4b) for aspen, 0.133 (Fig. 4c) and 0.082 (Fig. 4d) for birch, 0.049 (Fig. 4e) and 0.028 (Fig. 4f) for oak, and 0.105 (Fig. 4g) and 0.092 (Fig. 4h) for wheat. Red pixels on the leaf edge are a result of a ‘mixed-pixel’ effect (also known as mixed-edge effect, ghost returns or air returns; see Hebert & Krotkov, 1992 and Eitel et al., 2013 for further discussion), where a single GLRI value integrates reflectance properties from two surfaces as the laser point is split into two pulses by the leaf edge. Because objects behind the leaf were darker than the leaf itself, these points produce noticeably lower GLRI values (e.g. see red values at the leaf edge). Mixed-pixel hits were therefore not included in quantitative analysis (see ‘area of interest’ in Fig. 2).

GLRI and PRI estimation of NPQ

The relationship between NPQ and GLRI for individual leaves exhibited median coefficients of determination ranging from 0.52 (wheat) to 0.78 (sunflower), whereas the median coefficients of determination between NPQ and PRI ranged from 0.78 (wheat) to 0.84 (sunflower) (Fig. 5). When pooled across all species, we found no significant difference in the ability to predict NPQ using PRI vs using GLRI. The smaller variance and higher median values of individual leaf regression models show that the strongest results relating GLRI and PRI to NPQ occurred in sunflower. Overall, the median coefficient of determination for GLRI–NPQ was 0.71, with the interquartile range of r² values falling between 0.42 and 0.88, whereas PRI–NPQ produced a median value of 0.81 and interquartile range of 0.65–0.89.


Figure 5. Boxplots of coefficients of determination for individual regression models categorized by species. The box represents the interquartile range of the data (the 25th and 75th percentiles), and the whiskers represent the inner 10th and 90th percentiles, in some cases eliminating an outlier. GLRI, green laser return intensity; NPQ, non-photochemical quenching; PRI, photochemical reflectance index; A, antheraxanthin; V, violaxanthin; Z, zeaxanthin.

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Of the 47 individual leaf regression models computed for the prediction of NPQ, there were two leaves in which the GLRI trend yielded a Pearson's r > 0 (negative slope, no predictive power) with an increase in NPQ, whereas PRI–NPQ relationships yielded strong negative correlations for all 47 leaves. The two leaves that suggested positive correlations for GLRI and NPQ were wheat leaves with the lowest relative nitrogen and chlorophyll concentrations. Generally, as nitrogen and chlorophyll concentrations increased in wheat leaves, the predictive power of GLRI and PRI in ascertaining NPQ also increased (median r² for low N (lowest third) plants = 0.36, medium N (middle third) = 0.56, high N (highest third) = 0.72).

GLRI and PRI estimation of xanthophyll pigment pool (Z + A)/(V + A + Z)

For the prediction of (Z + A)/(V + A + Z), the median coefficients of determination for GLRI regression models ranged from 0.50 (saplings) to 0.87 (sunflower) and, for PRI, from 0.56 (wheat) to 0.80 (sunflower) (Fig. 5). No significant difference was found when using GLRI or PRI to remotely estimate foliar xanthophyll cycle de-epoxidation across all species. The smaller variance and higher median values of individual leaf regression models suggested that sunflower produced the strongest results for both GLRI and PRI. As with NPQ, the lower variance and lower median coefficients of determination suggested that the strongest regression models were associated with sunflower. The median coefficient of determination of GLRI vs (Z + A)/(V + A + Z) across all species was 0.71 with the middle 50% of the individual species r² values falling between 0.38 and 0.89, whereas PRI vs (Z + A)/(V + A + Z) exhibited a median value of 0.72 and interquartile range of 0.40–0.90.

Similar to the results reported in the section on GLRI and PRI estimation of NPQ, there were two leaves in which the GLRI trend yielded a Pearson's r > 0 (negative slope, no predictive power) with an increase in (Z + A)/(V + A + Z). PRI vs (Z + A)/(V + A + Z) yielded strong negative correlations for all but one regression model. This outlier also yielded a positive correlation in GLRI. The two leaves in the regression models that revealed positive correlations were also the leaves that exhibited positive correlations for NPQ.

GLRI and PRI trends with increasing NPQ and (Z + A)/(V + A + Z)

All but two leaves under study showed marked increases in both metrics of photoprotective thermal energy dissipation (NPQ and (Z + A)/(V + A + Z)) with increasing light intensity. Our remote sensing predictor variables, GLRI and PRI, tracked this increase in a negative fashion across species (Figs 6, 7). Interestingly, GLRI and PRI were most sensitive during the initial stages of increased NPQ and the latter stages of xanthophyll pigment interconversion. Further research is needed to explain this sensitivity. Bars in Figs 6 and 7 showing one standard deviation around the mean for GLRI and PRI suggest that there is no indication that GLRI is over- or under-predicting PRI during any stage of NPQ or the xanthophyll cycle.


Figure 6. Trends in the aggregated mean of within-species models of the photochemical reflectance index (PRI) and green laser return intensity (GLRI) over xanthophyll pigment interconversion ((Z + A)/(V + A + Z)). GLRI was normalized for visual purposes by setting the highest GLRI value equal to the highest PRI value for each leaf model. Error bars represent 1 SD. A, antheraxanthin; V, violaxanthin; Z, zeaxanthin.

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Figure 7. Trends in the aggregated mean of within-species models of the photochemical reflectance index (PRI) and normalized green laser return intensity (GLRI) over non-photochemical quenching (NPQ). GLRI was normalized for visual purposes by setting the highest GLRI value equal to the highest PRI value for each leaf model. Error bars represent 1 SD.

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  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The near-equivalent performances of GLRI and PRI in the prediction of xanthophyll cycle-dependent light energy dissipation (Figs 5-7) suggest that laser-based intensity measurements (such as GLRI) can be used to assess dynamic changes in xanthophyll cycle de-epoxidation at the leaf scale. This finding is a fundamental advance that supports previous foundational and theoretical work (Morsdorf et al., 2009; Woodhouse et al., 2011; Wallace et al., 2012), suggesting that laser measurements might be useful in ascertaining foliar physiological information in three dimensions. In addition, several patterns in the tracking of leaf physiological properties using active and passive remote sensing emerged from this work that should not be overlooked in future scaling exercises.

First, we noticed that, as chlorophyll content increased in leaves (a result of differing nitrogen treatments), the relationships between GLRI/PRI and NPQ/((Z + A)/(V + A + Z)) improved. This could potentially be explained by the strong correlation between bulk xanthophyll cycles (V + A + Z) and chlorophyll (a + b) content on a leaf area basis (μmol m−2) (r = 0.76, across all species in this study). Because the leaves used in this study were grown in low light conditions and mostly absent from situations of high light stress, leaves with low chlorophyll levels may not have needed to develop large xanthophyll cycle pigment pools, which could be superfluous in situations in which a high capacity for photoprotection is not routinely required. The poor PRI/GLRI response in leaves with low chlorophyll content (and concurrently low xanthophyll cycle contents) may be the result of the low optical detectability of a small physiological response. It is worth noting that a relatively large difference in xanthophyll cycle de-epoxidation or NPQ corresponds to a narrow range of differences in PRI or GLRI, which means that a small variation or error in the latter could translate into a larger difference in physiological measures. The above finding is consistent with the observations of other studies (Gamon et al., 2001; Filella et al., 2004a,b; Nakaji et al., 2006; Garrity et al., 2011; Rahimzadeh-Bajgiran et al., 2012), which demonstrate that the relationship between PRI and the xanthophyll pigment interconversion is weaker in leaves with low chlorophyll content. On longer time scales (days to weeks), PRI responds to variables other than xanthophylls (i.e. chlorophyll/carotenoid ratio) and, as a result, it is necessary to correct PRI (or GLRI) measurements by incorporating the effect of other photosynthetic pigments (Sims & Gamon, 2002; Filella et al., 2009; Garrity et al., 2011; Rahimzadeh-Bajgiran et al., 2012).

Second, in leaves in which concentrations of Z and A, and levels of NPQ, were higher (generally sunflower, saplings, wheat – in that order), GLRI and PRI performed relatively better as predictor variables. This could simply be explained by the wider range of xanthophyll de-epoxidation states triggered by species-specific responses (see Figs 6, 7), which, in turn, would induce a wider range in spectral variation at 532 nm. Another explanation could be that our strongest results come from the thinnest, flattest leaves under study; although not the aim of this research, further work should be performed to test how GLRI could be affected by leaf structure (rigidity, leaf lamina) and cellular structure (leaf mesophyll and epidermis – see Ollinger, 2011 for further discussion).

A ‘finer frontier’ for remote estimation of photoprotection

Great strides have been made over the past two decades to better quantify net ecosystem exchange (NEE) of CO2 (e.g. Baldocchi, 2008). However, the complexity of observing and modeling NEE over broad areas remains a challenge because carbon exchange is highly variable in time and space (Le Quéré et al., 2009). The documentation, understanding and forecasting of the cycling of carbon in response to global environmental change therefore rely heavily and increasingly on remote sensing systems (Running et al., 2004). Because the modeling and prediction of carbon sources and sinks at leaf, canopy and ecosystem scales require a nuanced understanding of vegetation structure and physiology (Schurr et al., 2006), remote sensing approaches must be responsive to physiological and structural vegetation changes that are relevant to carbon uptake by photosynthesis (Ustin & Gamon, 2010). A recent letter (Peñuelas et al., 2011) and Tansley review (Ustin & Gamon, 2010) published in this journal identified key challenges associated with passive remote estimates (namely, the PRI) of plant stress and function. Some of these challenges include accounting for canopy structural differences and changes, viewing and illumination geometry, background effects, changes in illumination conditions, and poor spectral and/or spatial resolution. An active, laser-based system could work to minimize these challenges.

First, a major challenge of passive optical remote sensing has been the inability to understand that plant spectral reflectance resulting from changes in leaf pigment activity varies among functional types (Gamon et al., 1997), and is complicated by vegetation structure (Barton & North, 2001; Middleton et al., 2009). Because leaf spectral response is often dictated by both canopy biochemical (chlorophyll, nitrogen, xanthophylls) and biophysical (leaf angle distribution and orientation) status, difficulties in interpreting the data from remote sensing platforms can arise. As a potential solution, the high-resolution imaging capability of vegetation 3-D structure with TLS, which can be used to determine leaf angle distribution and orientation, coupled with dynamic 3-D changes in biochemical conditions, could provide new insights into spatially explicit patterns of physiological function on the leaf scale (Figs 3, 4) and the canopy scale (as discussed in the following section).

Second, Hall et al. (2008) first demonstrated that differences in canopy PRI resulting from differing viewing and illumination geometries can be used to detect xanthophyll-induced changes at 531 nm. This was accomplished using remote sensing instruments and techniques that allow canopy spectra to be recorded at multiple angles (i.e. multi-angular spectroradiometer platforms capable of near-ground measurements; Hilker et al., 2009, 2010). A more mechanistic understanding of the relationship between canopy function and structure issues may be accomplished by coupling multi-angle imaging spectroscopy with LiDAR, thereby isolating subtle variations in vegetation reflectance associated with changes in xanthophyll cycle de-epoxidation. In addition to fusing passive spectral and active LiDAR data at the canopy scale to understand canopy function, there is wide interest in exploring ‘single-system’ techniques (such as TLS), which have the potential to improve upon the 3-D spatial understanding of leaf and canopy biochemistry (Omasa et al., 2007; Eitel et al., 2010, 2011; Gaulton et al., 2013). Green scanning TLS could eventually aid in research aimed at unraveling the ‘canopy conundrum’ (see Grace, 2007; Nichol et al., 2012), whereby the parameterization of vegetation structure and function at the leaf scale could elucidate the large-scale photosynthetic processes controlled by canopy and ecosystem structure. For example, a TLS system could provide a vertical, spatially referenced, high-resolution profile of dynamic photosynthetic changes through a tall tree canopy.

Third, varying background effects (e.g. soil color, non-photosynthetic material) may considerably alter passive estimates of photosynthesis (e.g. Sims et al., 2006). The fine spatial resolution of TLS exemplified in Fig. 4 can help account for these effects, because the exact 3-D location of a particular reflectance measurement is known. Background laser returns coming from soil or non-photosynthetic material can also be easily separated from vegetation laser returns by the use of easily applied thresholds (Eitel et al., 2011).

Fourth, the advantage of lasers over traditional passive sensors is that the laser return intensity is not affected by ambient light conditions, and can be measured in the dark to acquire a baseline measurement of foliar reflectance at 532 nm (i.e. in the absence of photosynthesis). Further, the high sampling rate (50 000 points s−1 for this particular terrestrial laser scanner), combined with the small field of view of lasers (< 4 mm), allows pure green vegetation returns to be isolated from non-photosynthetic tissue (Eitel et al., 2010, 2011). This permits the measurement of the 532-nm laser return intensity signal from individual leaves and portions of leaves growing within a radius of up to 150 m from the laser source (in the case of the instrument used in this study).

Achieving three dimensions: considerations and limitations for the use of GLRI at the canopy scale

When implementing GLRI to study plant physiological status at the canopy scale, the interpretation of laser return intensity values might be confounded by returns from non-photosynthetic tissue, variations in leaf angle and the distance between the laser and the surveyed leaves (Eitel et al., 2010). To isolate intensity readings from non-photosynthetic tissue, a simple threshold can be used (Eitel et al., 2010, 2011). The distance readings provided by TLS can allow the inverse distance square law to be accounted for, whereby the intensity of light returned to the laser decreases as a function of the square of increasing distance from the surveyed object. A greater challenge for the implementation of TLS measurements to infer photosynthetic performance at the canopy scale involves accounting for leaf angle effects on laser return intensity. To minimize the confounding effects of leaf angle in this study and establish that GRLI can be used to derive leaf photoprotective status, leaf angle was kept constant. Hence, our results effectively represent two-dimensional measurements. However, in order for GLRI to provide novel 3-D insights into photosynthetic performance throughout plant canopies, techniques and methods are required that allow variations in leaf angle to be accounted for. Although recent work has successfully calculated the leaf angle using TLS data (e.g. Eitel et al., 2010; Balduzzi et al., 2011; Zheng & Moskal, 2012), this work has not yet produced algorithms sufficient to calculate the leaf angle automatically. Because automatic leaf angle calculation is essential for scaling our work to a typical vegetation canopy containing > 102 leaves, we are developing a robust, nearest-neighbor-based algorithm to automatically calculate the leaf angle of every laser return recorded by TLS (Fig. 8; J. Jiang et al., unpublished). Based on the leaf angle data provided by such an algorithm, laser return intensity values could be corrected for variations in leaf angle to accurately map changes in NPQ and xanthophyll pigment pools in three dimensions.


Figure 8. Leaf angle normals of (a) oak, (b) wheat leaves and (c) an entire wheat canopy. Lines emanating from each point on the leaves depict the leaf angle normals for each individual laser hit on the leaf surface, as calculated using an automated nearest-neighbor surfacing algorithm (J. Jiang et al., unpublished). Because the leaf angle can exert a strong control on the green laser return intensity (GLRI), this leaf angle information offers new prospects for scaling measurements made in the laboratory to actual field conditions in three-dimensions.

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The use of TLS systems employing multiple wavelengths (e.g. Strahler et al., 2008; Jupp et al., 2009; Morsdorf et al., 2009; Chen et al., 2010; Woodhouse et al., 2011; Hakala et al., 2012; Gaulton et al., 2013) could also help to reduce the effect of leaf angle and distance on laser return intensity values. By calculating ratios of laser returns, laser return intensity readings could be normalized for changes in leaf angle and distance, similar to widely employed spectral vegetation indices. Gamon et al. (1992) originally found that a wide range of reference bands accompanying the 531-nm ‘xanthophyll wavelength’ could be used to minimize the effects of overlapping spectral features on the ‘xanthophyll signal’ caused by sun and leaf angle variation, although 570 nm has been the most widely used wavelength in PRI calculations (see Garbulsky et al., 2011 for a review). Future developments in laser technology could aid in exploring the use of a reference band in active remote sensing of plant photoprotective mechanisms.

The power of active remote sensing data is that they eliminate the confounding effects of shadow and non-photosynthetically active elements that confound passive remote sensing metrics; however, another limitation inherent in any remote sensing approach is that a fixed view angle will not ‘see’ objects between the scanner and the outer leaves (as described in the work of Hall et al., 2008). By scanning the plant canopy from different viewing positions (e.g. Clawges et al., 2007), more photosynthetically active materials could be ‘seen’, providing a more complete picture of canopy photosynthetic performance. Notably, because photoprotective mechanisms respond to foliar-level illumination conditions (i.e. shaded leaves experience lower ‘light stress’ than sunlit leaves), the interpretation of canopy-level GLRI signals would be assisted by illumination data specific to the time of measurement. Because most TLS systems are outfitted with an onboard red–green–blue (RGB) digital camera, the digital image could be used to map instantaneous canopy shadow conditions to better interpret the GLRI variation on a leaf-by-leaf basis. Combination of the rapid scan time and RGB digital imaging afforded by TLS systems could aid in the development of a multi-angle data acquisition approach that accounts for shadow fraction, whereby TLS scans and digital images are acquired for the same canopy from multiple angles over a short time frame. Finally, as TLS data have recently served as the basis for the development and evaluation of advanced 3-D geometric-optical ray tracing models of canopy illumination (e.g. Bittner et al., 2012), these models could be implemented to calculate foliar illumination conditions, offering further interpretation power for understanding leaf-level GLRI variation as it relates to foliar photoprotective physiology.

Because current methods of TLS data acquisition require an instrument operator to be present during scan acquisition, new opportunities to monitor variation in canopy 3-D photosynthetic function over time would be afforded by the development of automated LiDAR scanning technologies. Indeed, new techniques enabling reliable, repeatable LiDAR scans under a wide range of environmental conditions could greatly improve the operational capacity and interpretation of GLRI canopy physiology data. Recently, Eitel et al. (2013) developed a lightweight, low-cost, autonomously operating near-infrared laser scanner to quantify and monitor ecosystem structural dynamics; this concept of automated scanning is readily applicable to a green laser in order to acquire GLRI.

By accounting for leaf angle, viewing angle and the need for continuous data, we feel that the GLRI results shown here hold promise for future inferences of canopy photosynthetic performance in three dimensions. In addition to the impending advances afforded by the utilization of TLS data in three dimensions, our work demonstrates that spatial variation in foliar photoprotection using GLRI can be further explored in two dimensions at extremely high spatial resolution (Figs 3, 4). Seminal work towards the mapping of the efficiency of photosynthetic light utilization has already been addressed using a laser-induced fluorescence transient (LIFT) method for terrestrial vegetation (e.g. Ananyev et al., 2005; Kolber et al., 2005). In addition, 2-D measurements coupled with 3-D LiDAR measurements (Omasa et al., 2007; Hilker et al., 2008; Konishi et al., 2009) are an important step towards the better quantification of canopy-level photosynthetic parameters. In sum, although work remains to implement the findings of this paper for the study of 3-D canopy photosynthetic performance in situ, our results underscore the applicability of LiDAR technology as a viable and critical tool for the investigation of previously unobservable spatiotemporal patterns and relationships between plant structure and function.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Many thanks for the insightful conversations with and initial support from Drs John Marshall, Asaph Cousins, Steve Garrity and Thomas Hilker. Additional thanks to three anonymous reviewers who provided helpful comments to strengthen previous versions of the manuscript. This project was made possible through funding provided by US Department of Agriculture-National Institute of Food and Agriculture (USDA-NIFA) award 2011-637003-3034 and Grua/O'Connell travel award from Bowdoin College to S.A.E. Special thanks to Jaret Reblin for assistance with HPLC analyses, and to Dr Jerry Fairley for laboratory logistical support. Institutional support was provided by the University of Idaho and Bowdoin College.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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