Agricultural and Forest Meteorology 101 (2000) 247–250
Sampling methodology for LAI measurements with LAI-2000
in small forest stands
Kris Nackaerts (Research Assistant)
∗, Pol Coppin (Professor of Spatial Data Sciences),
Bart Muys (Assistant Professor of Forestry), Martin Hermy (Professor of Ecology)
Laboratory for Forest, Nature and Landscape Research, Department of Land Management, Katholieke Universiteit Leuven, VitalDecosterstraat 102, B-3000 Leuven, Belgium
Received 18 January 1999; received in revised form 10 December 1999; accepted 17 December 1999
Abstract
Leaf Area Index (LAI) plays a prominent role as an indicator of forest ecosystem condition in research on change detection. For this, rapid and reliable estimation of the effective LAI (LAIe) — this is the ratio of the total one-sided area of vegetation elements over the unit ground area) — at various scales is of utmost importance. We used the Licor LAI-2000 Plant Canopy Analyzer (PCA) for the acquisition of point LAI values within small (about 1 ha) stands. Canopy influences, external to the stand for which LAI was being assessed, and direct sunlight were excluded from respectively the LAIe computations and the fields of view of the PCA sensors by the use of a 270◦viewcap. The effect of sampling scheme and data aggregation
method on LAIe was quantified by means of a Monte Carlo simulation. The methodology presented is generalized and can be applied to forest stands with different canopy architectures. Our results show that for our study area the LAIe populations are normally distributed. A power function relationship was shown to exist between the relative accuracy of the acquired LAIe value and the sampling intensity. Based on this information, an appropriate sampling scheme can be selected for a predetermined relative accuracy. The method allowed us to quantitatively assess LAIe in small stands often occurring in very heterogeneous environments, which is typically the case for large parts of Western Europe. ©2000 Published by Elsevier Science B.V. All rights reserved.
Keywords:Leaf Area Index; Relative accuracy; Monte Carlo simulation; Sampling scheme
1. Introduction
Numerous studies on the Earth’s ecosystem single out Leaf Area Index (LAI), the total one-sided foliage area per unit soil surface area, as an important bio-physical parameter describing ecosystem conditions. Indirect LAI measurements, correlating total leaf area with the probability of light penetration through a
∗Corresponding author. Tel.:+32-16-329771; fax:+32-16-329760.
E-mail address:[email protected] (K. Nackaerts).
canopy, are most widely used (Welles, 1990). The Licor LAI-2000 Plant Canopy Analyzer (PCA) instru-ment incorporates this approach and has been used for this research (LI-COR, 1992).
However, indirect LAI measurements are sensitive to a range of external and internal factors, often induc-ing difficult-to-define errors in the final LAI estimate at the scale of interest, e.g. the forest stand. Illumination conditions and boundary effects are external factors independent of the properties of the element of inter-est, e.g. forest canopy. Sampling scheme (design and intensity), data aggregation method (from point-level
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measurements to one stand-level LAI value), and mea-surement errors caused by the inability to distinguish green foliage from other vegetation elements and by imperfections of the simplified Poisson foliage distri-bution model (assuming a random foliage distridistri-bution) are considered internal factors.
If the accurate quantification of temporal changes in stand-level LAI is of interest, a correction for canopy architecture influences as presented by Sten-berg (1996) and Chen (1996) is not needed if changes in canopy architecture are minor over the temporal domain of interest. In that case, stand-level LAI mea-surements only necessitate high relative monotempo-ral accuracy.
The objectives of this study were firstly, the min-imization of the effects of external factors, and sec-ondly, the quantification of the combined effects of sampling scheme (PCA-sensor-specific) and data ag-gregation method on final stand-level LAI estimation. It is evident that such a technique is of primary neces-sity and importance when a link is to be made to earth observation data, which are area- or pixel-based.
2. Materials
2.1. Study site
The test site for this research is situated in the Pijnven forest in Belgium (51◦10′N, 5◦20′E) at an average altitude of 55 m and measured 150 m×150 m and consisted of a 56 year old even-aged uniform Cor-sican pine stand (Pinus nigravar.corsicana) with an average canopy height of about 20 m. There was no significant presence of an understory.
2.2. Instrument description
The Licor LAI-2000 PCA is an instrument de-signed to measure LAI of green canopies from single vantage points. It encompasses five sensors, each si-multaneously measuring light intensities (blue range) in five concentric Field of Views or FOVs centered at zenith angles of 7, 23, 38, 53 and 68 degrees, and respectively referred to as PCA Sensors 1, 2, 3, 4 and 5. Usually, below- and above-canopy readings are simultaneously acquired and ratioed to calculate the
canopy gap fraction, which represents the probability of light penetration. Gap fraction values are then con-verted to contact frequency values that are used for further analysis (LI-COR, 1992). The effective LAI or LAIe (Chen et al., 1991) is calculated from these mea-surements (LI-COR, 1992). A random distribution of foliage in the canopy is assumed by the PCA model.
3. Methods
3.1. Measurement technique
A reference data set of individual PCA measure-ment points was established for the test site. The spa-tial sampling interval was 10 m, preventing an overlap of the first sensor’s conical FOV’s when the average tree height was 20 m. For ergonomic reasons, a regu-lar grid design oriented parallel to a test site bound-ary was selected for the sampling layout resulting in a series of concatenated FOV centers.
As Welles (1990) has pointed out, the use of a view-cap is required to prevent direct sunlight from hitting the sensor causing increased variability and underes-timation in LAIe measurements with the PCA instru-ment.
The zone free of border effects can be calculated for each sensor individually based on traditional trigonometry, given the sensor’s FOV and stand ge-ometry for each sensor individually. It describes the sensor-specific internal area of a forest stand. To op-timize the stand coverage, it was decided to use the 270◦viewcap.
Once the internal areas were defined for each sen-sor, only sensor-specific PCA data collected inside the corresponding internal area were retained for further analysis. Stand-level LAIe could then be calculated out of averaged ‘influence-free’ contact frequency values.
3.2. Quantification of measurement errors
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The distribution of point-level contact frequencies for the test site was tested for normality by means of the Kolmogorov D-statistic. The correlation between contact frequency data of adjacent sensors caused by a vertical overlap of their FOV’s, was tested. Spatial autocorrelation in the contact frequency data was ex-plored by means of an analysis of the semi-variograms. Independent data sets, having the same statistical and spatial characteristics as the source data set, were generated by Monte Carlo simulations. These values could be considered as if they were retrieved from be-low a ‘virtual’ forest stand with comparable architec-tural characteristics as the reference test site.
Subsequently, different realistic sampling schemes, with changing intensities were applied to the virtual measurements. The resulting simulated point-level data were aggregated into a single stand-level LAIe value. Each independent simulation of a complete set of new contact frequency values (one value for each sampling point) resulted in one new independent sample LAIe value for a given sampling scheme. The sample population could be used to estimate distribu-tion parameters of the populadistribu-tion that described the stand-level LAIe measurements.
The 95% confidence limits of the virtual LAIe mea-surements were cross-referenced against the number of sampling points and, if necessary, the sampling de-sign (e.g. spacing). A function was fitted through this data and used to select the most appropriate sampling scheme with respect to a required accuracy and exist-ing operational constraints. The minimum number of simulations was defined using a pre-determined accep-tance level of 90% for theR2of the fitted regression function.
4. Results and discussion
4.1. Quantification of measurement errors
Contact frequencies were sampled on a regular grid (10 m spacing) covering the whole test site (150 m×150 m). Taking into account the internal ar-eas, respectively 132, 110, 110, 81 and 24 samples for Sensors 1, 2, 3, 4 and 5, were retained out of the original 225 sampling points. Significant (significance level of 5%), but low coefficients of determination
were found between contact frequencies of adja-cent sensors under a first order parametric regression model (range from 0.18 to 0.24), indicating only a weak relationship. The uniformity and closure of the canopy on the study site can explain this. This re-sult might also be related to the fact that no spatial auto-correlation was detected in the reference data. However, larger gaps in the canopy should lead to an increase of the correlation together with a strength-ening of the spatial autocorrelation between the sensor-specific measurements. The only adaptation to the model presented, would be the use of an appro-priate random-number generator, which takes spatial autocorrelation into account (e.g. Goodchild, 1980).
For our test site, we found all sensor-specific contact frequencies to be normally distributed withp-values for the Kolmogorov D-test larger than 0.15.
To be able to determine the number of simulations needed to obtain adequate model parameters, the ab-solute confidence bounds of the simulated stand-level LAIe values were plotted against the corresponding sampling intensities for different numbers of tions. From about 50 simulations, additional simula-tions did not result in consistentR2gains.
The function displayed in Fig. 1 is characterized by an asymptotic behavior where the number of sampling points is concerned. The curve starts to flatten at ap-proximately 15 sampling points. Not only for this
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son, but also because of ergonomic considerations, a regular grid of 4×4 sampling points (16 in total) was
ultimately selected to assess stand-level LAIe under the conditions described in this paper.
The absence of spatial autocorrelation between internal area sampling points led to the fact that only sampling intensity had to be considered, and not the actual spatial distribution of the sampling points. However, it can be expected that stand architecture has a major impact on the optimal sampling scheme (intensity and design). First, canopy closure has an important impact on the variability in point-level con-tact frequency values. An increase in openness of the canopy would therefore lead to a relative increase of the variability of stand level LAIe measurements. Second, the pattern of gaps in the canopy has a major impact on the spatial autocorrelation between contact frequency values for different sampling points. Con-sequently, the impact of the sampling design on the accuracy of stand level LAIe measurements would increase.
5. Conclusion
First, a new measurement technique for the PCA instrument is proposed for LAIe assessment in small (about 1 ha in this study) forest stands. Sensor specific internal areas are defined based on the stand geometry and optimized by the use of a 270◦viewcap. Only data originating from inside the internal areas is aggregated for LAIe calculation, ensuring no elements outside the area of interest influences the LAIe assessment.
Second, a general applicable method is presented to quantify the relative accuracies of LAIe measure-ments for different sampling schemes. The value of
stand-level LAIe measurements then lays within this confidence interval for a set significance level. This information is of utmost importance if the measured LAIe values are to be compared with other stand-level LAI measurements as, for example, littertraps-derived LAI’s and remote-sensing-based LAI estimations.
Acknowledgements
The authors wish to acknowledge the Research Council of the Katholieke Universiteit Leuven under whose umbrella this research was sponsored and the forest supervisor of the State Forest Pijnven for their collaboration.
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