LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status

Field Crops Research 159 (2014) 21–32

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LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status Jan U.H. Eitel a,b,∗ , Troy S. Magney a,b , Lee A. Vierling a,b , Tabitha T. Brown c,d , David R. Huggins d a

Geospatial Laboratory for Environmental Dynamics, University of Idaho, Moscow, ID 83844-1135, USA McCall Outdoor Science School, University of Idaho, McCall, ID 83638, USA c Washington State University, Pullman, WA 99163, USA d USDA-ARS, Pullman, WA 99163, USA b

a r t i c l e

i n f o

Article history: Received 22 November 2013 Received in revised form 17 January 2014 Accepted 20 January 2014 Keywords: Nitrogen nutrition index Crop biochemistry Crop biomass LiDAR Precision agriculture Fertilizer decisions

a b s t r a c t Optical remote sensing of crop nitrogen (N) status is developing into a powerful diagnostic tool that can improve N management decisions. Crop N status is a function of dry mass per unit area (W in t ha−1 ) and N concentration (%Na ), which can be used to calculate N nutrition index (NNI), where NNI is %Na /%Nc (%Na is actual N concentration and %Nc is the minimum N concentration required for maximum growth). Using optical remote sensing to estimate crop N status is particularly important during the critical early crop developmental stages when reliable data could still guide effective in-season N fertilizer management decisions (e.g., by adding topdressed fertilizer). However, because the spectral signal measured by traditional optical remote sensing devices during early crop development is often dominated by soil spectral reflectance, early season estimates of W and %Na are prone to large errors. Terrestrial LiDAR (light detection and ranging) scanning (TLS) may alleviate errors as fine scale TLS point data can be used to directly quantify physical W proxies (e.g., crop height or volume) and derive %Na from green (532 nm) TLS point return intensity. We evaluated the potential of TLS to assess W, %Na and NNI of winter wheat (Triticum aestivum L.). Green TLS measurements were obtained for two seasons during tillering and jointing. Strong (r2 > = 0.72, RMSE ≤ 0.68 t ha−1 ) relationships occurred between observed W and TLS-derived vegetation volume across all growth stages and seasons. A wider range of relationships existed between %Na and green laser return intensity (r2 = 0.10–0.75, RMSE = 0.31–0.63%). When fused to calculate a TLS based NNI, a moderately strong relationship occurred (r2 = 0.45–0.54, RMSE = 0.11 NNI). Our results demonstrate that green TLS can provide useful information for improving N management during early season wheat growth. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Improved nitrogen (N) fertilizer management has and will continue to play an important role in meeting future food demands (Cassman et al., 2002; Tilman et al., 2002; Mueller et al., 2012). In many agricultural systems, N is uniformly applied across the field despite field heterogeneity in available soil water, solar irradiation, and soil type (see Diacono et al., 2012, for review). Sitespecific nutrient management offers the potential to improve N use efficiency (NUE) by tailoring N inputs to address relevant within-field variability (Cassman, 1999). Successful precision N

∗ Corresponding author at: McCall Outdoor Science School, University of Idaho, McCall, ID 83638, USA. Tel.: +1 2085969277. E-mail address: [email protected] (J.U.H. Eitel). 0378-4290/$ – see front matter © 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.fcr.2014.01.008

management strategies can lead to improved crop yield, increased profitability, and decreased reactive N and associated adverse environmental and health impacts (Pierce and Nowak, 1999; Gastal and Lemaire, 2002). Recently, Mueller et al. (2012) estimated that 11 million tons of N fertilizer could be annually saved while maintaining yields using precision agriculture techniques. To realize some of this saving potential, site- and time-specific estimates of both crop N concentration and crop biomass could be used to monitor crop nutritional status, aid field diagnoses of adverse factors, target incrop N applications and lead to improved overall NUE. Crop biomass information is necessary for calculating the critical crop N concentration (%Nc ) defined as the minimum % Nc that allows maximum growth (Ulrich, 1952). With increasing crop biomass, %Nc decreases along a mathematical trajectory described by the critical N dilution curve (Lemaire and Gastal, 1997): %Nc = ac ∗ W −b

(1)

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where ac (in kg N ha−1 ) is a crop species specific constant representing the %Nc for one metric ton of crop dry mass per hectare (W in t ha−1 ) and the dimensionless exponent b describes the decline of %Nc with increasing W. Based on %Nc and the actual crop N concentration (%Na ), the nitrogen nutrition index (NNI) can be calculated as follows (Lemaire and Gastal, 1997): NNI =

%Na %Nc

(2)

NNI values < 1 indicate crop growth is limited by N and NNI values > 1 indicate excess N. Though the NNI could be a valuable measure for improving N management decisions, its operational use has been limited due to the lack of efficient methods that allow simultaneous measurement of both %Na and W (Lemaire et al., 2008; Lemaire and Gastal, 2009). Traditionally, destructive sampling methods have been used for determining %Na and W. However, these destructive sampling methods are laborious and time-consuming, limiting operational use for calculating NNI. Non-destructive alternatives for deriving %Na and W include chlorophyll meters and line quantum sensors, respectively (e.g., Cerovic et al., 2012; Tremblay et al., 2011; Bréda, 2003). Although these methods are comparably faster and less expensive, they are point- based and limited for fully capturing the variability of crop N status within a field. Tractor-mountable, optical remote sensors have been used to more effectively capture the within field variability of W and %Na (Raun et al., 2002; Erdle et al., 2011; Li et al., 2010). This commercially available technology allows for on-the-go fertilizer decisions based on remotely sensed crop N status. The technology makes use of spectral reflectance readings in the visible and near-infrared (NIR) parts of the electromagnetic spectrum to calculate spectral vegetation indices (SVIs) that are sensitive to variations in W, %Na , and NNI (e.g., Mistele and Schmidhalter, 2008; Vigneau et al., 2011; Erdle et al., 2011; Houlès et al., 2007). There are two important challenges, however, that currently limit the use of this technology for making fertilizer management decisions: Challenge 1: The SVIs measured by optical remote sensors are dimensionless values affected by complex interactions of factors such as the mixture of reflectance signal from plants and soil background, viewing geometry, and canopy architecture (Jacquemoud and Baret, 1990; Verhoef, 1984; Jackson and Huete, 1991). The dimensionless nature of SVIs combined with a complex interaction of factors affecting their values make SVIs difficult to interpret, particularly for non-experts. Also, the complex interaction of factors affecting the values of SVIs makes the relationships between crop properties and SVI highly empirical, limiting their spatial and temporal transferability (e.g., Zarco-Tejada et al., 2001). Challenge 2: The use of optical remote sensors is particularly limited during critical early crop developmental stages when N management assessments are often diagnostic of eventual crop performance, and when further remedial fertilizer application decisions (such as topdressing) can be made (e.g., Li et al., 2010b; Eitel et al., 2011). While these early crop developmental stages are critical for determining the trajectory of plant N status, the small size of plants during early growth creates observational challenges because the relatively large ground instantaneous field of view (typically > 50 cm2 ) of optical remote sensors is often dominated by the soil and crop residue spectral reflectance. This soil makes it difficult to isolate the vegetation signal from the integrated reflectance within the field of view of the sensor (Eitel et al., 2009; Peddle and Smith, 2005; Huete et al., 1985). Time-of-flight terrestrial LiDAR (light detection and ranging) scanning (TLS) is a remote sensing technology that may alleviate these challenges because it can measure physical, easy to interpret crop biomass proxies such as crop volume and height. TLS

is able to survey the x, y, and height (z) location of object surfaces at a rate of tens of thousands of 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 (for more detail, see Eitel et al., 2013). The x, y, z values surveyed by a TLS can then be used to derive physical measures of crop structural properties that are more easy to interpret even for non experts such as crop height or volume (Hosoi and Omasa, 2009; Ehlert et al., 2009; Hosoi and Omasa, 2012; Long and McCallum, 2013). While applications of TLS is becoming more common in ‘tallstature’ (i.e., forested) ecosystems (e.g., Lovell et al., 2003; Clawges et al., 2007; Zheng et al., 2013), relatively little research has been conducted in agricultural systems. Ehlert et al. (2009) showed strong relationships (r2 = 0.98 and RMSE = 0.12) between W in winter wheat and TLS-derived height acquired from a moving (2 m s−1 ) platform during a total of 10 sampling events conducted between the beginning of jointing to heading. Similarly, Long and McCallum (2013) measured wheat height with TLS during harvest from a combine and found strong (r2 = 0.79) relationships between observed and TLS-derived crop height. During ripening of wheat, Hosoi and Omasa (2009) showed a strong (r2 = 0.94, standard error = 26.6 g m−2 ) exponential relationship between the observed dry weight of stems and leaves and the TLS derived area of stems and leaves per unit ground area. Though these studies are promising, relatively little is known about the suitability of TLS to determine W during early crop developmental stages when further decisions regarding N management can be made (e.g., topdressed N). Also, relatively little is known about the spatial and temporal transferability of empirical relationships between measures of W and TLS derived biomass metrics (e.g., crop height, vegetation volume). In addition to measuring x, y, z coordinates of surveyed objects, TLS can also measure the intensity at which the reflected laser beam returns to the instrument. Recent findings have demonstrated that the laser return intensity can provide information about plant biochemistry (Morsdorf et al., 2010; Eitel et al., 2010, 2011; Gaulton and Danson, 2013; Magney et al., 2014). For example, during jointing (Zadoks 3.2) of wheat, Eitel et al. (2011) showed a statistically significant relationship (r2 = 0.68, p < 0.001) between %Na and the laser return intensity of a green (532 nm) scanning TLS. The return intensity of a green scanning laser can also provide biochemical information about xanthophyll pigment inter-conversion, an early indicator of plant stress (Magney et al., 2014). The theoretical basis behind the sensitivity of green laser return intensity to %Na is that the amount of green light absorbed by a plant is strongly affected by chlorophyll concentration, which in turn correlates with %Na (Evans, 1983; Karele, 2001). With increasing plant chlorophyll concentration (and therewith-correlated %Na ), more of the green laser light is absorbed, decreasing the intensity of the laser return (Eitel et al., 2010). The advantage of using TLS for estimating %Na over traditional optical sensing technology is the small field of view (<5 mm) of the laser, combined with its fast sampling rate (2–50 kHz). The small field of view, coupled with the 3-D location of points, allows pure green vegetation returns to be isolated from non-photosynthetic elements (i.e., soil and crop residues) based on simple laser return intensity thresholds (Eitel et al., 2011). The ability to isolate the vegetation spectral signal from the soil spectral signal is an important

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advantage over traditional optical sensing technology given that the crop cover is generally sparse during early crop developmental stages (e.g., tillering and jointing) when further decisions regarding N management can be made (e.g., see Eitel et al., 2011). Though the use of TLS for estimating %Na shows promise, previous work has only focused on one growing season and one particular developmental stage (jointing of wheat). Hence, to further understand the robustness of laser derived %Na estimates for agricultural decision making, this study extends the hypothesis so that it is tested beyond one growing season, and includes another key developmental stage (tillering) during which important crop diagnoses and decisions can be made. To the best of our knowledge, no work has yet explored the suitability of laser technology to determine crop N status by fusing the structural and biochemical information provided by green TLS. The overarching objective of this work was to evaluate the potential of TLS to assess the crop N status in winter wheat based on W and %Na estimates simultaneously provided by a green scanning TLS. To meet this objective, we tested our ability to use TLS to (i) simultaneously provide information about W and %Na across multiple growth stages (tillering and jointing) and growing seasons (2011 and 2012) and to (ii) estimate the NNI based on the structural and biochemical information provided by green TLS.

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(DN) values of −2047 to 2048. DN values were modified in this study to range from 0 to 4095 by adding 2047. Within each sampling plot, a 0.5 m2 sub-plot was established and each corner was marked with survey lath. The lath then allowed us later to identify and delineate a sub-plot within the scan image during pre-processing of the TLS data. To adjust for some potential drift of laser return intensity due to environmental factors such as air temperature, atmospheric pressure, and relative humidity, a 99% reflectance, white SpectralonTM (Labsphere, Inc., North Sutton, NH) reference panel was mounted on a tripod 0.6 m above the soil surface and 0.3 m behind each sub-plot (see Eitel et al., 2011). The surface of the reference panel was placed at a right angle to the soil surface and perpendicular to the row direction so it faced the scanner. Each plot and reference panel was scanned parallel to the row direction from a single position 6 m away from the plot center. The laser point spacing at 6 m was 4 mm and the scan duration was about 10 s plot−1 . The point clouds associated with the reference panel (hereafter referred to as “reference panel point cloud”) and sub-plot (hereafter referred to as “plot point cloud”) were each visually identified in the scan image displayed in the Cyclone software environment (Version 7.3, Leica Geossytems Inc., Heerbrugg, Switzerland) and exported for further processing.

2.3. Crop sampling 2. Materials and methods 2.1. Study site Soft white winter wheat (Triticum aestivum L.) was grown following chickpeas (Cicer arietinum) during two consecutive growing seasons (2011 and 2012) in 80, 9.76 m2 (2.14 m × 4.57 m) plots with 19 cm row spacing at the Washington State University Cook Agronomy Farm (CAF) near Pullman, Washington, USA (N 46.7805, W 117.0855). The research plots were located in adjacent fields on differing aspects and micro-climatic conditions between growing seasons. To promote a wide range of W and crop N levels, two sowing rates (80 and 335 plants m−2 ) and five N rates (0, 40, 80, 120, 160 kg ha−1 ) of granular urea (46% N) were deep banded (10 cm) during planting. From the total of 80 plots, 27 plots and 38 plots were randomly selected in 2011 and 2012, respectively. The soil at the site is a Palouse silt loam (fine-silty, mixed, superactive, mesic Pachic Ultic Haploxerolls) formed in loess and some ash (Soil Survey Staff, 2013). Permeability is moderate to high and available water capacity through the entire soil profile is about 28 cm. The average annual precipitation ranges between 460 and 610 mm, the average annual air temperature is 8–10 ◦ C, and the average frost-free period is 130–150 days. Terrestrial LiDAR scanner measurements and destructive crop sampling were conducted during tillering (Zakoks developmental stage 26 during 2011 and 2012) and jointing (Zadoks developmental stage 31 in 2011 and 32 in 2012).

2.2. Terrestrial LiDAR instrumentation and scanning The laser instrument used in this study was the Leica ScanStation 2 (Leica Geosystems Inc., Heerbrugg, Switzerland). This discrete return, time-of-flight, terrestrial LiDAR scanner (TLS) employs a pulsed green (532 nm) laser. The TLS instrument has a beam diameter of 4 mm at a range of 0–50 m, a scan rate of up to 50 kHz, a maximum sample density of <1 mm, and a maximum range of 134 m at 18% albedo. Distance accuracy is ±4 mm and position accuracy is ±6 mm. The instrument records the amount of green laser light reflected back to the sensor, here termed green laser return intensity (GLRI), with a dynamic range of possible digital number

Directly following the TLS data acquisition, each subplot (0.5 m2 ) was destructively harvested and the aboveground dry biomass and %Na of the harvested plant material determined. Plant material was dried in a low temperature oven (50◦ C for 48 h) and weighed (W) before being ground in a Wiley Mill prior to %Na determination using automated dry combustion (TruSpec CN, Leco Corporation, St. Joseph, MI). From dry matter measurements, the NNI was calculated by using Eqs. (1) and (2) and setting coefficients ac and b to 5.3 kg N ha−1 and 0.44, respectively, as proposed by Justes et al. (1994) for wheat (for more information of how these coefficients are derived and to get these coefficients for other crop species, the reader is referred to Lemaire and Gastal, 2009).

2.4. Terrestrial LiDAR scanner derived estimates of aboveground crop dry mass To derive aboveground crop dry mass estimates from TLS data, a program (upon request available from the authors) was written in the in the Interactive Data Language (IDL) software package (Version 8.0, ITT Visual Information Solutions) to calculate vegetation volume based on the surface differencing approach (e.g., Loudermilk et al., 2009). For this, the program grids the TLS points onto two arbitrarily subdivided 0.01 m × 0.01 m regular gridded surfaces – a digital surface model (DSM) representing the earth’s surface of all surface features including canopy surfaces and a digital terrain model (DTM) representing the bare earth surface. The z value assigned to each grid cell by the program was the minimum (for DTM) or maximum (for DSM) z value contained within the search radius from the center of the grid cell or, if there was no z value within the search radius, linearly interpolated based on surrounding z values. The search radius for both the DTM and DSM was empirically determined by varying the search radius between 1 and 20 cm in 1 cm intervals. Finally, based on the DTM and DSM, the program calculates the laser derived vegetation volume (VL ) as follows:

⎛ ⎞ cl rl   VL = ⎝ (DSM(xc , yr ) − DTM(xc , yr )) × Area⎠ × SF c=cf r=rf

(3)

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where cf is the index of the first grid cell column, cl is the index of the last column, rf is the index of the first grid cell row, rl is the index of the last row, c is the column index, r is the row index, DSM(xc , yr ) is the height value of the DSM in meters at position xc , yr , DTM(xc , yr ) is the height value of the DTM in millimeters at position xc , yr , Area is the grid cell size (in this study 0.001 m2 ), and SF is the scaling factor (in this study = 20,000) that scales the vegetation volume from the sampled plot area (here, 0.5 m2 ) to a hectare. 2.5. Terrestrial LiDAR scanner derived crop nitrogen concentration estimates We utilized a computer program written in the open-source software package R 2.14.1 (R Development Core Team, 2011) to preprocess each sub-plot point cloud using a two-step process. First, to adjust for sensor drift (e.g., due to environmental conditions including air temperature, atmospheric pressure, and relative humidity), the computer program normalized the GLRI values measured within the subplot to values between 0.00 and 1.00 by dividing each of them by the average GLRI of the reference panel point cloud (hereafter, we refer to the normalized green laser return intensity as GLRIn ). Next, soil laser return values and edge laser return values (also known as mixed pixel, ghost return, or air return values) were filtered out from each plot point cloud by excluding GLRIn values that were below and above a GLRIn threshold. Edge laser returns occur when the laser beam is split on the edge of the crop foliage and strikes two or more surfaces behind that of the initial surface (Hebert and Krotkov, 1992; Tuley et al., 2005). The splitting of the laser beam results in GLRIn values that are lower than vegetation returns and consequently confound TLS derived %Na predictions (Eitel et al., 2011). The lower and upper GLRIn threshold values for a given year and growth period were empirically determined by varying the lower GLRIn threshold between 0.00 and 0.80 and the upper GLRIn threshold between 1.00 and 0.50 in 0.01 intervals. The lower and upper GLRIn threshold combination that resulted in the strongest relationship between %Na and GLRIn was selected for further analysis. 2.6. Terrestrial LiDAR scanner derived nitrogen nutrition index (NNI) Based on Eqs. (1) and (2), the laser derived nitrogen nutrition index (NNIL ) was calculated as follows: NNIL =

1/GLRIn ac × VL−b

(4)

GLRIn and VL were used as surrogates for %Na and W, respectively. Since GLRIn is inversely related to %Na , we utilized the inverse of GLRIn . The coefficients ac and b were set to 5.3 kg N ha−1 and 0.44, respectively, as proposed by Justes et al. (1994) for wheat (see also Lemaire and Gastal, 2009). 2.7. Statistical analysis Simple linear regression was performed in the open source software package R (Version 2.14.1, R Development Core Team, 2011) to examine the relationship between each of the observed crop parameters and TLS derived metrics. The goodness of fit was evaluated based on the coefficient of determination (r2 ) and the root mean square error (RMSE). To evaluate the temporal and spatial transferability of regression models, the 2011 regression models were used to predict crop parameters observed in 2012. Based on the resultant predictions, the root mean squared deviation (RMSD), the r2 , and the slope and intercept were calculated by fitting a

linear regression between the observed (dependent variable) to ˜ predicted variables (independent variable) (Pineiro et al., 2008). The RMSD quantifies the deviation of each predicted value against the 1:1 line and was calculated by using the R package “hydroGOF” (Zambrano-Bigiarini, 2013). 3. Results and discussion 3.1. Relationship between observed crop aboveground dry mass and terrestrial LiDAR scanner derived vegetation volume During tillering, aboveground biomass varied between 0.02–0.78 t ha−1 (in 2011) and 0.04–0.71 t ha−1 (in 2012) with a mean and standard deviation of 0.25 ± 0.20 t ha−1 (in 2011) and 0.29 ± 0.19 t ha−1 (in 2012). During jointing, aboveground biomass increased to a mean and standard deviation of 1.32 ± 0.66 t ha−1 (in 2011) and 2.42 ± 1.29 t ha−1 (in 2012) while ranging from 0.37–2.88 t ha−1 (in 2011) to 0.41–5.21 t ha−1 (in 2012). The combinations of search radii selected for fitting the DTMs and DSMs strongly affected the relationship between observed W and VL (Fig. 1). Depending on the selected search radii, the relationship between W and VL varied between an r2 of 0.00–0.79. Though the exact search radius values are likely to change depending on the TLS scan resolution, our results suggest that a small search radius slightly larger than the scan resolution should be selected for fitting a representative DSM. In contrast, the search radius for identifying ground returns for fitting a DTM has to be considerably larger than for the DSM and in this study appears to be approximately 2/3 of the row spacing (19 cm in this study). The search radii for fitting both the DTM and DSM that resulted in the strongest relationships between W and VL are given in Table 1. Using these search radii resulted in strong (r2 ≥ 0.72, RMSE ≤0.68 t ha−1 ), linear relationships between observed W and VL across both developmental stages and growing seasons (Fig. 2). The strength of the relationship shown here are comparable with results shown by others (e.g., Ehlert et al., 2009; Hosoi and Omasa, 2009). Some of the variance not explained by the regression model might have been caused by the gridding approach used in this study that simply assigns the minimum and maximum z-value within a search radius to each grid cell. This approach might introduce some error in sloped areas where it may result in the overestimation of vegetation height and thus W (e.g., Vosselman, 2000; Evans and Hudak, 2007). However, the majority of our plots were on relatively flat terrain (mean and standard deviation of slopes derived from TLS data acquired during tillering: 5.5 ± 1.4◦ in 2011; 6.36 ± 4.32◦ in 2012). Also, the TLS derived slope angles showed to be a non significant (p > 0.05) predictor in both 2011 and 2012 when included as a covariate into the regression models that predict W as a function of VL . Consequently, one can assume that slope and its effect on VL did not significantly confound W estimates in this study. The increasing scatter from the regression lines with increasing W (see Fig. 2) suggests that the accuracy of VL as a predictor of W tends to decrease with increasing W which is in broad agreement with earlier findings by Hosoi and Omasa (2009) who found that the accuracy of TLS derived plant area density estimates in wheat decreased with increasing canopy height. An explanation for our finding is that the likelihood of the laser beam to fully penetrate the canopy decreases with increasing canopy cover. Hence, the accuracy of the DTM decreases with increasing canopy cover resulting in less accurate laser based W estimates. To increase the amount of ground returns during later developmental stages, a close to nadir viewing (e.g., see work by Hosoi and Omasa, 2012) as opposed to an oblique viewing TLS used here, might be beneficial. Also, choosing a laser with a small beam diameter (<4 mm at measuring distance between TLS and plant) could increase the likelihood of a laser beam

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Fig. 1. Search radii for fitting the digital terrain model (DTM) and digital surface model (DSM) and resulting coefficient of determination (r2 ) for the regression model fit between the observed crop dry mass (W in t ha−1 ) and terrestrial LiDAR scanner (TLS) derived vegetation volume (VL ). The vegetation volume was calculated from the fitted DTM and DSM as follows: VL = (DSM–DTM) × Area.

to fully penetrate the canopy and thus the accuracy of TLS derived DTMs. Though beyond the scope of this paper, future research is needed to specifically compare the accuracy of TLS derived W estimates based on different approaches, such as the voxel approach or the surface differencing approach used in this study (e.g., see Loudermilk et al., 2009). Using the regression model fit between W and VL with 2011 data to predict W in 2012 revealed that the model is able to provide precise (r2 = 0.88, RMSD = 0.58 t ha−1 ) W estimates (Fig. 3). The intercept (−0.09) and slope (1.25) of the model indicate that the

model overpredicts W for values ≤ 0.36 t ha−1 and underpredicts W for values > 0.36 t ha−1 . Though further research is necessary to examine the spatial and temporal transferability of regression models that predict W based on VL , the initial results shown in this study are promising. What makes TLS derived W estimates such as the VL particularly useful for agricultural decision making is that the estimates provided are interpretable physical measures (e.g., height or volume estimates) in contrast to dimensionless measures provided by spectral vegetation indices that are often difficult to interpret relative to plant structure (Jackson and Huete, 1991).

Table 1 Search radii used for fitting the digital surface model (DSM) and digital terrain model (DTM) (see also Fig. 1). Year

Growth stage

Search radius search radius digital surface model (DSM)

Digital terrain model (DTM)

2011 2011 2012 2012

Tillering Jointing Tillering Jointing

1 cm 1 cm 1 cm 1 cm

14 cm 12 cm 9 cm 14 cm

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Fig. 2. Relationships between observed crop dry mass (W in t ha−1 ) and terrestrial LiDAR scanner (TLS) derived vegetation volume (VL ).

3.2. Relationship between observed and terrestrial LiDAR scanner derived crop nitrogen concentration The seeding and N treatments promoted a wide range of %Na as quantified by the summary statistics (min, max, mean ± sd) for tillering in 2011 (3.0%, 5.54%, 4.4 ± 0.7%), tillering in 2012 (2.8%, 5.7%, 4.5 ± 0.7%), jointing in 2011 (2.1%, 4.5%, 3.2 ± 0.6%), and jointing in 2012 (1.3%, 2.8%, 2.0 ± 0.4%). Depending on the upper and lower GLRIn threshold values chosen, the strength of the relationship between %Na and GLRIn ranged between an r2 of 0 and 0.75 (Fig. 4). The lower and upper GLRIn thresholds that resulted in the strongest relationships between %Na and GLRIn are given in Table 2. Based on the values in Table 2,

there appears to be no universally applicable lower and upper GLRIn intensity threshold though setting the latter to 0.44 ± 0.01 resulted in the strongest relationships between %Na and GLRIn except during jointing in 2012. Using the thresholds listed in Table 2, the GLRIn showed only a weak relationship (r2 ≥ 0.10, RMSE ≤ 0.63%) to %Na during tillering (Fig. 5). This might be explained by the relatively narrow foliage of wheat during tillering that was often not much wider than the laser beam diameter (4 mm). Hence, despite our attempt to remove edge returns based on a simple GLRIn threshold, the narrow leaves during tillering might have increased the likelihood that edge returns were included in the analysis which then adversely affected the strength of the relationship between GLRIn and %Na . To reduce the likelihood

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Fig. 3. Observed vs. predicted crop dry mass (W in t ha−1 ) for the 2012 growing season. Predictions of W were obtained by using the terrestrial LiDAR scanner (TLS) derived vegetation volume (VL ) measured in 2012 as input for the regression model that was fit with 2011 data between the observed W and terrestrial LiDAR scanner (TLS) derived vegetation volume (VL ).

of edge returns during tillering and thus improve the relationship between %Na and GLRIn , TLS with beam diameters of <4 mm are needed. The strength of the relationship between %Na and GLRIn increased during jointing (r2 ≥ 0.36, RMSE ≤ 0.32%) when compared to tillering (r2 ≥ 0.10, RMSE ≤ 0.63%). An explanation for this might be that the leaves during jointing were wider than during tillering which decreased the likelihood of edge returns being included in the analysis. The strength of the relationship between %Na and GLRIn during jointing 2011 was similar to the strength of the relationship (r2 = 0.68, RMSE = 0.30 ␮g g−1 ) shown in Eitel et al. (2011). However, the strength of the relationship in 2012 (r2 = 0.36, RMSE = 0.32%) appears to be considerably weaker when compared to 2011 and results shown in Eitel et al. (2011). The likely reason for this is that the measurements in 2011 and the ones shown in Eitel et al. (2011) were conducted during earlier stages of jointing (Zakoks 31) as opposed to the measurements conducted in 2012 during later stages of jointing (Zadoks 32). During these later stages of jointing, %Na only varied between 1.3 and 2.8% as opposed to earlier stages of jointing in 2011, where %Na varied between 2.1 and 4.5%. This wider range in %Na values in 2011 likely increased the strength of the relationship in particular by decreasing the unexplained variance quantified by the r2 value. The RMSE appears to be less affected. Some of the unexplained variance shown in Fig. 5 might be explained by the single wavelength used by the TLS. As a result, the laser return intensity is strongly affected by variations in leaf angle that affect the angle of incidence. For the same leaf, different

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amounts of light are reflected back to the laser instrument depending on the angle of incidence (Eitel et al., 2010). Another factor that can confound the laser return intensity is the inverse distance square law of light. With increasing distance, the laser return intensity decreases which complicates the correct interpretation of the laser return signal. In this study, we tried to minimize the effect of the inverse distance square law by keeping the distance between the sensor and the surveyed plots constant. Based on the 2011 regression model that relates %Na with GLRIn , %Na was predicted as a function of GLRIn for 2012 (Fig. 6). Though only the r2 value (0.86) may suggest relatively precise %Na predictions, the r2 value of the relationship appears to be mainly driven by two distinct data point clusters that drive the relationship. Removing either the data point cloud associated with %Na during tillering in the upper right or the data point cloud associated with %Na during jointing in the lower left would considerably weaken (r2 ≤ 0.36, RMSE ≥ 4.95%) the relationship. Also, the model accuracy appears to be limited as indicated by the large intercept (1.38) and flat slope (0.72). As a consequence of the large intercept and flat slope, the model appears to under predict %Na particularly for lower %Na levels (<4%). In summary, the relationships between GLRIn and %Na appear to be highly empirical, similar to the relationships between SVI and %Na . Further, the dimensionless nature of GLRIn may also make relationships based on this laser index difficult to interpret, analogous to SVI. However, the advantage of using a TLS for estimating %Na over traditional optical sensing technology is the small field of view (<5 mm) of the laser combined with its fast sampling rate (2–50 kHz routinely possible). The small field of view allows pure green vegetation returns to be isolated from non-photosynthetic tissue (i.e., soil) by using intensity thresholds and thus allows improving %Na estimates during early crop developmental stages when compared to optical remote sensing (Eitel et al., 2011). For example, Eitel et al. (2011) showed that the GLRIn more accurately predicted %Na (r2 = 0.68; RMSE = 0.30 ␮g g−1 ) in wheat during tillering than SVIs measured by a ground optical on-the-go sensor (r2 < 0.41, RMSE > 0.30 ␮g g−1 ). Improvements of the %Na and GLRIn relationships are expected with technological improvements as discussed more detail in Section 3.4 below. 3.3. Fusion of laser derived crop structural and biochemical information for estimating the nitrogen nutrition index The NNI was only calculated during jointing since during tillering all W values were <1.55 t ha−1 (Fig. 2) and thus below the threshold value when Eq. (1) does not apply and a constant %Nc of 4.4% is assumed instead (Justes et al., 1994). Though during jointing some plots also showed W values <1.55 t ha−1 (see Fig. 2), Eq. (3) was used to calculate an NNI value for all plots. The NNI in 2011 ranged between 0.42 and 1.06 with a mean and sd of 0.65 ± 0.15. In 2012, the NNI ranged between 0.22 and 0.89 with a mean and sd of 0.52 ± 0.16. All except of one NNI value were below 1, which suggests that almost all of the sampled wheat was limited by N. Using both VL and GLRIn as predictors of NNI in a multiple regression model showed that both VL and GLRIn were statistically significant predictors (p < 0.01) of NNI during both growing seasons. This suggests, as expected, that both information on W

Table 2 Lower and upper normalized green laser return intensity (GLRIn ) threshold values used to isolate vegetation GLRIn values (see also Fig. 4). Year

Growth stage

Lower normalized green laser return intensity (GLRIn ) threshold value

Upper normalized green laser return intensity (GLRIn ) threshold value

2011 2011 2012 2012

Tillering Jointing Tillering Jointing

0.43 0.45 0.44 0.62

0.64 0.79 0.64 0.92

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Fig. 4. Lower and upper normalized green laser return intensity (GLRIn ) threshold values used to isolate vegetation GLRIn values and resulting coefficient of determination (r2 ) for the regression model fit between the actual crop N concentration (%Na ) and GLRIn . No data were calculated for the white area within the plot.

and %Na are necessary for predicting NNI. We used Eq. (4) with VL and GLRIn as surrogates for W and %Na , respectively, for calculating a laser based estimate of the NNI. Relating the resulting NNIL to NNI showed weak (r2 = 0.32; RMSE = 0.12 NNI) to moderately strong (r2 = 0.51; RMSE = 0.11 NNI) relationships in 2011 and 2012, respectively. The strength of the relationship between NNIL and NNI could be improved for both 2011 (r2 = 0.45; RMSE = 0.11 NNI) and 2012 (r2 = 0.54; RMSE = 0.11 NNI) by cubing GLRIn so Eq. (4) was slightly revised as follows: NNIL =

1/GLRI3n 5.3 × VV −0.44

(5)

Cubing GLRIn adjusted the relative magnitude of GLRIn to similar relative magnitudes observed for %Na , which explains why cubing GLRIn improves laser based estimates of NNI (Fig. 7). Using the 2011 regression model between NNI and NNIL for predicting NNI in 2012 showed that though the prediction model had an intercept of 0.00, the slope of 1.39 caused the model to underpredict NNI (Fig. 8). The prediction error of the model was at 0.18 NNI as

quantified by the RMSD. Further research is needed to determine if the accuracy of such model predictions is sufficient to inform fertilizer decisions. Though studies have been conducted using optical remote sensing for determining NNI (e.g., Erdle et al., 2011; Houlès et al., 2007) comparisons of these results with the results shown here are difficult. For example, in areas where W and %Na co-vary, optical remote sensing would likely provide accurate NNI measurements as opposed to areas where W varies independently of %Na such as commonly found in water limiting systems. Hence, for a fair comparison, both sensors should be evaluated simultaneously following some initial work by Eitel et al. (2011) who compared the performance of a green scanning TLS and a ground optical onthe-go sensor for remotely sensing %Na during jointing (Zadoks developmental stage 3.2). Finally, though beyond the scope of this paper, future work is warranted to explore approaches other than the NNI approach for translating TLS derived %Na and W estimates into a measure of crop N status that allows informing N fertilizer management decisions.

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Fig. 5. Relationships between actual crop N concentration (%Na ) and GLRIn .

3.4. Future development needs for the operational use of terrestrial LiDAR scanning for agricultural decision making To further develop and test TLS technology for simultaneous sensing of W and %Na in agricultural systems, it is necessary to develop rugged, relatively inexpensive (<$8000), multi-wavelength TLS with a beam diameter of <4 mm that can be operated from a moving platform. Though relatively inexpensive TLS exist that can be operated from a moving platform, such as the instrument used by Long and McCallum (2013), this instrumentation has not been explicitly designed for the use in agriculture where it will be exposed to conditions, such as vibration and dust,

that might adversely affect the instrument performance. Hence, rugged TLS are necessary that have been specifically designed for such conditions analog to commercially available optical sensors such as the GreenSeeker (Trimble Navigation Limited, Ukiah, CA), CropCirlce (Holland Scientific, Inc., Lincoln, NE), or Yara N-Sensor (Yara, Germany). Further, the TLS used in this study was operated from a tripod which is not operational for agricultural decision making since it is time consuming and the point based estimates of W and %Na do not allow to fully capture the within field variability in crop N status in most agricultural systems. To be suitable for agricultural decision making, TLS technology is necessary that can be operated

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Fig. 6. Observed vs. predicted crop N concentration (%Na ) for the 2012 growing season. Predictions of %Na were obtained by using the GLRIn measured in 2012 as input for the regression model that was fit with 2011 data between %Na and GLRIn .

from a moving platform while fertilizing to fully capture the variability of crop N status throughout a farm field. Such mobile TLS becomes increasingly available and some work has already been conducted (e.g., Long and McCallum, 2013; Ehlert et al., 2009) that employed TLS from a moving platform for monitoring crop biomass. Building on the research presented in this work here, research is warranted that utilizes a TLS from a moving platform to simultaneously acquire information about W and %Na for estimating the NNI. For such work, a multi-wavelength TLS that employs a NIR reference wavelength in addition to the green wavelength would be desirable. The availability of at least two wavelengths would allow

Fig. 8. Observed vs. predicted nitrogen nutrition index (NNI) for the 2012 growing season. Predictions of NNI were obtained by using the laser derived nitrogen nutrition index (NNIL ) measured in 2012 as input for the regression model that was fit with 2011 data between NNI and NNIL .

calculating a laser based index value. Index values have been widely used in optical remote sensing to minimize confounding factors such as variations in viewing and illumination geometry on remote estimates of vegetation properties (Jackson and Huete, 1991). Similarly, a laser based index could be calculated to minimize the effect of leaf angle, sensor drift, and the inverse distance square law on TLS derived %Na predictions (Eitel et al., 2011, 2012). Moreover, laser vegetation indices, particularly when employing wavelengths in which plants and soil strongly differ in the amount of light they absorb, could allow to more easily separate vegetation returns from confounding mixed edge and soil

Fig. 7. Relationships between the observed nitrogen nutrition index (NNI) and the laser derived nitrogen nutrition index (NNIL ).

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returns. Consequently, multi-wavelength TLS could help to considerably improve the relationships between %Na and make the use of a reflectance panel for adjusting for sensor drift obsolete (Eitel et al., 2011). Several prototypes of multi-wavelength TLS have been developed over the recent years (e.g., Gaulton and Danson, 2013; Woodhouse et al., 2011; Hakala et al., 2012) but to our knowledge none of them have yet (as of 2013) been tested for sensing %Na of crops and/or from a moving platform. Finally, the multi-wavelength TLS should be designed so its beam diameter at measuring distance is <4 mm. The smaller the beam diameter at measuring distance the lower the likelihood of edge returns that confound relationships between %Na and GLRIn . 4. Conclusions Reliable information on crop nitrogen status is necessary for sound in-season N management decisions. Crop nutritional status is a function of both W and %Na and can be quantified by the NNI. This study showed that green scanning TLS technology can provide independent estimates of both W and %Na during early wheat developmental stages when further decisions regarding N management can be made (e.g., topdressed N). W estimates are based on the x, y, z coordinates surveyed by the TLS whereas the %Na estimates are based on the laser return intensity. TLS derived W estimates showed to be reliable across growing seasons and developmental stages as opposed to %Na estimates which appeared to be less reliable particularly during tillering. Fusing the structural (derived from x, y, z measures) and biochemical information (derived from intensity measures) provided by the TLS allowed estimating the crop nutritional status by calculating a laser based NNI. The laser derived NNI showed a moderately strong relationship to observed NNI. Optical remote sensing theory suggests that TLS derived %Na estimates and hence NNI estimates could be improved with a multi-wavelength laser system which would allow calculating TLS based vegetation indices. This work highlights the necessity for the development of mobile, multi-wavelength, TLS systems to further develop and improve this promising technology that could be a viable alternative to the use of optical remote sensing for onthe-go fertilizer decisions. Acknowledgements We thank David Uberuaga for implementing the study design at the Cook Experimental Farm and to Sam Finch for assisting during field work of this study. We greatly appreciate Margaret Davies for processing and analyzing wheat samples in the laboratory. This work was supported by USDA-NIFA Award Nos. 2011-67003-3034 and 2011-68002-30191. Use of trade names does not constitute an official endorsement by the University of Idaho. References Bréda, N.J.J., 2003. Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. J. Exp. Bot. 54, 2403–2417. Cassman, K., Dobermann, A., Walters, D., 2002. Agroecosystems, nitrogen-use efficiency, and nitrogen management. AMBIO 31, 132–140. Cassman, K.G., 1999. Ecological intensification of cereal production systems: yield potential, soil quality, and precision agriculture. Proc. Natl. Acad. Sci. U. S. A. 96, 5952–5959. Cerovic, Z.G., Masdoumier, G., Ghozlen, N., Ben Latouche, G., 2012. A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids. Physiol. Plant. 146, 251–260. Clawges, R., Vierling, L., Calhoon, M., Toomey, M., 2007. Use of a ground-based scanning lidar for estimation of biophysical properties of western larch (Larix occidentalis). Int. J. Remote Sens. 28, 4331–4344. Diacono, M., Rubino, P., Montemurro, F., 2012. Precision nitrogen management of wheat. A review. Agron. Sustain. Dev. 33, 219–241. Ehlert, D., Adamek, R., Horn, H.-J., 2009. Laser rangefinder-based measuring of crop biomass under field conditions. Precis. Agric. 10, 395–408.

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