Calibration of LAI-2000 canopy analyser with leaf area index in a young eucalypt stand

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1 Trees (2006) 20: DOI /s y ORIGINAL ARTICLE Steven B. Dovey BenduToit Calibration of LAI-2000 canopy analyser with leaf area index in a young eucalypt stand Received: 29 October 2003 / Accepted: 16 November 2005 / Published online: 20 December 2005 C Springer-Verlag 2005 Abstract Plant area index (PAI) measured with a LI-COR LAI-2000 plant canopy analyser (PCA) was calibrated with leaf area index (LAI) in a young stand of Eucalyptus grandis in the KwaZulu-Natal Midlands, South Africa. Destructive sampling and allometric equations were used to estimate LAI at 2 and 3 years after planting. Significant correlations (P<0.001) were found between LAI and PAI for each age with different equations being generated for the two ages (LAI=1.0594(PAI) at 2 years of age, and LAI=1.0393(PAI) at 3 years of age). The equations differed from those reported in other eucalypt studies, as the PCA in this study over-predicted LAI at 2 years, and slightly underpredicted at 3 years, of age. It is argued that the stage of growth influenced this calibration, as the canopy and foliar structure may have been different in the young stands, affecting the basic assumptions for the PCA. A broad conversion from PCA derived PAI to LAI may not necessarily be valid for young, short rotation eucalypt plantations. Keywords Introduction LI-COR. Eucalyptus grandis. Canopy area Leaf area index determination Light interception by forest canopies is largely dependant on leaf area index (LAI), defined as the single-sided leaf area per unit ground area (Deblonde et al. 1994). Many different methods involving destructive sampling, litter-fall sampling and indirect light-based readings have been used for the estimation of LAI in plantation forests (Attiwill 1962; Gower and Norman 1991; Battaglia et al. 1998; Barclay and Trofymow 2000). S. B. Dovey ( ) B. du Toit Institute for Commercial Forestry Research, Box , Scottsville, 3209 South Africa steven@icfr.unp.ac.za Tel.: Fax: The LI-COR LAI-2000 plant canopy analyzer (PCA) is used in this study at two ages in a young Eucalyptus grandis plantation to estimate LAI. The PCA indirectly estimates plant area index (PAI) from simultaneous measurements of intercepted diffuse light at five distinct zenith angles through a fisheye light sensor. The gap fractions at the five angles are used to calculate the canopy area based on mathematical calculations (LI-COR 1992). This method involves the relationship between leaf area and the probability of light being intercepted as it passes through the canopy. In order to simplify the estimation of LAI by gap fraction techniques it is assumed that the canopy is horizontally homogenous. The foliage elements are assumed to be small and black, have a set angular distribution and be randomly distributed azimuthally and in space (Chason et al. 1991; LI-COR 1992). These assumptions may not be fully adhered to in forest canopies, as some level of foliar clumping (gathering of leaves into clusters along the branches), light scattering and transmission of light through the foliage may occur. No distinction is made between foliage, branch and stem elements with light measurements, i.e. the entire canopy area is measured. The divergences from the assumptions often result in an underestimation of LAI, and hence a need to correct the PAI measurements. Provided a good relationship exists between optically estimated PAI and LAI in a forest stand, a calibration model may be formulated to convert PAI to LAI. Many such calibrations have been successfully generated for mature plantation forest tree species to account for differences in canopy architecture and divergence from the basic assumptions in gap fraction measurements and have generally shown that the LAI-2000 PCA tends to underestimate LAI, e.g. Barclay and Trofymow (2000) for Douglas-fir; Chason et al. (1991) for oak-hickory; Cherry et al. (1998) foreucalyptus nitens; Nackaerts et al. (2000) for Corsican pine and Stenberg et al. (1994) for Scots pine. These conversion factors assume the level of non-adherence to the gap fraction assumptions to be consistent within a species and have been calculated only in mature plantations.

2 274 No work has been reported for calibrating the PCA in young short rotation plantation forests that have high foliage : woody ratios, low canopy bases, different leaf morphologies and different canopy architectures. It is also uncertain whether the calibration may change with age or become constant at some level of stand maturity. The accuracy and age-related dynamics of the calibration of the LAI-2000 to predict LAI in young stands or in early canopy growth stages is unknown. Calibrations were developed in this study to correct PAI to LAI, intended initially to demonstrate early growth differences as a response to silvicultural treatments. Destructive determinations of LAI using allometric relationships were compared to PAI readings for closed canopy E. grandis at age 2 years and age 3 years to determine whether or not a single conversion can be used for both ages. Materials and methods Site description This study was conducted in a stand of E. grandis situated in the midlands of KwaZulu-Natal, South Africa, and superimposed on a long-term site productivity trial, planted in February 1999 (du Toit et al. 2001). The site was situated at an altitude of 1260 m above sea level, had a mean annual precipitation of 950 mm and a mean annual temperature of 15 C. The trial consisted of 24 rectangular plots measuring 44 m 39 m with their boundaries defined between the tree rows. Initial planted stocking was about 1680 stems per hectare (spacing m). Soil fertility was altered in each of six treatments by manipulation of the forest floor and soil. Intensive silvicultural treatments have been shown to greatly affect tree and canopy development in the early stages of stand growth (Schönau 1984; Schönau 1989), as was accomplished in this study through harvest residue burning, total harvest residue removal and by fertilization (du Toit 2003). There were only a few small gaps in the canopy as complete canopy closure had occurred a few weeks prior to this study. Plant canopy analyser readings were taken at 3-month intervals and LAI was estimated by destructive leaf samplings to coincide with PAI measurements at ages 2 and 3 years. A distinct range of growth and LAI values on this site made the calibration of the PCA and hence this study feasible. PAI measurement PAI readings were taken on overcast windless days using two PCA s in remote mode (LI-COR 1992) with one sensor located above open grassland adjacent to the trial. Readings below the canopy were taken as 16 readings in four transects after Cherry et al. (1998) within an 8 6 tree sub-plot at the centre of each plot. The sensor was held at a distance of at least 0.6 m from the canopy base, level with the terrain. A 270 view cap was used to block the operator from the sensor s view and PAI was calculated for each plot omitting the fifth sensor ring to prevent measurements of neighbouring plots or edge tree rows. Destructive sampling All the trees in each plot were assessed for height, diameter at breast height (dbh, 1.3 m), and canopy length. Sample trees were selected from within one standard deviation unit on either side of the mean dbh of each treatment and within two standard deviation units from the mean of all trees over the trial site. Due to limited resources and the risk of damage to the trial a limited number of 24 sample trees was taken for each of the 2-year and 3-year destructive sampling events. It is unlikely that the sampling or calculation methodologies could have affected the results as the sampling procedure ensured that trees over virtually the entire range of size classes were sampled by using a relatively large number of trees in each measurement plot. After felling the sample trees, the leaves were picked from each sample tree and weighed. A quarter of the leaves were taken from each tree as a sub-sample for specific leaf area (SLA) determination. Prior to oven drying at 65 C, a LI Area Meter was used to determine the area of the sub-sample leaves. The wet:dry mass ratio and SLA for each sub-sample was calculated and used to estimate total leaf area for each sample tree. Total woody dry mass as the sum of stem, bark and branches was estimated by the same sub-sampling technique as above and used to calculate foliage:woody ratio. Scaling up leaf area index Sample tree leaf areas and measurements were combined for each age independently over the site through regression analyses to establish allometric equations. Many factors describing the trees and plot variables were tested in conjunction with dbh in an effort to best predict tree leaf area. Treatments imposed on this trial were tested in the regressions and had no significant statistical influence on the allometric relationships. The regression relationship for age 2 years only required dbh to best predict tree leaf area. The mean tree height (mht) combined with dbh produced the best regression fit for age 3 years. These two allometric equations were used to estimate the area of leaves on all the trees in each plot for the two ages, respectively. The predicted leaf areas of the individual trees in each plot were summed and LAI was calculated by dividing by the plot area. Plant area index and leaf area index comparison LAI estimated from the destructive sampling was plotted with PAI and a simple regression was used to describe their relationships at each age. Regression lines were compared between ages using a t-test. All regressions and statistical analyses were performed using Genstat 5th edition statistical analysis software (VSN 2001).

3 275 Table 1 General mean growth data for age 2 and 3 years Attribute Age 2 years Age 3 years Mean Range Mean Range dbh (cm) Height (m) Canopy length (m) Foliage: woody 24% 10% 10% 6% SLA (m 2 kg 1 ) LAI MTA PCA dbh is diameter at breast height (1.3 m); The woody is the sum of the stem, bark and branch dry mass; SLA is oven dry specific leaf area; MTA PCA is the mean tip angle (leaf angle) as calculated by the LAI-2000 plant canopy analyser, LAI is the leaf area index In order to better understand the differences between the canopies at each age, canopy lengths, foliage: woody ratios, SLA, PCA derived mean tip angle (MTA PCA ) and light extinction were assessed. Light extinction was calculated by using the fraction of light below the canopy recorded by the PCA in relationship to destructively determined LAI. Radiation at the bottom of the canopy (I) is related to LAI and can be calculated by the following equation after Linder and Parsons (1985): I = I 0 e k LAI where I 0 is the above canopy radiation, k the extinction coefficient and LAI the leaf area index. The extinction coefficients gives an indication of the light intercepted in relation to LAI for the tree canopies at each age. Results Table 1 reveals general age-related differences in mean dbh, mean height, mean canopy length and mean foliage:woody ratios. The trees were larger at age 3 than at age 2 years and had longer canopies and lower foliage:woody ratios. Specific leaf area and LAI was higher for the younger canopy than the older one. Ratios between LAI and canopy length were higher at 2 years of age (0.63) that at 3 years of age (0.47). Estimated leaf angle was lower for the 2- year-old than for the 3-year-old trees. Ranges in growth attributes were larger at 3 years of age than at 2 years of age. Ranges in foliage attributes and foliage:woody mass ratio were lower at 3 years of age than at 2 years of age. The regression summaries in Table 2 show sample tree leaf area to be linearly related to dbh, with dbh accounting for 91% of the sample variation at age 2 years. The addition of mht to dbh at age 3 years improved the regression fit to 88% of the sample variation as opposed to 77% for dbh alone. A good fit at each age allowed for the development of allometric relationships to predict leaf area for all the trees at each age independently. Final models were leaf area (age 2)= dbh and leaf area (age 3)= dbh mht. Plant area index vs. leaf area index Table 3 shows the regression summaries of PAI and LAI at each age with a high level of significance for each of the two ages. The final relationships are LAI (age 2)= PAI and LAI (age 3)= PAI. Figure 1 is a graphical representation of LAI against PAI for the two ages together with the relationship developed by Cherry et al. (1998)for 6-year-old E. nitens (Fig. 1). The relationship shows PAI to marginally overpredict the destructively calculated LAI for the 2-year-old stand and slightly underpredicts LAI for the 3-year-old stand. The t-tests revealed the slopes as not significantly different between ages, although the intercepts were significantly different (F P<0.001). The calculated k values were higher (0.55) for the 2-year-old than for the 3-year-old trees (0.42). Discussion Tree canopies are highly dynamic during their development (Plates 1 and 2). Even in a small time frame such as our study, we have observed some changes in canopy characteristics (Table 1). The 2-year-old trees had a shorter canopy with a higher k value, while the 3-year-old trees had a longer canopy and a lower k value. Light adsorption at a given LAI is greater in stands with a higher k value. Dynamic changes in the canopy may lead to many simultaneous changes in the degree of adherence to the gap-fraction assumptions upon which PAI calibration is based. If these assumptions Table 2 Models generated from simple and multiple regression analyses to predict leaf areas of 2 and 3 year old sample tree data dbh diameter at breast height; mht plot mean height; r 2 ithe the regression variance accounted for; SE is the standard error; Final models: leaf area (age 2)= dbh 32.47; leaf area (age 3)= dbh mht Age 2 years Age 3 Years d.f m.s d.f m.s Regression (p<0.001) (p<0.001) Residual Total Regression estimates Constant (p<0.001) dbh (p<0.001) (p<0.001) mht (p<0.001) r SE

4 276 Table 3 Models generated for the relationship of PAI vs. LAI by simple regression analysis for 2 and 3 year old trees PAI plant area index; SE standard error; r 2 ithe the regression variance accounted for; SE is the standard error; Final models: LAI (age 2)= PAI 0.892; LAI (age 3)= PAI Age 2 years Age 3 Years d.f m.s d.f m.s Regression (p< 0.001) (p<0.001) Residual Total Regression estimates Constant (p=0.042) PAI (p<0.001) (p<0.001) r SE Fig. 1 Comparison of LAI as measured by destructive sampling with PAI as measured with LAI-2000 for 2 age classes, and the relationship for 6year-oldE. nitens by Cherry et al. (1998) years 3 years E. nitens 1:1 LAI from harvesting PAI from LAI-2000 are met leaves are (a) small and black; (b) randomly distributed, (c) have a set angular distribution, and (d) form part of a horizontally homogenous canopy, light measurement techniques should yield accurate estimates of PAI. If non-foliar elements make up a minimal fraction of the canopy, PAI should correlate well with LAI. Photographs of the canopies of 3 (Plate 1) and 8 (Plate 2) year- old stands of E. grandis looking directly up into the canopies from the sensor view. Note the increases in the degree of foliar clumping and the fraction of non-foliar elements that will be observed by the sensor in the older stand. We have observed strong relationships between PAI and LAI at age 2 and 3 years, more particularly at age 3, that

5 277 had slopes close to 1.0 (Fig. 1; Table 3). It appears that the degree of adherence to assumptions (b), (c) and (d) may have been high at these two ages, since there was little change in the estimated MTA, no visible clumping as is often the case in older canopies (cf. Plate 1 vs. Plate 2) and the canopy was fairly homogenously spread over a large vertical axis (Table 1 and visual observations). The apparently small degree of non-adherence to assumptions (b), (c) and (d) may have lead to underpredictions of the LAI in this study. Similar observations have been made by Battaglia et al. (1998) fore. nitens and Hingston et al. (1995), cited by Cherry et al. (1998)for E. globulus, where in both cases the PAI reading obtained with the PCA was shown to under predict the LAI by a factor of The under-prediction of LAI by the PCA in these studies has been attributed to foliar clumping and light scattering. Deviations from assumption (a) would lead to inflated PAI readings, while an increase in non-foliar elements (e) would change the relationship between PAI and LAI. Such deviations would both have the net effect of overpredicting LAI. Overestimation has been shown only when LAI was very low and this has been attributed to the stems and branches accounting for a large portion of the PAI (Chason et al. 1991; Smith et al. 1991; Deblonde et al. 1994). However, it is important to note that LAI versus PAI calibration studies on young tree stands (i.e. trees with low canopies and large SLA) were conspicuous by their absence in the published literature. The level of adherence to the assumptions of the gap fraction theory may have changed with age. At age 3 years, any deviations from assumptions (b), (c) and (d) that lead to an underprediction of LAI may have been similar in magnitude to any overprediction attributable to deviation from assumption (a) and the presence of non-foliar elements. The net effect was a near perfect fit. At age 2 years, the slight overprediction may be because of deviation from assumption (a). The foliar elements were larger (higher SLA than at age 3 years; Table 1) and the canopy lower and closer to the fish-eye lens of the PCA. Non-foliar elements probably had little influence in such young stands as the leaves mostly obscure them, as depicted by the high foliage : woody ratio (Table 1). These hypotheses require testing with additional observations. Conclusions This study highlights the need for different calibrations in juvenile and maturing stands. Generic conversion factors created for mature stands may not provide accurate LAI estimates in younger stands. The use of a single conversion equation for correcting PAI to LAI in short rotation plantation forests in their early growth stages, less than 3 years old in this case, must be done with caution. Further speciesspecific studies need to be done on a wider age series and over various levels of site conditions to provide insight into the reliability of an equation to convert from PAI to LAI for all ages in plantation forests. These studies need to be supplemented with measurements of canopy architecture and leaf morphology in order to understand the possible response mechanisms put forward in this study. Acknowledgements The authors wish to express their gratitude to the ICFR sponsors, to Anthony Job, Greg Fuller, and the late Thulani Ncgobo for the extensive fieldwork, to Sune Linder for advice, and to Keith Little and Colin Smith for constructive comments regarding this manuscript. References Attiwill PM (1962) Estimating branch dry weight and leaf area from measurements of branch girth in Eucalyptus. 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