Journal of Experimental Botany, Vol. 54, No. 392, pp. 2403-2417,
November 1, 2003
© 2003 Oxford University Press
Ground-based measurements of leaf area index: a review of methods, instruments and current controversies
Received 24 April 2003; Accepted 9 July 2003
INRA, Forest Ecology and Ecophysiology Unit, Phytoecology Team, F-54 280 Champenoux, France
* Fax: +33 3 83 39 40 22. E-mail: breda{at}nancy.inra.fr
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Abstract |
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 Top Abstract IntroductionMaterials and methodsCurrent controversial issuesSampling strategyConclusion and issues for...References |
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Leaf area index (LAI) is the total one-sided area of leaf tissue per unit ground surface area. It is a key parameter in ecophysiology, especially for scaling up the gas exchange from leaf to canopy level. It characterizes the canopy–atmosphere interface, where most of the energy fluxes exchange. It is also one of the most difficult to quantify properly, owing to large spatial and temporal variability. Many methods have been developed to quantify LAI from the ground and some of them are also suitable for describing other structural parameters of the canopy. This paper reviews the direct and indirect methods, the required instruments, their advantages, disadvantages and accuracy of the results. Analysis of the literature shows that most cross-validations between direct and indirect methods have pointed to a significant underestimation of LAI with the latter techniques, especially in forest stands. The two main causes for the discrepancy, clumping and contribution of stem and branches, are discussed and some recent theoretical or technical solutions are presented as potential improvements to reduce bias or discrepancies. The accuracy, sampling strategy and spatial validity of the LAI measurements have to be assessed for quality assurance of both the measurement and the modelling purposes of all the LAI-dependent ecophysiological and biophysical processes of canopies.
Key words: Clumping, error, leaf area index, plant area index, sampling.
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Introduction |
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TopAbstractIntroductionMaterials and methodsCurrent controversial issuesSampling strategyConclusion and issues for...References |
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Leaf Area Index (LAI) was defined by Watson (1947) as the total one-sided area of leaf tissue per unit ground surface area. According to this definition, LAI is a dimensionless quantity characterizing the canopy of an ecosystem. Leaf area index drives both the within- and the below-canopy microclimate, determines and controls canopy water interception, radiation extinction, water and carbon gas exchange and is, therefore, a key component of biogeochemical cycles in ecosystems. Any change in canopy leaf area index (by frost, storm, defoliation, drought, management practice) is accompanied by modifications in stand productivity. Process-based ecosystem simulations are then often required to produce quantitative analyses of productivity and LAI is a key input parameter to such models. Ecophysiologists, but also managers (farmers and foresters), ecologists, site and global modellers, request information about canopy leaf area index. Unfortunately, this interface between ecosystem and atmosphere is very difficult to quantify, due to its spatial (horizontal and vertical) and temporal variability: annual cycles and interannual variability interact with the stand or crop structure, stratification and heterogeneity.
Since the reviews of Norman and Campbell (1989) and Welles (1990), many comparisons between the direct and indirect methods of LAI measurement have been published for crops (Brenner et al., 1995; Levy and Jarvis, 1999) and forest stands (Chason et al., 1991; Smith et al., 1991; Fassnacht et al., 1994; Dufrêne and Bréda, 1995; Comeau et al., 1998; Barclay and Trofymow, 2000; Küßner and Mosandl, 2000). Although no completely new equipment has been explicitly developed for LAI measurements since 1990, new topics including error analysis, cross-calibration, sampling strategy, spatial validation or scaling are emerging from the recent literature.
The objective of the present paper is to review all available ground-based methods for leaf area index measurement at site and stand/crop scales. Discussion of remotely sensed vegetation indices (either from satellite or air-borne high-resolution imagery) has been deliberately omitted though they have novel potential. From personal experience in forest ecology, remotely sensed vegetation indices at present need a site- and stand-specific calibration against ground-based measurements of LAI and still do not yield suitable results for complex canopies such as forests with a high LAI. Chen et al. (2002) reached a similar conclusion recently for LAI mapping in Canada. It is necessary to rely first on ground-based LAI estimates if remotely sensed vegetation indices need cross-calibration.
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Materials and methods |
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TopAbstractIntroductionMaterials and methodsCurrent controversial issuesSampling strategyConclusion and issues for...References |
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Direct methods
Direct or semi-direct methods involve a measurement of leaf area, using either a leaf area meter or a specific relationship of dimension to area via a shape coefficient. In coniferous species, projected leaf area differs from the developed one by a coefficient depending on a needle cross-sectional area (Grace, 1987; Barclay, 1998; Sellin, 2000). Leaf area is measured on a sub-sample of leaves and related to dry mass (e.g. via specific leaf area, SLA, cm2 g–1). Finally, the total dry mass of leaves collected within a known ground-surface area is converted into LAI by multiplying by the SLA. As the direct methods only relate to foliage, they are the only ones giving real access to leaf area index. They allow separate computation of the shape, size and number of leaves. Direct methods provide the reference for the calibration or evaluation of indirect methods. It is crucial to sample leaves correctly for establishing leaf area to dry mass ratio, as it changes among species and among sites for a given species. Figure 1 shows some averaged values of LAI estimated by direct measurements in forest stands. Direct methods include harvesting, allometry and litter collection.
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Harvesting the vegetation and measuring the area of all the leaves within a delimited area is the first method, widely used for crops and pastures. This method is well adapted for vegetation of small structure, but is destructive. Such an exhaustive approach cannot be applied to large areas or to large trees, but it is suitable for measuring LAI in the space of a gas exchange chamber.
Foresters have developed a less destructive method that relates foliage area to the diameter of the sapwood area at breast height or at crown base (Grier and Waring, 1974; Albrekston, 1984; Makela et al., 1995). The leaf area per unit sapwood area varies from 0.15–0.75 m2 cm–2 in conifers (Waring et al., 1982). It has been suggested that the product of sapwood area and sapwood permeability should improve the relationship with leaf area (Whitehead et al., 1984; Shelbrune et al., 1993). The underlying hypothesis is that leaf area is in balance with conducting tissues, hence such allometric relationships are site and species dependent, and, in some cases, also year dependent. Any changes in the leaf versus sapwood area ratios due to management, health, fertility (Brix and Mitchell, 1983) or ageing are not reflected in a single allometric relationship. For broad-leaved species, most of the diffuse porous species exhibit dispersed sapwood and, in ring porous species like oaks, the efficiently conducting sapwood is limited to the most recent rings (Rogers and Hinckley, 1979). Because of the difficulties of measuring the conducting area, the sapwood area should be replaced by more readily measured variables, such as diameter at breast height (Vertessy et al., 1995). Finally, if the establishment of allometric relationships with leaf area is conducted in individual trees by taking into account the height of branches, the vertical distribution of LAI may be estimated (Bidlake and Black, 1989; Maguire and Bennett, 1996). According to these authors, estimating allometric relationships through destructive sampling is a reliable method of deriving LAI for a given experimental site, but remains year-dependent. Hence such an approach cannot be used to describe a time-course of LAI recovery after any change in canopy opening.
In deciduous stands, a non-destructive method consists of collecting leaves in traps distributed below the canopy during leaf fall. Litter collection has been widely used in forest ecology. Litter has to be collected in a number of traps with a known collecting area every second week at least to avoid losses and decomposition. Collected litter is dried (at 60–80 °C for 48 h) and weighed to compute the dry mass of litter as g m–2. Leaf dry mass at each collection date is converted into leaf area by multiplying the collected biomass by the specific leaf area (SLA, expressed in m2 g–1). Finally, the leaf area index is the accumulated leaf area over the period of leaf fall (Fig. 2). The estimating of specific leaf area is the most critical point in this procedure. It varies with species (Chason et al., 1991; Niinemets and Kull, 1994; Fig. 3), site fertility (Vanseveren and Herbauts, 1977; Jurick, 1986; Burton et al., 1991; Fig. 4), date and year, duration of remaining in the traps, weather and even within stands (Bouriaud et al., 2003). Sorting leaves by species for weighing and establishing specific area ratio is of importance: litter collection is the only method giving access to the contribution of each species to total leaf area index (Fig. 5). Once again, this method is a reference one and is suitable for deciduous species: it can give a decrease of LAI during leaf fall (Fig. 2) and the contribution of each species to total leaf area index (Fig. 5).
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First proposed by Guittet (J Guittet, personal communication), the needle technique is derived from the inclined point quadrat method (Warren Wilson, 1959, 1960, 1963). It is an alternative for sampling litter in deciduous stands without traps. A fine needle of 1 mm in diameter is plunged vertically into the litter lying on the soil, as soon as all the leaves have fallen to avoid any decomposition of the leaves. With a vertical probe and horizontal leaves, the number of leaves collected on the needle corresponds to the contact number and equals the leaf area index. This method needs an intensive sampling (from 100 to 300 points) to quantify an average contact number and LAI properly (Nizinski and Saugier, 1988; Dufrêne and Bréda, 1995). The method is well suited for oak and beech forests with their large leaves and is easiest to apply in sites where litter is completely decomposed every year to avoid mixing with litter from previous years. Recently, this line-intercept method was adapted to an old Douglas-fir canopy in a spectacular way (Thomas and Winner, 2000): a vertical line (one edge of a fibreglass measuring tape, <0.10 mm thickness) was lowered from a crane from above the canopy and each intercept point was checked.
Indirect methods
Indirect methods infer leaf area index from measurements of the transmission of radiation through the canopy, making use of the radiative transfer theory (Anderson, 1971; Ross, 1981). These methods are non-destructive and are based on a statistical and probabilistic approach to foliar element (or its complement, gap fraction) distribution and arrangement in the canopy (Jones, 1992). LAI is calculated by inversion of the exponential expression of the gap fraction:
where 
is the zenith angle of view, 
is the leaf angle, P() is the gap fraction, G(, ) is named the G-function and corresponds to the fraction of foliage projected on the plane normal to the zenith direction. G(, ) depends on leaf-angle distribution . The latter is generally not known, and the LAI calculation requires gap fraction measurements for a range of angles of view. Another alternative is to work at an angle of elevation of about 32°, which is quite insensitive to distribution of leaf inclination (Warren Wilson, 1963; Jones, 1992).
Radiation measurement and ‘gap fraction’-based methods must be distinguished. The radiation measurement method uses the turbid medium analogy, which makes the assumptions that (1) leaves are randomly distributed within the canopy, and (2) individual leaf size is small when compared with the canopy. With these assumptions, gap fraction is equivalent to transmittance.
The gap fraction-based methods are dependent on leaf-angle distribution (Campbell, 1986). By inverting equation (1), the expression for LAI is:
as the G-function here is independent of the leaf-angle distribution, . The ‘gap fraction’-based methods (canopy analyser systems and hemispherical images) use several ways to solve this equation as described in theory papers (Miller, 1967; Nilson, 1971; Norman and Jarvis, 1974; Ross, 1981; Norman and Welles, 1983; Lang, 1986, 1987; Norman and Campbell, 1989; Bréda et al., 2002).
In fact, the indirect methods do not measure leaf area index, as all canopy elements intercepting radiation are included. Therefore, the terms of plant area index (PAI) or surface area index (SAI) are preferred if no correction to remove branches and stems is made.
Radiation measurement method: Monsi and Saeki (1953) expanded the Beer–Lambert extinction law to plant canopies. The law of Beer–Lambert expresses the attenuation of the radiation in a homogenous turbid medium. In such a medium, the flux is absorbed proportionately to the optical distance. The method of LAI evaluation by the inversion of the Beer–Lambert equation requires the measurement of both incident (Io) and below-canopy radiation (I). Following Monsi and Saeki (1953) and with a random distribution of leaves within the canopy:
where Io is the incident radiation, I is the radiation transmitted below-canopy, k is the extinction coefficient and LAI is the leaf area index. From equation (2), the expression for k is:
i.e. k is a function of leaf angle distribution, , and leaf-azimuth angle (Jones, 1992). The incident radiation can be measured above the canopy or in a nearby open area in the case of tall stands. Beer–Lambert’s equation is inverted to compute k, based on an independent direct measurement of LAI (by allometry or litter fall) and on the measured transmittance (Vose and Swank, 1990; Smith et al., 1991; Burton et al., 1991). Then, seasonal transmittance and k are used to derive LAI (Fig. 6). Several authors have discussed how to determine k (Ledent, 1977; Smith, 1993; Vose et al., 1995; Hassika et al., 1997) and the accuracy of the method (Nel and Wessman, 1993). Pierce and Running (1988) proposed the use of a constant k value of 0.52 for coniferous species based on measurements by Jarvis and Leverenz (1983). The extinction coefficient also depends on stand structure and canopy architecture (Turton, 1985; Smith et al., 1991; Dufrêne and Bréda, 1995). Campbell (1986) and Thomas and Winner (2000) ascribed 10% of the variation in LAI to the effects of alternative assumptions of distribution of foliage inclination. Comeau et al. (1998) have discussed integration over time and effects on LAI calculation. The canopy extinction coefficient is a function of wavelength (Jones, 1992), radiation type and direction (Berbigier and Bonnefond, 1995). It is also important to maximize spatial integration by the use of large sensors, linear sensors or mobile sensors. Some k values are listed in Table 1 for coniferous and broad-leaved stands. The variation is such that the k coefficient would better be estimated for every stand (Johansson, 1989; Cannell et al., 1989; Smith et al., 1991).
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In conclusion, the monitoring of seasonal transmittance remains one of the most efficient ways of daily monitoring both LAI increases and decreases. These two highly dynamic phases are difficult to survey with manual measurements, but are essential for the calculation of the seasonal time-course of energy fluxes. As an example, the progression of transpiration as LAI expands during spring in an oak stand has to be monitored with a daily resolution (Fig. 7). Other smaller LAI fluctuations, induced by successive flushing or by pest damage, could also be detected, dated and quantified by this method. Such measurements, nevertheless, often remain spatially limited by the number of below-canopy sensors used. An interesting answer may be to use a mobile sensor allowing both continuous and spatially integrated light measurements.
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Commercial canopy analysers: Four commercial canopy analysers are available for measuring the fraction of transmitted radiation that passes through a plant canopy. Two of them are suitable for operating in the sunfleck or the irradiance mode in the PAR waveband (SunSCAN, Delta-T Devices Ltd, Cambridge, UK; AccuPAR, Decagon Devices, Pullman, USA) and the two others measure the gap fraction for different zenithal angles. The LAI-2000 (Li-Cor, Lincoln, Nebraska, USA) measures in five zenith angles simultaneously, through a fish-eye light sensor, while the DEMON instrument (CSIRO, Canberra, Australia) measures direct beam radiation from the sun through a directional narrow angle of view (0.302 sr). Measurements with the DEMON instrument have to be repeated several times from early morning until noon to collect data over a range of zenith angles. The main characteristics of these instruments are listed in Table 2.
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The SunScan and AccuPAR (Decagon Devices, Pullman, USA) are two instruments that measure the incident photosynthetic active radiation (PAR) and the transmitted PAR. Both instruments were developed and optimized for low and regular canopies. The probe for the below-canopy measurement is a linear sensor including 64 or 80 equidistant calibrated photodiodes measuring in the PAR waveband (400–700 nm). Each photodiode can be logged individually if sunflecks are to be estimated or used to provide an average reading along the probe. Both instruments can work with alternative above- and below-canopy PAR measurements using only the linear probe. The SunSCAN probe can also be connected to a Beam Fraction Sensor (BFS) measuring both direct and diffuse incident radiation above the canopy and simultaneously connected to the common logger of the linear probe. This configuration is unsuitable for LAI measurements in tall canopies, for which application Delta-T proposes two alternatives: the first is to replace the cable connection between the BFS and the SunScan probe by a radio link. The effective distance between the two sensors using such a transmission should be worth testing under forest canopies as it may reach only 150–200 m (Ecotechnic, personal communication). The second option is to disconnect the BFS and to connect it to an independent data logger. These options work but increase the equipment cost and the advantage of the real-time LAI calculation and display is lost.
The Plant Canopy Analyser, LAI-2000 has been widely used for the ecophysiology of agricultural crops (Hicks and Lescano, 1995), coniferous stands (Gower and Norman, 1991; Deblonde et al., 1994) and deciduous stands (Dufrêne and Bréda, 1995; Cutini et al., 1998; Le Dantec et al., 2000). The simplest way to measure below- and above-canopy radiation is to use two cross-calibrated sensors connected to the same data logger, one devoted to above-canopy measurements, the other moving below the canopy. Another procedure consists of alternating below- and above-canopy measurements with a single sensor. For tall canopies such as forests, the above-canopy measurement is critical and limits the use of the instrument as an open area has to be found (theoretically with a diameter at least seven times the canopy height), which is generally only available far from the stand (outside the forest). To reduce the size of this required open area, view caps, providing azimuthal masking into several quadrants, are available. Another way to proceed is to use two cross-calibrated sensors and two synchronized loggers. In any case, these solutions are expensive and negate the advantage of the real-time LAI calculation and display. Another precaution is to perform measurements in diffuse radiation (i.e. under a uniformly overcast sky or a clear sky at sunset or sunrise). Figure 8 shows that PAI from LAI-2000 measurements underestimates LAI established by litter collection in beech stands.
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The Demon uses an extension of the point quadrat method (Warren Wilson, 1959, 1960, 1963), where the direct beam of the sun replaces the needle. Surprisingly, this instrument is little used in spite of its performance and its ease of use in forestry, agronomy and horticulture (Lang et al., 1990, 1991); Brenner et al., 1995; Fassnacht et al., 1994; Dufrêne and Bréda, 1995; Berbigier and Bonnefond, 1995; Fig. 6). In tall canopies, the operator moves beneath the canopy along a linear path, keeping the sensor oriented to the sun with the help of a sight. This is easy to do in most forest stands. In crops, the sensor is driven along a track beneath the canopy, still aimed at the sun. To compute the transmittance, the direct incident radiation has to be measured in a fixed position, which can be done in a small canopy gap because of the reduced viewing angle of the sensor. This is a clear advantage for LAI measurements in forest stands where only small open areas are available. Gap fraction is computed by logarithmic averaging of the transmittances of subgroups of the data (Lang, 1986, 1987). Gap fraction is finally expressed as a function of solar angle, and the measurements have to be repeated several times from early morning until noon. Figure 6 shows good agreement in the seasonal time-courses in an oak stand of PAI as measured with a DEMON instrument and LAI by global radiation interception and litter collection. Figure 9 compares direct LAI estimates with indirect PAI measurements by LAI-2000 and Demon canopy analysers. Good agreement between LAI and Demon-PAI is observed, while the best agreement between LAI and LAI-2000 PAI is obtained for a calculation using only the three upper rings (0–43° from zenith) of the hemispherical sensor (Dufrêne and Bréda, 1995).
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Canopy analysis systems based on hemispherical image analysis: Fish-eye photography (and related hemispheric view analysis) has been used for a long time to describe canopy structure, to map and quantify radiation microclimate below canopies, to calculate solar radiation indices (Anderson, 1964; Becker, 1971; Ducrey, 1975a, b), and, afterwards, to estimate the canopy leaf area index (Bonhomme, 1970, 1993; Bonhomme et al., 1974; Rich, 1990). Recently, classical fish-eye photography has been used to assess horizontal (Walter and Grégoire-Himmler, 1996) or vertical (Soudani et al., 2002) heterogeneity in canopies, but without validation by direct measurements. Hemispherical photography can also be used from above the canopy looking downward. In that case, bare soil has to be distinguished from woody material and live green vegetation by using reflectance ratios (Barnsley et al., 2000). However, as it involves many time-consuming steps from photography to LAI calculation, fish-eye photography was progressively forsaken for canopy analysers. Nevertheless, with the development of high-resolution digital cameras and advances in image processing software, there has been a renewal of interest in this method. To date, few published data are available to assess the performance of digital pictures compared with classical ones from film (Frazer et al., 2001). The following section will briefly compare three available commercial integrated instruments from photography through to LAI calculation (Table 3). Free software is available for computing LAI from any fish-eye photograph (GLA Gap Light Analyser from Gordon W. Frazer or WinPhot from Hans ter Steege). Information about the available tools may be obtained directly from the authors at www.bio.uu.nl/
boev/staff/personal/htsteege/htsteege.htm or www.rem.sfu.ca/ forestry/gla/gla_info.htm. Whatever the analysis system, hemispherical photographs with both digital and 35 mm images from film cameras must be taken under uniform sky conditions such as exist just before sunrise or sunset or when the sky is evenly overcast. The most critical step in image processing is probably determining the threshold between the sky and canopy elements.
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Both WinSCANOPY (Regent Instruments Inc., Quebec, Canada) and HemiView (Delta-T Devices Ltd, Cambridge, UK) are canopy analysis systems based on coloured hemispherical images. Their standard systems include a digital camera, a calibrated fish-eye lens to add to the standard camera and a self-levelling system. The systems do not require above-canopy measurements. Images are taken in the field and processed externally using specific software from each company. They calculate canopy parameters according to Norman and Campbell (1989), such as leaf area index (but not with the basic WinSCANOPY version), leaf-angle distribution and mean leaf angle, angular distribution of gap frequencies, and site factors (direct, diffuse, and global). It can also predict radiation values beneath the canopy. Most of the outputs are available by sky sector or aggregated into a single overall whole sky or annual value.
The digital plant canopy imager CI-110 is quite different because it takes and processes coloured hemispherical images in real-time in the field. The hemispherical lens is mounted on an auto-levelling design on the tip of a handle connected to a portable computer devoted to the equipment. The sensor plus auto-levelling system is 10 cm high, therefore no image can be captured at ground level. The CI-110 uses a digital camera to zoom and focus to gain a detailed picture of the canopy with a resolution of 640x480 pixels. No reference is needed. Pictures can be saved and analysed either in the field or in the laboratory. With the help of the software included for image processing, the operator can define the grid size by choosing a number of both zenithal angles and azimuthal divisions in the range of one to ten each. The software computes leaf area index from the gap fraction inversion procedure according to Norman and Campbell (1989), sky view factor, mean foliage inclination angle, foliage distribution and extinction coefficient of the canopy. No published results of cross-comparison among these instruments and software are available yet.
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Current controversial issues |
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TopAbstractIntroductionMaterials and methodsCurrent controversial issuesSampling strategyConclusion and issues for...References |
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Several papers have compared plant area index as measured by indirect methods with direct LAI estimates (Neumann et al., 1989; Chason et al., 1991; Lang et al., 1991; Smith et al., 1993; Fassnacht et al., 1994; Dufrêne and Bréda, 1995; Vertessy et al., 1995; Comeau et al., 1998; Küßner and Mosandl, 2000). Most of these papers concluded that indirect methods underestimated LAI compared with direct measurement. The reported underestimate varies from 25% to 50% in different stands (Gower and Norman, 1991; Cutini et al., 1998; Gardingen et al., 1999; Gower et al., 1999). It is now widely accepted that a reason for the underestimation is the non-random distribution of foliar elements within the canopy. The degree of error in the LAI measurement is a result of the canopy’s deviation from this assumption of random dispersion, which was named ‘clumping’ (Nilson, 1971; Lang, 1986, 1987; Kucharik et al., 1997; Chen et al., 1997). Many solutions have been proposed to overcome this clumping bias.
The first proposal was from Nilson (1971), who introduced a correction factor 
in the formulation of gap fraction. Chen et al. (1991) proposed a new term for effective LAI (Le), which equals to the product of by L, where L represents the actual LAI (equal to a harvested LAI measurement) and refers to a clumping index describing the non-random distribution of canopy elements. When a canopy displays random dispersion, is unity; when a canopy is clumped, is higher or lower than unity. More recently, several papers (Chen et al., 1991, 1997; Fournier et al., 1997; Walter and Torquebiau, 1997) reported that clumping occurs at several scales, between plants within a stand, and between branches or shoots within plants. The clumping factor was then divided into two components: e is the between-shoots clumping factor and 
e is the within-shoot clumping factor.
Two new instruments have been developed to measure the between-shoot clumping factor e: the TRAC (Tracing Radiation and Architecture of Canopies) developed by Chen et al. (1997) and the MVI (Multiband Vegetation Imager) developed by Kucharik et al. (1997). Table 4 compares the main characteristics of these two instruments. The spatial resolution of both devices is wider than within-branch gaps, so that they both measure e. Quantification of e requires laboratory measurements.
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Clumping factors estimated by the TRAC have recently been validated (Chen and Cihlar, 1995; Chen, 1996; Chen et al., 1997; Kucharik et al., 1997). The TRAC device is suitable for computing PAI, but Chen et al. (1997) advised correcting indirect LAI measurements (e.g. from the LAI-2000) using the clumping factor derived from TRAC estimates. Both instruments have recently been used to derive the ground-based LAI and to validate remotely sensed indices in Canada (Chen et al., 2002). The PAI derived from MVI has been validated against allometric LAI (Kucharik et al., 1998a, 1999) and the clumping factors estimated by the MVI have been compared with independent measurements. Some values of between-shoot clumping factor (
e), as estimated using either TRAC or MVI, are listed in Table 5. The between-shoot clumping factor estimated from TRAC measurements is twice as large as the MVI ones. According to Kucharik et al. (1999), this was due to differences in maximum gap sizes and gap-size distributions in conifers obtained at different measurement angles, because the canopy gap-size distribution is dependent on zenith angle. In these studies, gap-sizes were measured at solar zenith angles of 30–70° with TRAC, while towards the zenith with MVI.
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Several authors suggested that indirect methods measured a shoot area index not a leaf (or needle) area index (Gower and Norman, 1991; Chen, 1996; Chen et al., 1997). The leaf area index should be calculated simply as the product of indirect measurement by the within-shoots clumping factor (
e) according to Gower and Norman (1991). They first used the ratio between total projected area of needles and shoot projected area for coniferous trees. Their ratios are used in the LAI-2000 user’s manual (Li-Cor, 1992) and equal 1.5 (±0.41), 1.61 (±0.35), 1.49 (±0.28), and 1.6 (±0.14), for Pinus resinosa, Pinus strobus, Larix decidua, and Picea abies, respectively. Fassnacht et al. (1994), Stenberg et al. (1994) and Chen (1996) pointed out that these authors measured the clumping factor using a single vertical projection. They noted that the projected area depends on both shoot inclination and radiation direction and suggested a new method to quantify the clumping. Table 6 indicates some clumping factors e as estimated by multi-angular projection. In deciduous forests, the value of e is 1.0, while in conifers values of e are typically between 1.2 and 2.0 (Kucharick et al., 1998b). This is due to the distribution of branches, shoot and needles whorls being highly non-random.
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This last source of discrepancy between direct and indirect measurement is specific to forests and shrubs. All the indirect optical methods calculate a Plant Area Index, because they include the contribution of stems and branches. Figure 10 shows an example of variability among oak stands of both PAI measured at maximum LAI and Wood Area Index measured during winter. Wood Area Index ranges from 0.43 to 2.45. Taking into account both clumping and woody parts, Chen (1996) expressed the effective LAI (Le) as (
e/e) PAI. Leaf area index (LAI), strictly, is the difference between PAI and wood area index, WAI: LAI=PAI–WAI=PAI(1–) where is the ratio of WAI to PAI or LAI=(1–) Le e /e. LAI is the measured parameter using direct methods, while indirect methods compute Le. The accurate estimation of LAI, therefore, requires the determination of the contribution of both clumping and woody parts. Several studies have attempted to estimate WAI either directly or indirectly (Table 7). The recent review by Gower et al. (1999) makes these main points: (1) the contribution of woody parts to PAI as measured by indirect methods ranges from 5–35% and (2) PAI has then to be corrected. Nevertheless, the procedure for deriving LAI from PAI is still a much debated question. Lang (1991) and Chen (1996) thought that PAI equals the addition of LAI to WAI whereas Dufrêne and Bréda (1995) and Gower et al. (1999) emphasized that this equality is not general because of the overlapping of branches by leaves. Dufrêne and Bréda (1995), using an indirect versus direct method calibration in deciduous stands, concluded that the PAI calculated by the LAI-2000 using the three upper rings (0–43° from zenith) was equivalent to direct LAI measurements from litter without any correction.
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Sampling strategy |
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TopAbstractIntroductionMaterials and methodsCurrent controversial issuesSampling strategyConclusion and issues for...References |
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The sampling strategy used to record LAI is probably as crucial as the technical choices for measurements. Common to all methods and instruments is the question of spatial and temporal relevance of LAI measurements. Parameters like canopy height and vertical stratification, plot dimensions, site topography, spatial integration of sensors, and canopy continuity (close, randomly dispersed or regular geometric designs, isolated plant) are of importance for the procedure used and for the reliability of the result. The timing of sampling is related to the natural or incidental seasonal time-course of LAI. For both spatial and temporal sampling strategies, advice can be given about field measurement design and the choice of algorithms to compute LAI.
For litter collection, the number of traps and their arrangement are basic questions for sampling. Traps of various dimensions and shapes may be used (square or circular, ranging from 0.18–1 m2), with drainage holes to slow down leaf decomposition. Depending on leaf size, a large number of small traps should be preferred to a few large traps (Aussenac, 1969; McShane et al., 1983; Morrison, 1991). Several sampling strategies (number and size of traps) have been reported (Burton et al., 1991; Vose et al., 1995), but traps are commonly distributed according to a systematic network. Recently 32 protocols used in the EC for forest ecosystem monitoring were gathered and analysed (Bréda and Landmann, 2001). The litter-fall collecting designs involved from 20 to 40 collectors (33 traps ha–1 on average) with a collecting area ranging from 0.25–1 m2 (0.45 m2 on average). In that study, the sampling rate (cumulative sampling area divided by plot area) ranged from less than 0.1% up to 2%. Specific designs should be adopted for sloping sites (Welbourn et al., 1981) or mixed stands (Ferrari and Sugita, 1996).
Before defining a sampling design for both hemispherical photography and indirect methods, the minimum plot area (for both below- and above- canopy measurements) has to be evaluated using geometric calculation, taking into account canopy height, sensor angle of view, and distance to the edge of the stand (Chason et al., 1991; Nackaerts et al., 2000). Specific designs and precautions should be adopted for slope, such as holding the sensor parallel to the slope); computation procedures for LAI with fish-eye sensors have been specifically proposed for sloping sites (Walter and Torquebiau, 2000). The spatial variability of the canopy structure in communities then has to be assessed and allowed for in the measurement design to minimize the impact of clumping. For close or randomly dispersed canopies, a systematic or random distribution of below-canopy measurements is suitable. In the case of regular geometric designs (row crops or tree lines in plantations), below-canopy measurements have to be distributed along diagonals between rows. Special attention has to be paid to the compass orientation of rows and to significant gaps in the structure. In the case of discontinuous and heterogeneous canopies, such as forests with open areas, row crops before canopy closure or sparse canopies that never close, the previously mentioned underestimation of PAI as compared with direct LAI is especially large. Lang et al. (1985) evaluated the effect of plantation lines on indirect LAI measurement and suggested that logarithmic averaging would be more suitable for inverting the gap fraction (Lang, 1986). Lang and Yueqin (1986) finally proposed a procedure for averaging logarithms of transmittance in order to accommodate gaps in the canopy. Levy and Jarvis (1999) confirmed the performance of Lang and Yueqin’s (1986) algorithm in minimizing the effect of clumping in sparse and highly clumped canopies of millet row crops. Finally, it may be suggested that the developed algorithms for calculating clumping (Chen et al., 1997; Kucharik et al., 1997) could be applied to individual PAR measurements along the linear path of AccuPAR or SunScan instruments; this should also be expanded to the DEMON if individual scans instead of averages could be logged. The incorporation of clumping algorithms may improve the performance of these instruments.
Other sampling difficulties arise for LAI measurement on single trees, shrubs, dwarf shrubs and herbs. For individual trees growing singly or in groups, Norman and Welles (1983) proposed to replace the term LAI/cos() with (ds), where d is the leaf area density within the crown and s is the distance through the tree crown along which the beam passes. According to Whitehead et al. (1990) for individual tree crowns:
k is the fraction of leaf area that is projected on a plane normal to the beam: k equals 0.5 if leaf-angle distribution is assumed to be spherical (Ross, 1981). The leaf area density is the foliage area divided by canopy volume, which requires additional dendrometrical measurements. The leaf density has the dimension of inverse length (m–1).
Direct methods might be preferred (Nowak, 1996) because indirect methods are not suitable for single plants, although LONETREE with Demon equipment (Lang and McMurtrie, 1992) or Hemiview software propose specific options for LAI measurements on a single tree. The LAI-2000 user’s manual also suggests how to measure the LAI of an isolated plant (Li-Cor, 1992). In the case of direct methods for single plants, total plant harvesting is the most efficient strategy, because sub-sampling of leaves within the crown requires care, for example, specific leaf area changes between sun-exposed and shaded leaves or according to orientation within the canopy (Cermak, 1998).
Seasonality has temporal implications for the sampling strategy. First, all radiation-based methods are influenced by the seasonal time-course of solar elevation and diffuse versus direct radiation ratio. As an example, the derivation of a seasonal time-course of LAI from radiation interception monitoring requires that only days with similar direct/diffuse proportion of radiation be selected, following Spitters et al. (1986). Moreover, measurements at the beginning and the end of each day are best eliminated, as the transmittance changes as a result of larger fractions of diffuse radiation. Moreover, LAI itself exhibits a seasonal progression, especially from WAI to PAI for deciduous species, but also in coniferous stands and tropical forests (Wirth et al., 2001): expansion of new leaves is not necessarily concomitant with the fall of older ones. In mixed deciduous and coniferous stands, winter measurement leads to a complex PAI including LAI of coniferous trees and WAI of deciduous ones. Another seasonal example is the canopy closure in row crops, with the regular geometric design progressively disappearing as the canopy develops. As a consequence of seasonality, extinction coefficient (Cannell et al., 1989; Norman and Campbell, 1989; Smith et al., 1991; Berbigier and Bonnefond, 1995), PAI versus LAI ratio (Dufrêne and Bréda, 1995; Gower et al., 1999), and clumping change with the season.
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Conclusion and issues for the future |
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TopAbstractIntroductionMaterials and methodsCurrent controversial issuesSampling strategyConclusion and issues for...References |
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This literature review suggests that there are complementary approaches to describe the interaction of light with the canopy and the leaf area index measurement is only one of several canopy descriptions. Leaf area index may be measured either directly or by one of the indirect methods. Both categories of methods are complementary as calibration is still necessary for indirect methods. Recent research has attempted to improve LAI estimates through a better description and sampling of canopy heterogeneity (vertical and horizontal heterogeneity, clumping, canopy closure or gaps and so on). New instruments or algorithms still need to be developed to aim at converting PAI into LAI properly. It is difficult to make any generalizations, as each worker has to select the most appropriate technique for their own situation, bearing in mind the physiological process of interest. Sampling is often crucial as spatial variability in canopies is large, and replicates at several locations should always be used to determine LAI. For instance, the technical options are quite different if one is interested in leaf area index than if a detailed assessment of canopy geometry is also required. Finally, the main challenging point to improve LAI measurement should be to identify clearly the causes of its variation. The determination of LAI variation is an exciting topic still largely undocumented: to what extent does an individual leaf area change from one site to the other or from one year to the next? Did LAI fluctuation result from changes in leaf size, leaf number or both? Are there any spatial or temporal changes in leaf inclination and clumping? What is the contribution of vertical canopy structure to LAI variation? The answers to these questions will probably occur in the near future and contribute to new thinking on LAI measurements.
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Acknowledgements |
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I gratefully acknowledge the help provided by the staff of all the companies dealing with the area of leaf area index measurements. I have no commercial link with any of them and the views expressed arise wholly from my own experience and reading. I also greatly appreciated working with Kamel Soudani in a previous French review about LAI.
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Albrekston A. 1984. Sapwood basal area and needle mass of Scots Pine (Pinus sylvestris L.) trees in Central Sweden. Forestry 57, 35–43.
Anderson MC. 1964. Studies of the woodland light climate. I. The photographic computation of light conditions. Journal of Ecology 52, 27–41.[CrossRef]
Anderson MC. 1971. Radiation and crop structure. In: Sestak Z, Catsky J, Jarvis PG, eds. Plant photosynthetic production: manual of methods. The Hague, The Netherlands: Junk.
Aussenac G. 1969. Production de litière dans divers peuplements forestiers de l’Est de la France. Oecologia Plantarum 4, 225–236.
Barclay HJ. 1998. Conversion of total leaf area to projected leaf area in lodgepole and Douglas-fir. Tree Physiology 18, 185–193.[Abstract]
Barclay HJ, Trofymow JA. 2000. Relationship of readings from the LI-Cor canopy analyser to total one-sided leaf area index and stand structure in immature Douglas-fir. Agricultural and Forest Meteorology 132, 121–126.
Barclay HJ, Trofymow JA, Leach RI. 2000. Assessing bias from boles in calculating leaf area index in immature Douglas-fir with the Li-Cor canopy analyser. Agricultural and Forest Meteorology 100, 255–260.[CrossRef][Web of Science]
Barnsley MJ, Hobson PD, Hyman AH, Lucht W, Muller JP, Strahler AH. 2000. Characterizing the spatial variability of broadband Albedo in a semi-desert environment for MODIS validation. Remote Sensing of Environment 74, 58–68.[CrossRef][Web of Science]
Berbigier P, Bonnefond JM. 1995. Measurements and modelling of radiation transmission within a stand of maritime pine (Pinus pinaster Ait). Annales des Sciences Forestières 52, 23–42.[CrossRef][Web of Science]
Becker M. 1971. Une technique nouvelle d’utilization des photographies hémisphériques pour la mesure du climat lumineux en forêt. Annales des Sciences Forestières 28, 425–442.
Bidlake WR, Black RA. 1989. Vertical distribution of leaf area in Larix occidentalis: a comparison of two estimation methods. Canadian Journal of Forest Research 19, 1131–1136.[CrossRef]
Bonhomme R. 1970. Application de la technique des photographies hémisphériques in situ à la mesure de l’indice foliaire. In: Techniques d’étude des facteurs physiques de la biosphère. Paris: INRA, 501–505.
Bonhomme R. 1993. The solar radiation: characterization and distribution in the canopy. In: Varlet-Grancher C, Bonhomme R, Sinoquet H, eds. Crop structure and light microclimate: characterization and applications, Sciences Update. Paris: INRA Editions, 17–28.
Bonhomme R, Varlet-Grancher C, Chartier M. 1974. The use of hemispherical photographs for determining the leaf area index of young crops. Photosynthetica 8, 299–301.[Web of Science]
Bouriaud O, Soudani K, Bréda N. 2003. Leaf area index from litter collection: impact of specific leaf area variability within a beech stand. Canadian Journal of Remote Sensing (in press).
Bréda N, Granier A. 1996. Intra- and interannual variations of transpiration, leaf area index and radial growth of a sessile oak stand (Quercus petraea). Annales des Sciences Forestières 53, 521–536.[CrossRef][Web of Science]
Bréda N, Granier A, Aussenac G. 1995. Effects of thinning on soil and tree water relations, transpiration and growth in an oak forest (Quercus petraea (Matt.) Liebl.). Tree Physiology 15, 295–306.[Web of Science][Medline]
Bréda N, Landmann G. 2001. Report on the ad hoc expert group meeting on litter fall assessment. 4–6 March, Fontainebleau, France. Report from International co-operative programme on assessment and monitoring of air pollution effects on forest (ICP Forests) and European scheme on the protection of forests against atmospheric pollution.
Bréda N, Soudani K, Bergonzini JC. 2002. Mesure de l’indice foliaire en forêt. Paris: ECOFOR.
Brenner AJ, Cueto Romero M, Garcia Haro J, Gilabert MA, Incoll LD, Martinez Fernandez J, Porter E, Pugnaire FI, Younis MT. 1995. A comparison of direct and indirect methods for measuring leaf and surface areas of individual bushes. Plant, Cell and Environment 18, 1332–1340.[CrossRef]
Brix H, Mitchell AK. 1983. Thinning and nitrogen fertilization effects on sapwood development and relationships of foliage quantity to sapwood area and basal area in Douglas-fir. Canadian Journal of Forest Research 13, 384–389.
Burton AJ, Pregitzer KS, Reed DD. 1991. Leaf area and foliar biomass relationships in northern hardwood forests located along an 800 km acid deposition gradient. Forest Science 37, 1041–1059.[Web of Science]
Campbell GS. 1986. Extinction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution. Agricultural and Forest Meteorology 36, 317–321.[CrossRef][Web of Science]
Cannell MGR, Milne LJ, Sheppard LJ, Unsworth MH. 1989. Radiation interception and productivity of willow. Journal of Applied Ecology 24, 261–278.
Cermak J. 1998. Leaf distribution in large trees and stands of the floodplain forest in southern Moravia. Tree Physiology 18, 727–737.[Web of Science][Medline]
Chason J, Baldocchi D, Hutson M. 1991. A comparison of direct and indirect methods for estimating forest leaf area. Agricultural and Forest Meteorology 57, 107–128.[CrossRef][Web of Science]
Chen JM. 1996. Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands. Agricultural and Forest Meteorology 80, 135–163.[CrossRef][Web of Science]
Chen JM, Black TA, Adams RS. 1991. Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand. Agricultural and Forest Meteorology 56, 129–143.
Chen JM, Cihlar J. 1995. Quantifying the effect of canopy architecture on optical measurements of leaf area index using two gap size analysis methods. IEEE Transactions on Geosciences and Remote Sensing 33, 777–787.[CrossRef]
Chen JM, Rich PM, Gower TS, Norman JM, Pulmmer S. 1997. Leaf area index on boreal forests: theory, techniques and measurements. Journal of Geophysics Research 102, 429–444.
Chen JM, Pavlic G, Brown L, Cihlar J, et al. 2002. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing of Environment 80, 165–184.[CrossRef][Web of Science]
Comeau P, Gendron F, Letchford T. 1998. A comparison of several methods for estimating light under a paper birch mixedwood stand. Canadian Journal of Forest Research 28, 1843–1850.[CrossRef]
Cutini A, Matteucci G, Mugnozza GS. 1998. Estimating of leaf area index with the Li-Cor LAI 2000 in deciduous forests. Forest Ecology and Management 105, 55–65.[CrossRef][Web of Science]
Deblonde G, Penner M, Royer A. 1994. Measuring leaf area index with Li-Cor LAI-2000 in pine stand. Ecology 75, 527–1511.
Decagon. 2001. AccuPAR, Linear PAR/LAI ceptometer. Operator’s Manual, Version 3.4. Decagon Devices, Inc.
Ducrey M. 1975a. Utilization des photographies hémisphériques pour le calcul de la perméabilité des couverts forestiers au rayonnement solaire. I. Analyse technique de l’interception. Annales des Sciences Forestières 32, 73–92.
Ducrey M. 1975b. Utilization des photographies hémisphériques pour le calcul de la perméabilité des couverts forestiers au rayonnement solaire. II. Etude expérimentale. Annales des Sciences Forestières 32, 205–221.
Dufrêne E, Bréda N. 1995. Estimation of deciduous forest leaf area index using direct and indirect methods. Oecologia 104, 156–162.[CrossRef][Web of Science]
Fassnacht KS, Gower ST, Norman JM, McMurtrie RE. 1994. A comparison of optical and direct methods for estimating foliage surface area index in forests. Agricultural and Forest Meteorology 71, 183–207.[CrossRef][Web of Science]
Ferrari JB, Sugita S. 1996. A spatially explicit model of leaf litter fall in hemlock-hardwood forests. Canadian Journal of Forest Research 26, 1905–1913.[CrossRef]
Fournier RA, Rich PM, Landry R. 1997. Hierarchical characterization of canopy architecture for boreal forest. Journal of Geophysical Research 102, 445–454.[CrossRef]
Frazer GW, Fournier RA, Trofymow JA, Hall RJ. 2001. A comparison of digital and film fisheye photography for analysis of forest canopy structure and gap light transmission. Agricultural and Forest Meteorology 109, 249–263.[CrossRef][Web of Science]
Gardingen PR, Jackson GE, Hernandez-Daumas S, Russell G, Sharp L. 1999. Leaf area index estimates obtained for clumped canopies using hemispherical photography. Agricultural and Forest Meteorology 94, 243–257.[CrossRef][Web of Science]
Gower ST, Kucharik CJ, Norman JM. 1999. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sensing of Environment 70, 29–51.[CrossRef][Web of Science]
Gower ST, Norman JM. 1991. Rapid estimation of leaf area index in conifer and broad leaf plantations. Ecology 72, 1896–1900.[CrossRef][Web of Science]
Grace J. 1987. Theoretical ratio between ‘one-sided’ and total surface area for pine needles. Forest Science 17, 292–296.
Grier CC, Waring RH. 1974. Conifer foliage mass related to sapwood area. Forest Science 20, 205–206.
Hagihara A, Yamaji K. 1993. Interception of photosynthetic photon flux density by woody elements in a hinoki (Chamaecyparis obtusa (Sieb. et Zucc.) Endl.) stand. Ecological Research 8, 313–318.
Hanan N, Bégué A. 1995. A method to estimate instantaneous and daily intercepted photosynthetically active radiation using a hemispherical sensor. Agricultural and Forest Meteorology 74, 155–168.[CrossRef][Web of Science]
Hassika P, Berbigier P, Bonnefond JM. 1997. Measurements and modelling of the photosynthetically active radiation transmitted in a canopy of maritime pine. Annales des Sciences Forestières 54, 715–730.[CrossRef][Web of Science]
Hicks S, Lascano R. 1995. Estimation of leaf area Index for cotton canopies using the Li-Cor LAI 2000 plant canopy analyser. Agronomy Journal 87, 458–464.
Jarvis PG, Leverenz JW. 1983. Productivity of temperate, deciduous and evergreen forests. In: Lange OL et al., eds. Physiological plant ecology. IV. Encyclopedia of plant physiology, Vol. 12D. New York: Springer-Verlag, 233–280.
Johansson T. 1989. Irradiance within the canopies of young trees of European aspen (Populus tremula) and European birch in stands of different spacings. Forest Ecology and Management 28, 217–236.
Jones HG. 1992. Plant and microclimate, 2nd edn. Cambridge, UK: Cambridge University Press.
Jurick TW. 1986. Temporal and spatial patterns of specific leaf weight in successional northern hardwood tree species. American Journal of Botany 78, 1083–1092.
Kucharik CJ, Norman JM, Gower ST. 1998a. Measurements of branch and adjusting indirect leaf area index measurements. Agricultural and Forest Meteorology 91, 69–88.[CrossRef][Web of Science]
Kucharik CJ, Norman JM, Gower ST. 1998b. Measurements of leaf orientation, light distribution and sunlit leaf area in a boreal aspen forest. Agricultural and Forest Meteorology 91, 127–148.[CrossRef][Web of Science]
Kucharik CJ, Norman JM, Murdock LM, Gower TS. 1997. Characterizing canopy nonrandomness with a Multiband Vegetation Imager MVI. Journal of Geophysical Research 102, 455–473.
Kucharik JC, Norman JM, Gower ST. 1999. Characterization of radiation regimes in non-random forest canopies: theory, measurements, and a simplified modelling approach. Tree Physiology 19, 695–706.[Abstract]
Küßner R, Mosandl R. 2000. Comparison of direct and indirect estimation of leaf area index in mature Norway spruce stands of eastern Germany. Canadian Journal of Forest Research 30, 440–447.[CrossRef]
Lang ARG. 1986. Leaf area and average leaf angle from transmittance of direct sunlight. Australian Journal of Botany 34, 349–355.[CrossRef]
Lang ARG. 1987. Simplified estimate of leaf area index from transmittance of the sun’s beam. Agricultural and Forest Meteorology 41, 179–186[CrossRef][Web of Science]
Lang ARG. 1990. An instrument for measuring canopy structure. Remote Sensing Reviews 5, 61–71.
Lang AGR. 1991. Application of some of Cauchy’s theorems to estimation of surface areas of leaves, needles and branches of plants, and light transmittance. Agricultural and Forest Meteorology 55, 191–212.[CrossRef][Web of Science]
Lang ARG, McMurtrie RE. 1992. Total leaf areas of single trees of Eucalyptus grandis estimated form transmittances of the sun’s beam. Agricultural and Forest Meteorology 58, 79–92.[CrossRef][Web of Science]
Lang ARG, McMurtrie RE, Benson ML. 1991. Validity of surface area indices of Pinus radiata estimated from transmittance of sun’s beam. Agricultural and Forest Meteorology 57, 157–170.[CrossRef][Web of Science]
Lang ARG, Yueqin X. 1986. Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies. Agricultural and Forest Meteorology 37, 229–243.[CrossRef][Web of Science]
Lang ARG, Yueqin X, Norman JM. 1985. Crop structure and the penetration of direct sunlight. Agricultural and Forest Meteorology 35, 83–101.
Le Dantec V, Dufrêne E, Saugier B. 2000. Interannual and spatial variation in maximum leaf area index of temperate deciduous stands. Forest Ecology and Management 134, 71–81.[CrossRef][Web of Science]
Ledent JF. 1977. Sur le calcul du coefficient d’extinction du rayonnement solaire incident direct dans un couvert végétal. Oecologia Plantarum 12, 291–300.[Web of Science]
Levy PE, Jarvis PG. 1999. Direct and indirect measurements of LAI in millet and fallow vegetation in HAPEX-Sahel. Agricultural and Forest Meteorology 97, 199–212.[CrossRef][Web of Science]
Li-Cor. 1992. LAI-2000 plant canopy analyser. Operating manual. Li-Cor.
Maguire DA, Bennett WS. 1996. Patterns in vertical distribution of foliage in young coastal Douglas-Fir. Canadian Journal of Forest Research 26, 1991–2005.[CrossRef]
Makela A, Virtanen K, Nikinmaa E. 1995. The effects of ring width, stem position and density on the relationship between foliage biomass and sapwood area in Scots Pine (Pinus sylvestris L.). Canadian Journal of Forest Research 25, 970–977.[CrossRef]
McShane MC, Carlile DW, Hinds WT. 1983. The effect of collector size on forest litter-fall collection and analysis. Canadian Journal of Forest Research 13, 1037–1042.[CrossRef]
Miller JB. 1967. A formula for average foliage density. Australian Journal of Botany 15, 141–144.[CrossRef][Web of Science]
Monsi M, Saeki T. 1953. Über den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung für die Stoffproduktion. Japanese Journal of Botany 14, 22–52.
Morrison IK. 1991. Effect of trap dimensions on mass of litter fall collected in an Acer saccharum stand in northern Ontario. Canadian Journal of Forest Research 21, 939–941.[CrossRef]
Nackaerts K, Coppin P, Muys B, Hermy M. 2000. Sampling methodology for LAI measurements with LAI-2000 in small forest stands. Agricultural and Forest Meteorology 101, 247–250.[CrossRef][Web of Science]
Nel EM, Wessman CA. 1993. Canopy transmittance models for estimating forest leaf area index. Canadian Journal of Forest Research 23, 2579–2586.[CrossRef]
Neumann HH, Den Hartog G, Shaw RH. 1989. Leaf area measurements based on hemispheric photographs and leaf-litter collection in deciduous forest during autumn leaf fall. Agricultural and Forest Meteorology 45, 325–345.[CrossRef][Web of Science]
Niinemets U, Kull K. 1994. Leaf weight per area and leaf size of 85 Estonian woody species in relation to shade tolerance and light availability. Forest Ecology and Management 70, 1–10.[CrossRef][Web of Science]
Nilson T. 1971. A theoretical analysis of the frequency of gaps in plant stands. Agricultural Meteorology 8, 25–38.[CrossRef][Web of Science]
Nizinski JJ, Saugier B. 1988. A model of leaf budding and development for a mature Quercus forest. Journal of Applied Ecology 25, 643–655.[CrossRef][Web of Science]
Norman JM, Campbell GS. 1989. Canopy structure. In: Pearcy RW, Ehleringer JR, Mooney HA, Rundel PW, eds. Plant physiological ecology: field methods and instrumentation. London: Chapman and Hall, 301–325.
Norman JM, Jarvis PG. 1974. Photosynthesis in Sitka Spruce (Picea sitchensis (Bong.) Carr.) III. Measurements of canopy structure and interception of radiation. Journal of Applied Ecology 11, 375–398.[CrossRef][Web of Science]
Norman JM, Welles JM. 1983. Radiative transfer in an array of canopies. Agronomy Journal 75, 481–488.
Nowak DJ. 1996. Estimating lea area and leaf biomass of open-grown deciduous urban trees. Forest Science 42, 504–507.[Web of Science]
Pierce LL, Running SW. 1988. Rapid estimation of coniferous forest leaf area using a portable integrating radiometer. Ecology 67, 1762–1767.[CrossRef]
Potter E, Wood J, Nicholl C. 1996. SunScan Canopy Analysis System. User Manual, SS1-UM-1.05. Delta-T Devices Ltd.
Rich PM. 1990. Characterizing plant canopies with hemispherical photographs. Remote Sensing Reviews 5, 13–29.
Rogers R, Hinckley TM. 1979. Foliar weight and area related to current sapwood area in oak. Forest Science 25, 298–303.[Web of Science]
Ross J. 1981. The radiation regime and architecture of plant stands. The Hague, The Netherlands: Dr Junk W.
Sellin A. 2000. Estimating the needle area from geometric measurements: application of different calculation methods to Norway spruce. Trees 14, 215–222.[CrossRef]
Shelbrune VB, Hedden RL, Allen RM. 1993. The effects of site, density and sapwood permeability on the relationship between leaf area and sapwood area in loblolly pine (Pinus taeda L.). Forest Ecology and Management 58, 193–209.[CrossRef][Web of Science]
Smith FW, Sampson AD, Long NJ. 1991. Comparison of leaf area index estimates from tree allometrics and measured light interception. Forest Science 37, 1682–1688.[Web of Science]
Smith NJ. 1993. Estimating plant area index and light extinction coefficients in stands of Douglas-fir (Pseudotsuga Menziesii). Canadian Journal of Forest Research 23, 317–321.[CrossRef]
Smith NJ, Chen JM, Black TA. 1993. Effects of clumping on estimates of stand leaf area index using the Li-Cor LAI-2000. Canadian Journal of Forest Research 23, 1940–1943.[CrossRef]
Soudani K, Trautmann J, Walter JMN. 2002. Leaf area index and canopy stratification in Scots pine (Pinus sylvestris L.) stands. International Journal of Remote Sensing 23, 3605–3618.[CrossRef][Web of Science]
Spitters CJT, Toussaint HAJM, Goudriaan J. 1986. Separating the diffuse and direct component of global radiation and its implications for modelling canopy photosynthesis. Part I. Component of incoming radiation. Agricultural and Forest Meteorology 38, 217–229.[CrossRef][Web of Science]
Stenberg P, Linder S, Smolander H, Flower-Ellis J. 1994. Performances of the LAI-2000 plant canopy analyser in estimating leaf area index of some Scots pine stands. Tree Physiology 14, 981–995.[Web of Science][Medline]
Thomas SC, Winner WE. 2000. Leaf area Index of an old-growth Douglas-fir forest estimated from direct structural measurements in the canopy. Canadian Journal of Forest Research 23, 1922–1930.[CrossRef]
Turton SM. 1985. The relative distribution of photosynthetic active radiation within four tree canopies, Cragieburn Range, New Zealand. Australian Forest Research 15, 383–394.[Web of Science]
Vanseveren JP, Herbauts J. 1977. Index foliaire, paramètres foliaires et caractéristiques édaphiques stationnelles dans quelques peuplements forestiers de Lorraine belge. Annales des Sciences Forestières 34, 215–229.[Web of Science]
Vertessy RA, Benyon RG, O’Sullivan SK, Gribben PR. 1995. Relationships between stem diameter, sapwood area, leaf area and transpiration in a young mountain ash forest. Tree Physiology 15, 559–567.[Abstract]
Vose JM, Sullivan NH, Clinton BD, Bolstad PV. 1995. Vertical leaf area distribution, light transmittance, and the application of the Beer–Lambert Law in four mature hardwood stands in the southern Appalachians. Canadian Journal of Forest Research 25, 1036–1043.[CrossRef]
Vose JM, Swank WT. 1990. Assessing seasonal leaf area dynamics and vertical leaf distribution in eastern white pine (Pinus strobus L.) with a portable light meter. Tree Physiology 7, 125–134.[Web of Science][Medline]
Walter JM, Torquebiau E. 1997. The geometry of the canopy of a dipterocarp forest in Sumatra. Agricultural and Forest Meteorology 85, 99–115.[CrossRef][Web of Science]
Walter JM, Torquebiau E. 2000. The computation of forest leaf area index on slope using fish-eye sensors. Compte-Rendu de l‘Académie des Sciences, Paris, Life Sciences 323, 801–813.
Walter JMN, Grégoire-Himmler C. 1996. Spatial heterogeneity of a Scots pine canopy: an assessment by hemispherical photographs. Canadian Journal of Forest Research 26, 1610–1619.[CrossRef]
Waring RH, Schroeder PE, Oren R. 1982. Application of the pipe model theory to predict canopy leaf area. Canadian Journal of Forest Research 12, 556–560.
Warren Wilson J. 1959. Analysis of the spatial distribution of foliage by two-dimensional point quadrats. New Phytologist 58, 92–101.[CrossRef]
Warren Wilson J. 1960. Inclined point quadrats. New Phytologist 59, 1–8.
Warren Wilson J. 1963. Estimation of foliage densness and foliage angle by inclined point quadrats. Australian Journal of Botany 11, 95–105.[CrossRef]
Watson DJ. 1947. Comparative physiological studies in the growth of field crops. I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Annals of Botany 11, 41–76.
Welbourn ML, Stone EL, Lassoie JP. 1981. Distribution of net litter inputs with respect to slope position and wind direction. Forest Science 27, 651–659.[Web of Science]
Welles JM. 1990. Some indirect methods of estimating canopy structure. Remote Sensing Reviews 5, 31–43.
Welles JM, Norman JM. 1991. Instrument for indirect measurement of canopy architecture. Agronomy Journal 83, 818–825.
Whitehead D, Edwards WRN, Jarvis PG. 1984. Conducting sapwood area, foliage area and permeability in mature trees of Picea sitchensis and Pinus contorta. Canadian Journal of Forest Research 14, 940–947.[CrossRef]
Whitehead D, Grace JC, Godrey MJS. 1990. Architectural distribution of foliage in individual Pinus radiata D. Don crowns and the effects of clumping on radiation interception. Tree Physiology 7, 135–155.[Web of Science][Medline]
Wirth R, Weber B., Ryel RJ. 2001. Spatial and temporal variability of canopy structure in a tropical moist forest. Acta Oecologica 22, 235–244.[CrossRef]
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 C. Sone, K. Saito, and K. Futakuchi Comparison of Three Methods for Estimating Leaf Area Index of Upland Rice Cultivars Crop Sci., June 26, 2009; 49(4): 1438 - 1443. [Abstract] [Full Text] [PDF]
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D. L.B. Jupp, D.S. Culvenor, J.L. Lovell, G.J. Newnham, A.H. Strahler, and C.E. Woodcock Estimating forest LAI profiles and structural parameters using a ground-based laser called 'Echidna(R) Tree Physiol, February 1, 2009; 29(2): 171 - 181. [Abstract] [Full Text] [PDF]
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 F. Hosoi and K. Omasa Factors contributing to accuracy in the estimation of the woody canopy leaf area density profile using 3D portable lidar imaging J. Exp. Bot., September 1, 2007; 58(12): 3463 - 3473. [Abstract] [Full Text] [PDF]
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 F. EWERT Modelling Plant Responses to Elevated CO2: How Important is Leaf Area Index? Ann. Bot., June 1, 2004; 93(6): 619 - 627. [Abstract] [Full Text] [PDF]
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