Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner

Remote Sensing of Environment 114 (2010) 2229–2237

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Remote Sensing of Environment j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e

Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner Jan U.H. Eitel a,⁎, Lee A. Vierling a, Dan S. Long b a b

Geospatial Laboratory for Environmental Dynamics, University of Idaho, Moscow, ID 83844-1135, United States Columbia Plateau Conservation Research Center, USDA-Agricultural Research Service, Pendleton, OR 97801, United States

a r t i c l e

i n f o

Article history: Received 3 March 2010 Received in revised form 23 April 2010 Accepted 25 April 2010 Keywords: Leaf area Leaf angle Edge effect Laser return intensity Canopy architecture Leaf chlorophyll estimation

a b s t r a c t Plant structure and chlorophyll content strongly affect rates of photosynthesis. Rapid, objective, and repeatable methods are needed to measure these vegetative parameters to advance our understanding and modeling of plant ecophysiological processes. Terrestrial laser scanners (TLS) can be used to measure structural and potentially chemical properties of objects by quantifying the x,y,z coordinates and intensity of laser light, respectively, returned from an object's surface. The objective of this study was to determine the potential usefulness of TLS with a green (532 nm) laser to simultaneously measure the spatial distribution of chlorophyll a and b content (Chlab), leaf area (LA), and leaf angle (LAN). The TLS measurements were obtained from saplings of two tree species (Quercus macrocarpa and Acer saccharum) and from an angleadjustable cardboard surface. The green laser return intensity value was strongly correlated with wetchemically determined Chlab (r2 = 0.77). Strong agreement was shown between measured and TLS-derived LA (r2 = 0.95, intercept = − 1.43, slope = 0.97). The TLS derived LANs of both species followed a plagiophile LAN distribution, and the measured angles of the cardboard surface allowed us to quantify that these LAN values were strongly correlated with TLS derived angles (r2 = 1.0, intercept and slope = 0.98). Our results show that terrestrial laser scanners are feasible for simultaneous measurement of LA, LAN, and Chlab in simple canopies of small broadleaved plants. Further research is needed in more complex and larger canopies. © 2010 Elsevier Inc. All rights reserved.

1. Introduction Photosynthetically active radiation (PAR) (400–700 nm) is a main driver of photosynthesis. The distribution of PAR throughout plant canopies and the ability of plants to capture PAR is strongly affected by leaf area (LA) and leaf angle (LAN) (deWit, 1965; Funk & Lerdau 2004). The potential of leaves to absorb PAR (400–700 nm) is driven by the content of light absorbing chlorophyll a and b (Chlab) pigments within a leaf. Information on LA, LAN, and Chlab are thus of key importance for modeling photosynthetic carbon dioxide (CO2) assimilation of plants. A variety of methods have been developed to directly measure LA, LAN, and Chlab. LAN has been directly measured using protractors or 3D sonic digitizers (Pearcy & Yang, 1996; Kaitaniemi et al., 1999; Ford et al., 2008). The LA of destructively sampled leaves has been measured directly using leaf area meters and computer scanners (e.g., Guenther et al., 1996). Wet-chemical laboratory methods have

Abbreviations: Chlab, Chlorophyll a and b content; LA, leaf area; LAN, leaf angle; PAR, photosynthetically active radiation; CO2, carbon dioxide; TLS, terrestrial laser scanner. ⁎ Corresponding author. Current address: Geospatial Laboratory for Environmental Dynamics, University of Idaho, Moscow, ID 83844-1135, United States. E-mail address: [email protected] (J.U.H. Eitel). 0034-4257/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2010.04.025

been developed to directly measure Chlab of destructively sampled leaves (e.g. Lichtenthaler & Wellburn, 1983). Because direct methods are often laborious, time consuming, and destructive, a suite of more efficient and non-destructive techniques has been developed (Norman and Campbell, 1989; Jonckheere et al., 2004; Weiss et al., 2004). Mean LA and LAN have been estimated as a function of light transmission through the canopy by means of cameras with fish eye lenses and other optical devices (e.g. LAI-2000 or DEMON) (Norman & Campbell, 1989). In addition, mathematical models have been used that describe different general LAN distributions (Nichiporovich, 1961; deWit, 1965). Reflectance of visible light (400–700 nm) can be measured using spectrometers to derive the Chlab of leaves (e.g. Daughtry et al., 2000; Gitelson & Merzlyak, 1994). Interested readers may wish to consult Norman and Campbell (1989) for details on direct and indirect LA and LAN measurement techniques, and Palta (1990) on Chlab measurement techniques. Unfortunately, the indirect methods are unable to simultaneously measure LA, LAN, and Chlab and their spatial distribution, which limits their usefulness in research of the exchange of CO2 between vegetation and the atmosphere (Arkebauer et al., 2009). Recent work with time-of-flight terrestrial laser scanners (TLS) suggests that TLS data may provide information about the spatial distribution of LA, LAN, and Chlab (Bredemeier and Schmidhalter,

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2001; Hopkinson et al., 2004; Watt and Donoghue, 2005; Clawges et al., 2007; Clawges et al., 2007; Thoren and Schmidhalter, 2009). A TLS can survey the x,y,z location of object surfaces relative to the sensor location at a rate of up to several thousand survey points per second. To determine the relative x,y,z location, the TLS measures the inclined distance as well as the horizontal (azimuth) and vertical (zenith) angles between itself and each given survey point. Distance measurements are obtained by measuring the time of flight (t) of a laser pulse incident on a survey point to the sensor (distance = (ct)/2, where c is the speed of light and t is round-trip elapsed time of light propagation). Based on the distance and two electronically measured angles (azimuth and zenith), the x, y, and z location can be calculated for each point using trigonometric principles. The x,y,z values surveyed by a TLS have been used to directly determine forest metrics such as diameter at breast height (dbh), tree height, gap fraction distribution, needle leaf clumping factors, and leaf area (Hopkinson et al., 2004; Watt & Donoghue, 2005; Clawges et al., 2007; Danson et al., 2007). Historically, passive remote sensing has been used to assess plant Chlab, but this method can be negatively influenced by diurnal variations in solar illumination and atmospheric conditions (Eitel et al., 2007). The TLS measured intensity of the reflected laser signal, termed laser return intensity, is independent of solar illumination and less affected by atmospheric conditions (Höfle & Pfeifer, 2007; Morsdorf et al., 2009). Though relatively little is known about the usefulness of laser return intensity, there is some recent evidence that it might provide information about Chlab (Bredemeier and Schmidhalter, 2001; Morsdorf et al., 2009; Thoren & Schmidhalter, 2009, Côté et al., 2009). For example, Côté et al. (2009) used a laser return intensity threshold value to separate laser return intensity values associated with photosynthetically active biomass from those associated with woody biomass. Bredemeier and Schmidhalter (2001) used a red (630 nm) laser employing instrument to induce and measure chlorophyll fluorescence at 680 nm and 740 nm. They showed that the measured ratio of chlorophyll fluorescence intensity at 680 nm and 740 nm was negatively correlated with total Chlab (r2 = 0.62). Recently, Thoren and Schmidhalter (2009) deployed the same laser technology on a tractor to provide on-the-go information about site-specific nitrogen (N) fertilizer needs that are known to be related to the Chlab of the crop (Eitel et al., 2008). The use of laser return intensity for Chlab prediction is complicated by the inverse distance square law, whereby intensity of light returned to the laser decreases with increasing distance from the target (Häckel, 1999; Höfle and Pfeifer, 2007). In addition, TLS-derived Chlab predictions are complicated by varying leaf angle that affects incidence angle of the laser and thus the laser light returned back to the laser. The amount of a constant beam of electromagnetic radiation striking a given unit of surface area reaches its maximum if the surface area is perpendicular to the light beam (cosine = 0) and decreases as the surface angle deviates from this perpendicular orientation (Jones, 1992; Häckel, 1999; Höfle & Pfeifer, 2007). Different correction techniques have been proposed to account for the laser incidence angle and inverse distance square law of propagated light (Coren & Sterzai, 2006; Höfle & Pfeifer, 2007; Kaasalainen et al., 2009). However, little is known about how to correct for another complicating factor known as the edge effect (Van Genechten et al., 2008). The edge effect occurs when the laser beam is split on the edge of an object so that the laser return is either a mix of signals reflected from at least two objects such as the edge of a leaf and soil background or attenuated because only a fraction of the laser beam is returned back to the sensor and the other is too weak to trigger a signal (Fig. 1). We are not aware of reports in the literature in which TLS was used to simultaneously measure the spatial distribution of LA, LAN, and foliar Chlab. Given that these parameters are of key importance to photosynthesis, the objectives of this study were to: (1) develop a method for deriving LA, LAN, and Chlab from TLS data; (2) examine the

influence of the edge effect on TLS derived LA and Chlab predictions; and (3) assess the effect of the laser light angle of incidence and the inverse distance-squared dependency of laser light on TLS derived Chlab estimates. 2. Methods 2.1. Plant material Twelve bur oak (Quercus macrocarpa) and twelve sugar maple (Acer saccharum) saplings were grown in individual 3.79 l pots. Each sapling had only one leaf layer which contained between 3 and 8 individual leaves. The average leaf size was 87.66 cm2. The growth substrate was a mixture of equal parts of vermiculite and forestry grade peat moss (Sun Gro Horticulture Distribution Inc., Bellevue, WA, USA). Each sapling was initially fertilized with 12.5 kg m− 3 of controlled release fertilizer. Prior to foliar measurements, the potential of TLS to measure (1) leaf angle and (2) the effect of leaf angle on LA estimates was simulated in the laboratory. An apparatus was built to change the angle of a leaf surface in 45 increments from − 30 to 30° (negative values indicate that the leaf surface faced away from the sensor and positive values indicate that leaf surfaces faced towards the sensor). The leaf surface of a known one-sided area of 97.56 cm2 was simulated by a flat piece of matte white cardboard which showed similar laser return intensity values as green leaves. 2.2. Characterization of plant foliar properties The angle of the simulated leaf surface was measured with a digital protractor (SmartTool Technologies, Oklahoma City, OK) having an accuracy of ±0.1°. The LAN of the saplings was not manually measured since it would have been exceedingly difficult to ensure that the manual and TLS derived LAN measurements of non-flat leaf surfaces were acquired at the same leaf location. In addition, manual leaf angle measurements of leaf surfaces that encompass many different angles are laden with potential error which voids a fair comparison between manual and TLS derived LAN (Norman & Campbell 1989). However, LA measurements were conducted on the actual plant leaves. To determine the LA of each sapling, all leaves were removed from each sapling following the terrestrial laser scans and scanned with a flatbed scanner (Hewlett Packard Scanjet G3110) to obtain a digital image of the leaves. The maximum likelihood classifier in the software package, “Environment for Visualizing Images” (ENVI, Version 4.5, ITT Corporation, New York, NY) was used to identify leaf pixels within the digital image. The number of pixels classified as leaf were then determined with the ENVI image summary statistics tool and multiplied by the known pixel surface area to obtain the one-sided LA for each sapling. To determine the Chlab associated with leaves of each sapling, representative leaf disks with a total one sided leaf area of 7 cm2 were removed from the previously scanned leaves and cut into fine pieces (b0.25 mm2). The cut leaves were immediately placed into 10 ml of aqueous 80% acetone and stored in a dark room for 24 h. Chlorophyll extracts were then filtered and absorbancy was measured at 644 nm and 663 nm with a Thermo scientific GENESYS 20™ visible spectrophotometer (Thermo Fisher Scientific Inc., MA, USA). The Chlab of the chlorophyll extract solution was calculated with coefficients determined by Lichtenthaler and Wellburn (1983) in units of µg cm− 2 of projected (one-sided) LA. 2.3. Terrestrial laser scanner The time-of-flight, terrestrial laser scanner used in this study was the Leica ScanStation 2 (Leica Geosystems Inc., Heerbrugg, Switzerland). The Leica ScanStation2 employs a pulsed green

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Fig. 1. Illustration of edge effect and scan resolution.

(532 nm) laser. Green light is advantageous for remote sensing of chlorophyll concentration because green light reflectance is known to be sensitive to a wide range of Chlab (Gitelson and Merzlyak, 1994; Daughtry et al., 2000). The TLS instrument has a beam divergence of 0.15 mrad, a scan rate of 50,000 points s− 1, a maximum sample density of b1 mm, and a maximum range of 134 m at 18% albedo (http://hds.leica-geosystems.com). Distance accuracy is quoted as 4 mm and position accuracy as 6 mm. The instrument provides a unitless measure of the amount of laser light reflected back to the sensor, which in the following will be termed laser return intensity. The laser return intensity range is −2047 to +2048. 2.4. TLS approach for characterizing canopy properties The scanner head was mounted on a tripod above the floor of the laboratory such that the average distance between the scanner and the sapling was 2 m. To minimize the effect of instrument temperature on laser return intensity, the instrument was turned on for 30 min prior to scanning to ensure that electronic components reached a uniform operating temperature. In addition, room temperature was held at 24 °C during scanning. Each sapling was scanned in its entirety from a single scan position, from which all leaves were visible to the scanner. The laser point spacing was 1 mm at 3 m (distance between scanner and surveyed object). The scan duration was about 1 minute per plant. All measurements (x,y,z, laser return intensity) were automatically logged into a field computer. LiDAR point clouds from foliar tissue were visually separated from non-foliar tissue (e.g. background, woody tissue etc.) in the software package Cyclone (Version 7.0, Leica Geosystems Inc., Heerbrugg, Switzerland) and exported as a tab delimited text file containing the x,y,z coordinates and laser return intensity value for each laser point returned from the leaf surface. The laser return intensity values measured by the TLS were all negative and adjusted to positive values by adding an arbitrary value of 2000. The exported LiDAR points were

gridded onto a regular 4 × 4 mm grid by using a program written in the Interactive Data Language (IDL) software package (Version 4.5, ITT Corp., New York, NY). The z value assigned to each grid cell was the maximum z value contained within a search radius of 2.83 mm (search radius = sqrt(2)/2 ⁎ grid resolution) from the center of the grid cell. No interpolation was necessary since each grid cell contained at least one LiDAR point. 2.4.1. Chlorophyll content For each sapling, the average laser return intensity of the previously exported LiDAR point cloud was calculated and correlated with Chlab determined in the laboratory. To investigate how much the inverse distance square law of light affected TLS derived Chlab predictions, we recorded the laser return intensity values of a black (3% reflectance) and white (99% reflectance, Spectralon®, Labsphere Inc.) reflectance target and associated distance values that were increased in 0.1 m intervals from 1.1 to 2.6 m. The center of the reflectance target was positioned perpendicular to the laser beam to minimize angle of incidence effects on laser return intensity (Kaasalainen et al., 2009). To study the effect of the laser beam angle of incidence and reflection on TLS derived Chlab predictions, the apparatus described in Section 2.2 was used to change the leaf angle in 2° intervals between −30 to 30° and the laser return intensity of the leaf surface at each given angle was recorded. The edge returns on TLS derived Chlab predictions was examined by removing edge returns based on a threshold value of 827 (Fig. 2). The threshold value was determined as the mean of 1200 randomly sampled edge return laser intensity values (50 randomly sampled edge returns from each of the 24 saplings). The edge returns were visually indentified along the leaf edges based on their red color that indicated low laser return intensity (Fig. 2). Because the scanning frequency (points recorded per second) of the TLS limits the number of returns cm− 2 that can be acquired from a moving platform, we incrementally reduced the number of returns

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2.5. Statistical analysis The observed LA and LAN (dependent variables) were related to the predicted LA and LAN (independent variables), respectively, in the open-source software package R 2.8.1 (R Development Core Team, 2009). For the resultant model, the root mean square error (RMSE), the coefficient of simple determination (r2), slope and intercept were calculated. A RMSE value of 0.0 and an r2 of 1.0 indicated high precision whereas a slope of 1.0 and intercept of 0.0 indicated high accuracy. Simple linear regression was performed to examine the relationship between measured Chlab and average laser return intensity of all categories of returns cm− 2. Each regression model for a given category was refit 100 times by taking repeated random samples and so a standard deviation of model parameters could be calculated for each category. 3. Results and discussion 3.1. Leaf angle and inverse distance square law effects on laser return intensity

Fig. 2. Terrestrial laser scan point cloud of a sugar maple leaf. Differences in color illustrate differences in laser return intensity (blue = high laser return intensity, red = low laser return intensity).

cm− 2 to simulate a mobile acquisition. Five categories of returns were considered: 100, 50, 10, 1, 0.5, and 0.1 returns cm− 2. Since on average there were more returns cm− 2 (e.g. 120) than in each given category, it was possible to take repeated random samples from a unit area (e.g. 100 times 1 sample cm− 2 out of the 120 returns cm− 2). 2.4.2. Leaf angle After gridding the leaf surface of the saplings and simulated leaf, the IDL program was used to calculate the LAN for each grid cell location x (LANx) using the following equation (Wilson and Gallant, 2000): s ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi z −z 2 z −z 2  x2 x6 + x8 x4 LANx = 2d 2d

ð2Þ

Laser return intensity increased with increasing leaf angle (Fig. 3), suggesting that differences in angle of incidence influenced laser intensity values and thus confounded Chlab estimates. Though not examined in this study, leaf roughness may have also had a strong effect on laser return intensity. For example, smooth and shiny leaf surfaces (directional reflectors) would likely have exhibited different laser return intensity values than rough and matte leaf surfaces (diffuse reflectors) even though they have the same Chlab. Laser return intensity from a black and white reflectance target showed no detectable trend to changes in distance over a range from 1.1 to 2.6 m (Fig. 4). Therefore, the inverse distance square law of light apparently had little effect on TLS derived Chlab predictions. Nevertheless, the inverse distance square law will likely apply to wider distance ranges than that used in this study (e.g., Höfle and Pfeifer, 2007). Some of the variability in laser return intensity at a given distance might be caused by differences in the incidence angle: the center of the 12.5 by 12.5 cm reflectance targets was exactly perpendicular to the laser beam, meaning that points away from the center of the reflectance targets were not entirely perpendicular to the laser beam. 3.2. TLS derived chlorophyll content

where zx6 and zx2 are the z values to the left (zx6) and right (zx2) of the center pixel zx, zx8 and zx4 are the z values above (zx8) and below (zx4) of the center pixel zx, and d is the pixel size (4 mm in this study). The LAN distribution was determined by calculating the frequency distribution of leaf angles between 0.00–10.00°, 10.01–20.00°, 20.01–30.00°, 30.01–40.00°, 40.01–50.00°, 50.01–60.00°, 60.01– 70.00°, 70.01–80.00°, and 80.01–90.00°.

The color of the leaves ranged from light to dark green, indicating a wide range of Chlab values. This appearance was confirmed by laboratory results showing Chlab to range from 10.40 to 40.80 µg cm− 2 with a mean and standard deviation of 26.29 ± 9.81 µg cm− 2. The range of Chlab

2.4.3. Leaf area Accounting for the LAN at each grid cell location x (LANx), the onesided LA was calculated as follows:

n

LA = ∑

zx = 1



 d d cosLAN x

ð3Þ

where the leaf area associated with the xth grid cell location is a function of the grid cell size (d) and LAN associated with the xth grid cell location. The effect of edge laser returns on TLS derived LA was examined by removing edge returns based on a threshold value of 827 (see Section 2.4.1).

Fig. 3. Laser return intensity as a function of leaf angle (negative values are associated with the leaf surface facing away from the terrestrial laser scanner and positive values are associated with the leaf surface facing towards the terrestrial laser scanner).

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Fig. 4. Laser return intensity values returned from a black (3% reflectance) and white (99% reflectance, Spectralon®, Labsphere Inc.) reflectance target.

values was comparable to those shown in other studies that examined the Chlab of broadleaf species (Richardson et al., 2002). Laser return intensity was correlated with Chlab (r2 = 0.75, Fig. 5). Removing edge returns had little effect on the laser return intensityChlab relationship. To correct for the incidence angle of the laser beam, we rasterized the intensity values and multiplied each laser return intensity value x with the inverse of the cosine of its corresponding slope (cos slopex)− 1. However, the cosine correction did not improve the Chlab to laser return intensity relationship (data not shown). During mobile data acquisition (e.g., by means of using a TLS mounted on a tractor), the number of returns cm− 2 from a stationary target will decrease with the speed of the moving vehicle. To simulate the effect of movement, we incrementally reduced the amount of returns cm− 2 of leaf surface (Table 1). The strength of the relationship between Chlab and laser return intensity was relatively constant from 100 to 0.5 returns cm− 2 for laser return intensity values with or without edge effects. For 0.1 returns cm− 2, the Chlab to laser return intensity relationship weakened, but the change was smaller if data were adjusted for edge effects. This phenomenon may be attributable to the ratio of edge to non-edge returns that were found to stay constant (5.58 ± 1.55%) if returns cm− 2 were larger or equal to 0.5, but increased (6.70 ± 4.47%) if returns cm− 2 dropped below 0.5. As a

result, the variability of the laser return intensity values increased and likely weakened the relationship between Chlab and laser return intensity. These findings suggest that removal of edge effects is important for remote sensing of Chlab using TLS, particularly if remotely sensed measurements are taken from a moving platform when b0.5 returns cm− 2 are likely. With the small field of view (12.56 mm2) and fast (e.g. Leica ScanStation2 up to 50 000 points s− 1) sampling rate, TLS has the ability to resolve small targets and thus separate leaf tissue from soil, woody tissue, and other background features based on a laser return intensity threshold values and potentially height information (e.g., everything below 0.20 m is background) (Van Genechten et al., 2008). For example, Morsdorf et al. (2009) showed in a modeling study that canopy returns could be separated from soil returns by using a simple normalized difference vegetation index (NDVI) threshold calculated from red (670 nm) and near-infrared (780 nm) laser return intensity values. This will be a key advantage over conventional remote sensing information of coarser resolution that generally integrates a mixture of leaf and background reflectance, and thus is less useful for remote sensing of Chlab, especially when vegetation is sparse (Eitel et al., 2009; Thoren and Schmidhalter, 2009) or possesses a large fraction of woody material (Verrelst et al., 2010).

Fig. 5. Relationship between chlorophyll content (Chlab) and mean laser return intensity values that are A) raw B) edge effect adjusted.

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Table 1 Parameters associated with linear models that relate chlorophyll content (Chlab) to mean laser return intensity. Mean laser return intensity not adjusted and adjusted for edge effects has been calculated for different categories of laser returns cm− 2 (100, 50, 10, 5, 1, 0.5, 0.1). Since on average there were more returns cm− 2 (e.g. 120) than in each given category, it was possible to take repeated random samples from a unit area (e.g. 100 times 1 sample cm− 2 out of the 120 hits cm− 2). By refitting the model in each category 100 times, a standard deviation of model parameters could be calculated for each category. Returns cm− 2

Slope

r2

RMSE

Not edge effect adjusted 100 156.38 ± 0.27 50 156.27 ± 0.48 10 155.93 ± 1.37 5 156.04 ± 1.95 1 153.99 ± 5.14 0.5 151.46 ± 4.91 0.1 136.24 ± 11.44

− 0.11 ± 0.00 − 0.11 ± 0.00 − 0.11 ± 0.00 − 0.11 ± 0.00 − 0.11 ± 0.00 − 0.11 ± 0.01 − 0.09 ± 0.01

0.75 ± 0.00 0.75 ± 0.00 0.75 ± 0.00 0.75 ± 0.01 0.73 ± 0.02 0.72 ± 0.04 0.62 ± 0.10

4.88 ± 0.01 4.90 ± 0.03 4.92 ± 0.08 4.92 ± 0.10 5.04 ± 0.21 5.14 ± 0.34 6.00 ± 0.75

Edge effect adjusted 100 159.95 ± 0.21 50 159.94 ± 0.40 10 159.78 ± 0.99 5 159.74 ± 1.52 1 158.78 ± 3.81 0.5 157.96 ± 4.40 0.1 148.29 ± 8.60

− 0.11 ± 0.00 − 0.11 ± 0.00 − 0.11 ± 0.00 − 0.11 ± 0.00 − 0.11 ± 0.00 − 0.11 ± 0.00 − 0.10 ± 0.01

0.77 ± 0.00 0.77 ± 0.00 0.77 ± 0.01 0.77 ± 0.01 0.76 ± 0.02 0.75 ± 0.03 0.70 ± 0.06

4.70 ± 0.01 4.70 ± 0.02 4.72±0.05 4.72 ± 0.08 4.80 ± 0.16 4.85 ± 0.25 5.39 ± 0.51

Intercept

The good relationship demonstrated between Chlab and laser return intensity suggests that TLS is potentially useful for determining the distribution of Chlab and other chlorophyll-related attributes such as nitrogen within plant canopies. This information combined with TLS-derived canopy structural measures could help to further an understanding of how canopy structure influences resource distribution within plant canopies (Funk and Lerdau, 2004). Moreover, with its ability to scan objects that are N100 m away, TLS could be used to estimate the Chlab of tree top foliage that otherwise is difficult to ascertain using conventional instrumentation where instrument-leaf contact is required. This feature also has important implications for validating remotely sensed Chlab maps of forest ecosystems.

Fig. 6. Relationship between observed and predicted (TLS derived) angle of a flat cardboard piece with reflectance properties similar to that of a healthy green leaf.

the object does not comprise the entire field of view of the laser (Fig. 1). This will likely cause overestimation of the area of the cardboard which would likely increase with a decrease in area: perimeter ratio. Removing the edge effect reduced the average overestimation of the cardboard area from 14.83 to 1.15 for angle adjusted area estimates. On average, both adjusted and non-adjusted area estimates deviated stronger from the observed area at angles facing away from the sensor (negative leave angles). This phenomenon may have resulted from the angle of incidence of the laser beam

3.3. TLS derived leaf angle distribution The model describing the predicted to observed angle of cardboard containing leaflike reflectance properties showed an r2 value of 1.0 and an intercept and slope of 0.98 (Fig. 6), thus suggesting that TLS measurements are well suited for predicting the LAN of plant canopies. The LAN distribution of the bur oak and sugar maple saplings closely followed a plagiophile LAN distribution with the majority of leaf angles ranging from 20 to 30° (Fig. 7). TLS derived information on LAN distribution could be of value to crop breeders since leaf angle distribution affects photosynthesis and thus might explain yield differences between species or hybrids (Ford et al., 2008). The availability of LAN information also provides insights into radiative transfer throughout plant canopies which can assist interpretation of canopy reflectance measured by optical remote sensing devices. 3.4. TLS derived leaf area Our cardboard area results suggested that it is necessary to adjust for leaf angle when estimating LA. The TLS-derived (one-sided) area of the cardboard constantly increased from a leaf angle of −30 up to 0° and constantly decreased thereafter resulting in a standard deviation of 6.24 (Fig. 8). In contrast, the area estimates of the cardboard stayed comparably constant if it was adjusted for angle as indicated by the lower standard deviation (4.45). However, predicted area of the cardboard tended to be overestimated likely because of the laser beam spot size, which can still record a return on an objects edge even when

Fig. 7. Observed leaf angle distribution of Quercus macrocarpa (bur oak) and Acer saccharum (sugar maple) saplings as well as general leaf angle distributions.

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Fig. 8. Terrestrial laser scanner (TLS) derived leaf area (LA) estimates of a flat cardboard piece at different angles. TLS derived LA estimates A) have not been angle and edge effect adjusted B) have been angle but not edge effect adjusted C) have not been angle but edge effect adjusted and D) have been angle and edge effect adjusted.

onto the cardboard surface, which was highest at − 30°. This phenomenon needs further examination. LA of all saplings ranged from 123 to 421 cm2 with a mean and standard deviation of 270 ± 78 cm2. TLS derived LA adjusted for leaf

angle and observed LA were highly correlated (r 2 = 0.96, RMSE = 15.41 cm2) (Fig. 9). However, observed LA was overestimated if TLS measurements were not adjusted for edge effects that negatively affected the accuracy of TLS derived LA measurements

Fig. 9. Relationship between observed and predicted (terrestrial laser scanner derived) leaf area (LA). Terrestrial laser scanner derived LA estimates A) have not been edge effect adjusted and B) have been edge effect adjusted.

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(intercept = − 7.8; slope = 0.88). Adjusting for edge effects improved the accuracy of TLS derived LA estimates (intercept = − 1.43; slope = 0.97), further confirming our previous results. 3.5. Limitations Our results showed that laser return intensity values are affected by the incidence angle. These phenomena can consequently confound the laser return intensity to Chlab relationship. The Chlab to laser return intensity relationship could be improved by using a multiwavelength TLS instrument that measures the return intensity of at least two laser wavelengths (Morsdorf et al., 2009): a reference laser wavelength that is independent of Chl ab and an index laser wavelength that is sensitive to Chlab. Based on previous studies (e.g. Eitel et al. 2008, Daughtry et al., 2000; Gitelson & Merzlyak, 1994) and available laser wavelengths, we propose use of a green index laser wavelength and a red reference laser wavelength. Calculating a spectral index from the ratio of the green and red laser return intensities would likely reduce variations in laser return intensity from surfaces with similar reflectance properties that are caused by the incidence angle. TLS derived LA measurements in this study are based on the following assumptions: (i) all leaves are visible to the scanner, (ii) scan resolution is less than or equal to the laser beam field of view, and (iii) leaves do not move during the scan. The first assumption will be violated in plant canopies where shadow effects occur. Shadow effects occur when the laser light of a first return TLS is reflected from the first leaf the laser encounters in its line of sight and doesn't reach other leaves behind the first leaf (Van der Zande et al., 2008). To get information about the shadowed leaves it would be necessary to scan them from different locations so that they are in the line of sight of the laser (Van der Zande et al., 2008; Côté et al., 2009). The violation of the second assumption would result in underestimation of LA since only a fraction of the leaf would be scanned (Fig. 1). The final assumption would be violated if TLS measurements are taken under windy conditions. Wind causes plant constituents to move that confounds TLS returns (Côté et al., 2009). Separating leaves from woody tissue and soil background requires manual post-processing procedures, but could be automated in the future. For example, a threshold laser return intensity value similar to one used in this study to exclude edge returns (see Section 2.4.1) could be used to separate leaf from background laser returns. The results of the present study are based on broadleaf saplings with small, simple canopies and should not be implicitly extrapolated to larger more complex canopies. For example, it will likely be necessary to correct for the inverse distance square law in larger canopies by employing standard correction techniques (e.g., Höfle & Pfeifer, 2007). Also, methods in this study that rely on TLS measures of all leaves within a canopy must be modified if used in canopies where shadow effects occur even after the canopy has been scanned from different scan locations. Under such circumstances, scaling techniques are needed that will allow one to scale TLS derived LA, LAN, and Chlab of a non-shadowed subsample of leaves to the entire canopy (Van der Zande et al., 2008). Additional research is needed to develop these scaling techniques and test them across different species and canopy densities. 4. Summary and conclusions The findings of this study show that TLS allows for simultaneous measurements of LA, LAN, and Chlab. Each laser return and laserderived LA, -LAN, and Chlab measurement has an associated spatial coordinate (x,y,z) (Fig. 2). Consequently, TLS is not only able to simultaneously measure Chlab, LA and LAN, but also their spatial distribution. For example, TLS could provide information about the distribution of Chlab and LA with plant height or the azimuth direction

of a given unit leaf area. The ability of TLS to measure the spatial distribution of LA, LAN, and Chlab sets it apart from traditional methods that provide only a single measurement value without information about their spatial distribution. For example, traditional methods may provide a single LA and average leaf LAN value, but do not provide information about the angular distribution of LA. The results of the present study are based on broadleaf saplings with small, simple canopies. Issues such as the shadowing of leaf layers and the inverse distance effect of light in complex canopies might complicate the estimation of LA, LAN, and Chlab from TLS data. Further research is needed that examines the potential of TLS to simultaneously measure LA, LAN, and Chlab in larger, complex canopies. Acknowledgements We thank Anthony S. Davis for growing and providing the saplings used in this study. This work was supported by a specific cooperative agreement between the University of Idaho and the USDA-ARS, and the University of Idaho Harold Heady professorship. Funding to acquire the TLS was provided by the University of Idaho, Idaho NSF EPSCoR, and by the National Science Foundation under ward number EPS-0814387. Use of trade names does not constitute an official endorsement by the University of Idaho or the USDA. References Arkebauer, T. J., Walter-Shea, E. A., Mesarch, M. A., Suyker, A. E., & Verma, S. B. (2009). Scaling up of CO2 fluxes from leaf to canopy in maize-based agroecosytems. Agricultural and Forest Meteorology, 149, 2110−2119. Bredemeier, C., & Schmidhalter, U. (2001). Laser-induced chlorophyll fluorescence to determine the nitrogen status of plants. Printed in the Netherlands: Kluwer Academic Publishers. Clawges, R., Vierling, L. A., & Calhoon, M. (2007). Use of ground-based scanning lidar for estimation of biophysical properties of western larch (Larix occidentalis). International Journal of Remote Sensing, 28, 4331−4344. Coren, F., & Sterzai, P. (2006). Radiometric correction in laser scanning. International Journal of Remote Sensing, 27, 3097−3104. Côté, J. -F., Wildlowski, J. -L., Fournier, R. A., & Verstraete, M. M. (2009). The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial lidar. Remote Sensing of Environment, 113, 1067−1081. Danson, F. M., Hetherington, D., Morsdorf, F., Koetz, B., & Allgoewer, B. (2007). Forest canopy gap fraction from terrestrial laser scanning. IEEE Geoscience and Remote Sensing Letters, 4, 157−160. Daughtry, C. S., Walthall, C. L., Kim, M. S., Brown de Colstoun, E., & McMurtrey, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229−239. deWit, C.T., 1965. Photosynthesis of Leaf Canopies. Agr.Res.Rep. No. 66, Centre Agr. Publ. Doc. and Wageningen Publ. Doc., Wageningen, The Netherlands. Eitel, J. U. H., Long, D. S., Gessler, P. E., & Hunt, E. R. (2008). Combined spectral index to improve ground-based estimates of nitrogen status in dryland wheat. Agronomy Journal, 100, 1694−1702. Eitel, J. U. H., Long, D. S., Gessler, P. E., Hunt, E. R., & Brown, D. J. (2009). Sensitivity of ground-based remote sensing estimates of wheat chlorophyll content to variation in soil reflectance. Soil Science Society of America Journal, 73, 1715−1723. Eitel, J. U. H., Long, D. S., Gessler, P. E., & Smith, A. M. S. (2007). Using in-situ measurements to evaluate the new RapidEye satellite series for prediction of wheat nitrogen status. International Journal of Remote Sensing, 28, 4183−4190. Ford, E. D., Cocke, A., Horton, L., Fellner, M., & Van Volkenburgh, E. (2008). Estimation, variation and importance of leaf curvature in Zea mays hybrids. Agricultural and Forest Meteorology, 148, 1598−1610. Funk, J. L., & Lerdau, M. T. (2004). Photosynthesis in forest canopies. In M. D. Lowman & H. Bruce Rinker (Eds.), Forest Canopies. Burlington: Elsevier Academic Press. Gitelson, A., & Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology, 22, 247−252. Guenther, A. B., Greenberg, J. P., Harley, P. C., Helmig, D., Klinger, L. F., & Vierling, L. A. (1996). Leaf, branch, stand and landscape scale measurements of volatile organic compound fluxes from US woodlands. Tree Physiology, 16, 17−24. Häckel, H. (1999). Strahlung. Meteorologie (pp. 145−146). Stuttgart: Eugen Ulmer GmbH & Co. Höfle, B., & Pfeifer, N. (2007). Correction of laser scanning intensity data: Data and model-driven approaches. ISPRS Journal of Photogrammetry & Remote Sensing, 62, 415−433. Hopkinson, C., Chasmer, L., Young-Pow, C., & Treitz, P. (2004). Assessing forest metrics with a ground-based scanning lidar. Canadian Journal of Forest Research, 34, 573−583.

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