LAI Estimation Research Articles

Comparação de três métodos para estimativa do índice de área foliar em pastos de capim-marandu sob lotação contínua

Objetivou-se comparar três métodos de determinação do IAF em pastos de capim-marandu (Brachiaria brizantha Hochst. ex A. Rich cv. Marandu) mantidos a quatro alturas de pastejo (10, 20, 30, e 40 cm) por meio de lotação contínua com bovinos de corte. Foram comparados um método direto, destrutivo (IAF MD), um método indireto, destrutivo, baseado no uso da densidade populacional de perfilhos e área foliar média por perfilho (IAF DPP), e um método indireto, não destrutivo, baseado no uso de um aparelho analisador de dossel LAI-2000 (LAI LICOR). O IAF MD foi estimado por meio de coletas de amostras de forragem contidas no interior de quatro retângulos de 0,11 m² (0,30 × 0,37 m). As avaliações foram realizadas mensalmente e agrupadas em cinco épocas do ano: verão, outono, inverno, início e final de primavera. O experimento foi realizado segundo um delineamento de blocos completos casualizados, com quatro repetições, e arranjo de parcelas subdivididas, em que as alturas corresponderam às parcelas e os métodos, às subparcelas. Em comparação aos outros dois métodos, o analisador de dossel superestima os valores de IAF, provavelmente em virtude da grande deposição de material morto na base do pasto, principalmente naqueles mantidos mais altos. O IAF MD foi o método que melhor detectou variações entre alturas e épocas do ano e o IAF DPP resultou em valores intermediários, com padrão de evolução nos valores absolutos similar ao registrado para o IAF MD. O analisador de dossel não deve ser utilizado visando coletar dados de IAF para fins de simulação e modelagem matemática ou para estimar a eficiência de uso da radiação incidente.

A generalized allometric equation to predict foliar dry weight on the basis of trunk diameter for eastern white pine ( Pinus strobus L.)

Leaf area index is a critical input into computations and models seeking to estimate or predict a whole host of ecophysiological processes, including carbon sequestration and evapotranspirational losses from forest ecosystems. The aim of this study was to derive and validate a generalized allometric equation for eastern white pine ( Pinus strobus L.) that would estimate and predict foliar biomass on the basis of trunk diameter at breast height (dbh, 1.37 m above-ground level). Coupled with locally derived specific leaf areas, this generalized equation would be a useful predictor of LAI for input to ecological models and a useful comparator to LAI estimated by remote sensing techniques or plant canopy analyzers. The generalized allometric equation was developed by using site-specific allometric equations from the four provenances of eastern white pine. Nine eastern white pine trees were destructively sampled over a 9-year period to form the validation data set. The generalized allometric equation derived and validated in this work is: y = 0.004 x 2.308 where y = foliar dry weight (kg) and x = dbh (cm) ( R 2 = 0.967, p < 0.000). There was strong agreement between the derived generalized allometric equation and the validation data set with a mean absolute error of only 2.60 kg. The allometric equation derived and validated in this study may be used with confidence by foresters, forest ecologists, and other plant scientists seeking estimates of foliar biomass or LAI for use in ecological models in natural and planted eastern white pine forests.

Estimation of LAI and above-ground biomass in deciduous forests: Western Ghats of Karnataka, India

This study demonstrates the potentials of IRS P6 LISS-IV high-resolution multispectral sensor (IGFOV∼6m)-based estimation of biomass in the deciduous forests in the Western Ghats of Karnataka, India. Regression equations describing the relationship between IRS P6 LISS-IV data-based vegetation index (NDVI) and field measured leaf area index (ELAI) and estimated above-ground biomass (EAGB) were derived. Remote sensing (RS) data-based leaf area index (PLAI) image is generated using regression equation based on NDVI and ELAI (r2=0.68, p≤0.05). RS-based above-ground biomass (PAGB) image was generated based on regression equation developed between PLAI and EAGB (r2=0.63, p≤0.05). The mean value of estimated above-ground biomass and RS-based above-ground biomass in the study area are 280(±72.5) and 297.6(±55.2)Mgha−1, respectively. The regression models generated in the study between NDVI and LAI; LAI and biomass can also help in generating spatial biomass map using RS data alone. LISS-IV-based estimation of biophysical parameters can also be used for the validation of various coarse resolution satellite products derived from the ground-based measurements alone.

LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm

This article describes the algorithmic principles used to generate LAI, fAPAR and fCover estimates from VEGETATION observations. These biophysical variables are produced globally at 10 days temporal sampling interval under lat–lon projection at 1/112° spatial resolution. After a brief description of the VEGETATION sensors, radiometric calibration process, based on vicarious desertic targets is first presented. The cloud screening algorithm was then fine tuned using a global network of cloudiness observations. Atmospheric correction is then achieved using the SMAC code with inputs coming from meteorological values of pressure, ozone and water vapour. Aerosol optical thickness is derived from MODIS climatology assuming continental aerosol type. The Roujean BRDF model is then adjusted for red, near infrared and short wave infrared bands used to the remaining cloud free observations collected over a time window of ± 15 days. Outliers due to possible cloud contamination or residual atmospheric correction are iteratively eliminated and prior information is used to get more robust estimates of the three BRDF kernel coefficients. Nadir viewing top of canopy reflectance in the three bands is input to the biophysical algorithm to compute the products at 10 days sampling interval. This algorithm is based on training neural networks over SAIL + PROPSPECT radiative transfer model simulations for each biophysical variable. Details on the way the training data base was generated and the neural network designed and calibrated are presented. Finally, theoretical performances are discussed. Validation over ground measurement data sets and inter-comparison with other similar biophysical products are presented and discussed in a companion paper. The CYCLOPES products and associated detailed documentation are available at http://postel.mediasfrance.org.

Canopy openness and lai estimates in two seasonally deciduous forests on limestone outcrops in central Brazil using hemispherical photographs

Patches of seasonally dry tropical forests occur on limestone outcrops in Central Brazil surrounded by the dominant savanna vegetation. They contain valuable timber species but are threatened by farming and mining activities. The objective of this study was to describe canopy opening and light relations in two seasonally deciduous dry forests on slopes and limestone outcrops, in the Paranã valley at the northeastern region of the Goiás state, Brazil. The studied forests were in the Fazenda Sabonete in Iaciara-Go and Fazenda Forquilha in Guarani-GO. Woody plants were sampled in 25 (20 x 20 m) plots in each forest. In the Sabonete forest 40 species, 705 ind./ha-1 with a basal area of 15.78 m²/ha-1 were found, while in Forquilha there were 55 species, 956 ind./ha-1 with a basal area of 24.76 m²/ha-1. Using hemispherical photographic techniques, 25 black and white photographs were taken at each site, during the dry season, totaling 50 photographs. These were taken at the beginning of each vegetation-sampling plot. The photographs were scanned in grey tones and saved as 'Bitmap'. The canopy opening and leaf area index (LAI) were calculated using the software Winphot. The mean canopy opening was 54.0% (±9.36) for Fazenda Sabonete and 64.6% (±11.8) in Fazenda Forquilha, with both sites presenting significant differences in the opening estimates (P &lt; 0.05). Their floristic richness and structure also differed with the more open canopy forest, Forquilha, being richer and denser, suggesting the need for further studies on species-environment relationships in these forests.

Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS

Climate change is predicted to alter the canopy phenology of temperate and boreal forests, which will affect carbon, water, and energy budgets. Therefore, there is a great need to evaluate remotely sensed products for their potential to accurately capture canopy dynamics. The objective of this study was to compare several products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to field measurements of fraction photosynthetically active radiation (FPAR) and plant area index (PAI) for a deciduous broadleaf forest in northern Wisconsin in 2002. MODIS products captured the general phenological development of the canopy although MODIS products overestimated the leaf area during the overstory leaf out period. Field data suggest that the period from budburst to canopy maturity, or maximum PAI, occurred in 10 to 12 days while MODIS products predicted onset of greenness and maturity from 1 to 21 days and 0 to 19 days earlier than that from field observations, respectively. Temporal compositing of MODIS data and understory development are likely key factors explaining differences with field data. Maximum PAI estimates differed only by 7% between field derived and MODIS-based estimates of LAI. Implications for ecosystem modeling of carbon and water exchange and future research needs are discussed.

Water-nitrogen relationships for wheat growth and productivity in late sown conditions

Field experiments were conducted on a sandy loam soil (Typic Ustochrept) to study the water-nitrogen –yield relationships of late sown wheat under adequate and limited water supply conditions. The treatments comprised of four levels of irrigation (I 0 , no post-sown irrigation; I 1 , one irrigation at CRI stage; I 2 , two irrigations, each at CRI and flowering stages; I 3 , four irrigations each given at CRI, jointing, flowering and dough stages) in main plots and a combination of three N levels (0, 50 and 100 kg N/ha) and two levels of zinc as ZnSO 4 . 7H 2 O (0 and 5 kg Zn/ha) in sub-plots of a split plot design and were replicated three times. Progressive increase in irrigations from 0 to 4 and nitrogen levels from 0 to 100 kg/ha, increased the average LAI, CGR, RGR and NAR, yield attributes, wheat biomass and grain yield significantly (p ≤ 0.05) over the control (I 0 and N 0 ), in both the seasons, respectively. Analyses by multiple regression techniques reveled that LAI estimates with N uptake were much higher at 84% predictability than the estimate based on water use that could account for 75% of the variations only. Lower levels of water consumption by crop were seen to result in less leaf area therefore, resulting in lower biomass and grain yields at lower levels of irrigation. The highest rate of biomass gains of 53.1 kg/ha-mm was obtained during the 60–90 day period, a period that fell within the maximum growth phase of wheat, followed by 28.3 and 6.7 kg/ha-mm during 90-120 and 0-60 days after sowing (DAS) periods. The response of the above ground biomass to nitrogen uptake was higher (76.6 kg/ha-kg N uptake) during 60-90 DAS period than during the 0-60 DAS period (22.1 kg/ha-kg N uptake). Additive production functions therefore, took care of the stage sensitivity along with the complete water and nitrogen stresses as induced through different applications of water and nitrogen. The maximum growth rates in wheat were commensurate with highest levels of water use as well as nitrogen use observed during the 60-90 DAS period of growth. For this reasons, scheduling of water and nitrogen application ought to consider providing relatively less quantities of water during the 0-60 DAS period as compared to the 90-120 and 60-90 DAS periods respectively, in order to provide just sufficient amounts of water so as to enhance the uptake of available nitrogen required for optimum growth and development of wheat. Journal of Agriculture Science and Technology Vol. 7(1) 2005: 1-17

Retrieval of leaf area index for a coniferous forest by inverting a forest reflectance model

The aim of this paper was to serve as a pilot study for running a physically based forest reflectance model through an operational forest management data base in Finnish coniferous forests. The LAI values of 250 boreal coniferous stands were retrieved with the physically based model by inversion from a SPOT HRVIR1 image. The use of three spectral vegetation indices (NDVI, RSR and MSI) in LAI estimation was tested for the same stands. Ground-truth LAI was based on an allometric model which can be applied to routine stand inventory data. Stand reflectances were computed as an average of reflectances of the pixels located within the digital stand borders. The relationships of LAI and spectral vegetation indices calculated from the SPOT data were very scattered. RSR exhibited the widest range of values (and the highest correlation with LAI), suggesting it to be more dynamic than MSI or NDVI. Inversion of the reflectance model was done twice: first using as simultaneous input three wavelength bands (red, NIR and MIR), then only the red and NIR bands. The aim was to observe whether including the MIR band in the inversion would improve the inverted LAI estimates or if using only the red and NIR bands would result in the same reliability of inverted values. The motivation for examining the influence of the MIR band resulted from several recent studies from the boreal zone which suggest that the pronounced understory effect could be minimized by the inclusion of the MIR band. The LAI values inverted by the model were slightly larger than the ground-truth LAI values. A minor improvement in LAI estimates was observed after the inclusion of the MIR band in reflectance model inversion. The errors in the ground-truth LAI were uncertain and the background understory reflectance was expected to be highly variable. Thus, the quality of the data used may be to a large extent responsible for the observed low utility of the tested channels.

Distributed hydrological modelling in California semi-arid shrublands: MIKE SHE model calibration and uncertainty estimation

The Generalized Likelihood Uncertainty Estimation (GLUE) methodology is used for model calibration, testing and predictive uncertainty estimation in the application of the MIKE SHE hydrologic model for estimating monthly streamflow in a semi-arid shrubland (chaparral) catchment in central California. Monte Carlo simulation is used to randomly generate one thousand parameter sets for a 20-year calibration period encompassing variable climatic and wildfire conditions, from which behavioural (acceptable) MIKE SHE parameter sets are identified and 5% and 95% uncertainty bounds for monthly streamflow are calculated. This group of behavioural parameter sets is subsequently used to predict streamflow and to construct uncertainty bounds for a 12-year test period with climatic and fire characteristics different from those of the calibration period. More than two-thirds of the observations in each period fell within the corresponding uncertainty bounds, suggesting a similar level of model performance in the calibration and test periods. Prediction errors (i.e. observations falling outside the uncertainty bounds) were generally associated with large rainfall and wildfire events and are indicative of deficiencies in model structure, uncertainty in input data, and/or errors in observed streamflow. The effect of uncertainty in remote sensing-based LAI model inputs on the uncertainty associated with MIKE SHE streamflow predictions receives special attention in this work due to the fire-prone nature of the study area and the increasing use of remotely sensed LAI estimates in distributed hydrological modelling applications. Results from MIKE SHE simulations for seven LAI input scenarios (including the baseline LAI sequence used in the model calibration and testing phase of this study) indicate that differences in predictive uncertainty between scenarios are usually less than ±10%. This is evidence that the baseline LAI trajectory is generally appropriate for distributed hydrological modelling of chaparral catchments.

Calibration and assessment of seasonal changes in leaf area index of a tropical dry forest in different stages of succession

A simple measure of the amount of foliage present in a forest is leaf area index (LAI; the amount of foliage per unit ground surface area), which can be determined by optical estimation (gap fraction method) with an instrument such as the Li-Cor LAI-2000 Plant Canopy Analyzer. However, optical instruments such as the LAI-2000 cannot directly differentiate between foliage and woody components of the canopy. Studies investigating LAI and its calibration (extracting foliar LAI from optical estimates) in tropical forests are rare. We calibrated optical estimates of LAI from the LAI-2000 with leaf litter data for a tropical dry forest. We also developed a robust method for determining LAI from leaf litter data in a tropical dry forest environment. We found that, depending on the successional stage of the canopy and the season, the LAI-2000 may underestimate LAI by 17% to over 40%. In the dry season, the instrument overestimated LAI by the contribution of the woody area index. Examination of the seasonal variation in LAI for three successional stages in a tropical dry forest indicated differences in timing of leaf fall according to successional stage and functional group (i.e., lianas and trees). We conclude that when calculating LAI from optical estimates, it is necessary to account for the differences between values obtained from optical and semi-direct techniques. In addition, to calculate LAI from litter collected in traps, specific leaf area must be calculated for each species rather than from a mean value for multiple species.

Elevated CO2 concentration, fertilization and their interaction: growth stimulation in a short-rotation poplar coppice (EUROFACE)

We investigated the individual and combined effects of elevated CO2 concentration and fertilization on aboveground growth of three poplar species (Populus alba L. Clone 2AS-11, P. nigra L. Clone Jean Pourtet and P. x euramericana Clone I-214) growing in a short-rotation coppice culture for two growing seasons after coppicing. Free-air carbon dioxide enrichment (FACE) stimulated the number of shoots per stool, leaf area index measured with a fish-eye-type plant canopy analyzer (LAIoptical), and annual leaf production, but did not affect dominant shoot height or canopy productivity index. Comparison of LAIoptical with LAI estimates from litter collections and from allometric relationships showed considerable differences. The increase in biomass in response to FACE was caused by an initial stimulation of absolute and relative growth rates, which disappeared after the first growing season following coppicing. An ontogenetic decline in growth in the FACE treatment, together with strong competition inside the dense plantation, may have caused this decrease. Fertilization did not influence aboveground growth, although some FACE responses were more pronounced in fertilized trees. A species effect was observed for most parameters.

Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations

The potential of radiative transfer modelling and inversion techniques for operational uses is investigated in order to retrieve leaf area index in a poplar plantation. The 1-D bidirectional canopy reflectance model SAIL, coupled with the leaf optical properties model PROSPECT, was inverted with hyperspectral airborne DAIS data by means of an iterative method. The root mean square error in LAI estimation was determined against in situ measurements in order to evaluate the impact of different inversion strategies on the LAI retrieval accuracy. These included the selection of an optimal spectral sampling set, the exploitation of prior knowledge in the inversion process and the use of multiview angle data. We claim that the best configuration is achieved by exploiting multiview DAIS data and prior knowledge information about the model variables (RMSE of 0.39 m 2 m −2). It is also shown that the use of prior knowledge and the selection of a limited number of bands forming the optimal spectral sampling are instrumental in increasing the accuracy of the inversion process. Our analysis confirms the operational potential of model inversion for biophysical parameter retrieval.

Comparison and sensitivity analysis of instruments and radiometric methods for LAI estimation: assessments from a boreal forest site

Retrievals of LAI from inversion of canopy radiometric measurements, using the Li-Cor LAI-2000 Plant Canopy Analyzer and the Decagon AccuPar Ceptometer (a linear quantum probe) were analyzed and compared. Field data were collected from 34 sites in the boreal forest of interior Alaska, and sensitivity tests were conducted to estimate the effect of a variety of measurement conditions on the LAI retrievals. We also tested the response of estimated LAI to different values of the theoretical parameters in the retrieval algorithms. Uncertainty in the incident radiation level was magnified by the LAI retrieval, meaning that even small errors in this measurement significantly affected the LAI estimates. Changes in solar zenith angle over long data acquisition times also contributed to the errors. The most important quality control factors for accurate retrieval of LAI from field measurements were the incident radiation and solar zenith angle. A series of sensitivity tests showed that extreme values of leaf angle distribution could change LAI estimates, but our multi-angle measurements produced results consistent with a spherical leaf angle distribution. Alternative methods taken from the literature for post-processing of the data from the two instruments produced similar results for the LAI-2000, but widely different results for the Decagon AccuPar. Retrievals from the two instruments had an overall correlation coefficient r=0.88,( P<0.01). Agreement was considerably better in aspen stands ( r=0.85, P<0.01, N=43) than in spruce ( r=0.56, P<0.05, N=22). Some of the variability was attributed to spatial heterogeneity within stands, particularly sparse spruce canopies. Overall, our results suggest the retrievals were robust, and largely comparable between instruments over a range of measurement conditions, provided variability in measurement conditions was adequately characterized.

Relating aspen defoliation to changes in leaf area derived from field and satellite remote sensing data

Trembling aspen is the most important deciduous species in the North American boreal forest for which methods are needed to consistently map and monitor insect defoliation patterns to assess impacts on aspen health and productivity. The loss of foliage due to insect defoliation results in changes to leaf area index (ΔLAI) that were studied from both field and satellite image data for a large aspen tortrix (Choristoneura conflictana (Wlk.)) outbreak in boreal forest aspen stands in northern Alberta. Field estimates of defoliation for stands undergoing various levels of defoliation severity and both allometric and optical LAI-2000 measurements of leaf area index were collected and related to Landsat enhanced thematic mapper plus (ETM+) image data representing before- and after-defoliation events. This study was undertaken as part of the Climate Change Impact on the Productivity and Health of Aspen (CIPHA) research initiative that is taking place in western Canada. Field estimates of ΔLAI followed defoliation trends but with substantial scatter that may be attributed to the use of allometric estimates of predefoliation LAI and the subjective nature of visual defoliation assessment. Change estimates derived from both Landsat ETM+ scenes were statistically correlated (correlation r = 0.84, probability P = 0.001) with percent defoliation. Such an approach may be used in the future to calibrate LAI algorithms to other defoliated areas or to map the temporal intensity of these events as they occur.

Ability for a model to predict crop production variability at the regional scale: an evaluation for sugar beet

The spatial application of crop models for the prediction of plot yields on a regional scale has specific constraints. Firstly, it supposes accurate input into the model from sources concerning the spatial variability of crop performance. Furthermore, it is necessary for the model to be suitable for simulating interactions between the environment, agricultural practices and the crop which explain this variability. The aim of this article is to evaluate a sugar beet growth model, SUCROS, in terms of its ability to predict the spatial variability of yields on the scale of a beet-producing region. The model was adjusted to the agronomic context of northern France by taking into account the effects of moisture stress on leaf senescence and on the partitioning of assimilates. This modification reduced by more than a half the error of LAI and storage organ dry weight estimates. The modified model was then tested on thirty four plots of two beet production region, and we were able to demonstrate that its performance was closely linked to the accuracy of input variables concerning the climate and soil water properties of each plot.

Test of Accuracy of LAI Estimation by LAI-2000 under Artificially Changed Leaf to Wood Area Proportions

The accuracy of LAI-2000 Plant Canopy Analyzer for leaf (LAI) and plant (PAI) area indexes measurements was tested in 20-year-old Norway spruce stand using the reduction of canopy biomass. Needle and branch areas were reduced progressively upward every one meter. Values of effective leaf area index (LAIe), as an uncorrected product of LAI-2000, were compared with directly estimated LAI and PAI values after each reduction step. LAI-2000 underestimates PAI and LAI values according to LAI-2000 rings readings, and varied proportions between leaf and wood areas. The values of LAIc have been increased with decreasing of the view angle of the relevant LAI-2000 rings. Therefore, the underestimation of LAI becomes smaller when the readings near the horizon are masked. More accurate results, for projected LAI (LAIp) calculation, are produced by LAI-2000 when some dense grids of measurement points and the most vertical ring readings (0 -13 °) are used. Correction factor 1.6 is possible to use for unreduced canopy hemi-surface LAI estimation, when the last rings (i.e. 5th and 4th rings, 47 -74 °) are excluded. Correction factor of 1.25 can be used to compute LAIp if the angle readings under 43 °are also masked.

Characterization of radiation regimes in nonrandom forest canopies: theory, measurements, and a simplified modeling approach.

We used field measurements and Monte Carlo simulations of canopy gap-size distribution and gap fraction to examine how beam radiation interacts with clumped boreal forest canopies of aspen (Populus tremuloides Michx.), black spruce (Picea mariana (Mill.) B.S.P.) and jack pine (Pinus banksiana Lamb.). We demonstrate that the Beer-Lambert law can be modified to accommodate transmission of radiation through a clumped forest canopy as a function of path length or sun zenith angle. Multiband Vegetation Imager (MVI) measurements and Monte Carlo simulations showed that values of the zenith element clumping index (Omega(e)(0)) are typically between 0.4 and 0.5 in jack pine and black spruce and 0.65 in aspen. Estimates of LAI obtained from MVI measurements of the canopy gap fraction and adjusted for canopy clumping and branch architecture yielded LAI values of 3.0 in jack pine, 3.3 in aspen, and about 6.0 in black spruce. These LAI estimates were within 10-25% of direct measurements made at the same sites. Data obtained with the MVI, along with numerical simulations, demonstrated that assumptions of random foliage distributions in boreal forests are invalid and could yield erroneous values of LAI measured by indirect techniques and false characterizations of atmosphere-biosphere interactions. Monte Carlo simulations were used to develop a general equation for beam radiation penetration as a function of zenith angle in clumped canopies. The essential measurements included stem spacing, crown diameter, crown depth, and within-crown gap fraction.

Estimation of leaf area index with the Li-Cor LAI 2000 in deciduous forests

The present study has been performed in Italy, in stands of the main broad-leaved forest species. Thinned and unthinned stands of Quercus cerris L. (6.3% of total area covered by deciduous species in Italy), Castanea sativa L., (8.5%) and Fagus sylvatica L. (12.6%) were selected from 15 permanent plots. LAI data have been collected during the 3 years (1993–1995), using both direct (littertraps) and indirect methods (LAI-2000 Plant Canopy Analyzer, PCA, Li-Cor, Lincoln, NE, USA). LAI estimation by litter collection (LAI LT, 3.2–7.6 m 2 m −2) was in the range of values reported for deciduous forest, while the PCA method generally underestimated the LAI (LAI PCA, 1.8–5.8 m 2 m −2). Average underestimation was 26.5%, being similar to other reports. The underestimation was higher in thinned (29.1%±14.7%) than in unthinned (22.2%±12.2%) stands and in stands characterised by a LAI LT>5 m 2 m −2. On the contrary, in stands with LAI LT<5 m 2 m −2, PCA estimates were closer to littertraps ones (−11%±9.6%). On the average, LAI LT was 4.51±0.92 m 2 m −2 and 5.86±0.11 m 2 m −2, for thinned and unthinned stands, respectively. Also PCA was able to estimate this difference, arriving at 3.14±0.70 m 2 m −2 (thinned stands) and 4.47±0.61 m 2 m −2 (unthinned stands). With both methods, the difference between the two stand types was strongly significant. Although LAI PCA values were always below LAI LT, the correlation between the two data sets was linear and significant. When recalculated omitting the reading of the external PCA ring, the correlation between LAI LT and LAI PCA improved, and the underestimation of LAI PCA was within 12%. Woody area index (WAI) was evaluated with the PCA during the leafless period. The instrument was able to show the difference between thinned (0.55±0.09) and unthinned stands (0.80±0.19). Similar to other studies, the subtraction of WAI from LAI PCA values increased the underestimation of LAI LT. The agreement between the two methods for LAI estimation was satisfactory. Nevertheless, the underestimation by the PCA method must be taken into account. Over different years and stands, the variability of underestimation was not marked, pointing to a reliable use of PCA in different conditions, as, for example, the comparison of thinned and unthinned stands and in measuring temporal and spatial variations of LAI. The overlapping of leaves, the presence of gaps within the canopy and light at the horizon level seem to be some of the important variables that influence LAI estimation by the PCA. Further corrections of the data can improve substantially the performance of the PCA and produce reliable LAI estimates even though the collection of direct reference measurements is strongly recommended to know exactly LAI, in order to assess instrument performance in a given site.

Leaf area index and site water balance of loblolly pine ( Pinus taeda L.) across a precipitation gradient in East Texas

Leaf area index (LAI, m 2 m −2) and site water balance were measured in closed-canopy loblolly pine ( Pinus taeda L.) plantations in East Texas. LAI was measured using littertrap collections and the LI-COR LAI-2000 Plant Canopy Analyzer (PCA) and site water balance was determined using a model developed by Thornthwaite and Mather [Thornthwaite, C.W., Mather, J.R., 1957. The water balance. Publications in Climatol. Vol. 8, pp. 1–104.]. The site water balance model uses precipitation, temperature and soil texture and moisture holding capacity to determine the balance of moisture inputs and outputs. The relationship between loblolly pine LAI and soil moisture availability indices (actual evapotranspiration and moisture deficit) was analyzed across a naturally occurring 50-cm precipitation gradient in East Texas. Results indicated no significant relationship between LAI using the PCA estimates and actual evapotranspiration ( r 2=0.062) or moisture deficit ( r 2=0.057). The lack of correlation was attributed to the deviation from historical precipitation patterns during the years just prior to sampling, which largely eliminated the precipitation gradient during the sampling period. One interesting result was the significant relationship found between LAI and nutrient content of the pine foliage. Although no correlation was found with PCA-based LAI, a significant correlation was found between littertrap LAI estimates and foliar concentrations of nitrogen and phosphorus ( r 2=0.863 and 0.873, respectively), suggesting that, given the recent precipitation patterns, site fertility may be a stronger determinant of stand LAI than water availability.

Measurements of branch area and adjusting leaf area index indirect measurements

Estimates of leaf area index obtained with indirect measurement techniques, which are replacing more arduous destructive sampling methods, are frequently questioned due to light interception by woody elements and a non-random distribution of foliage elements. Usually, branches are assumed to be positioned randomly with respect to leaves or shoots in the canopy. However, in this study of boreal forest architecture, branches are shown to be preferentially shaded by other non-woody elements (e.g. shoots or leaves) in both coniferous and deciduous species of the boreal region. A new instrument called a Multiband Vegetation Imager (MVI) is used to capture two-band (Visible, 400–620 nm and Near-Infrared, 720–950 nm) image pairs of contrasting Canadian boreal forest canopies during the BOReal Ecosystem-Atmosphere Study (BOREAS). The spatial relationship of branches and photosynthetically active foliage is studied to estimate the fraction of the effective branch hemi-surface area index ( B e) that is masked by leaves and shoots. We suggest an approach that corrects indirect LAI measurements using the LAI-2000 or a similar instrument by correcting for the following biases: (1) the effective canopy branch hemi-surface area that is not masked by leaves or shoots in the canopy, (2) the amount of stem hemi-surface area beneath crowns, (3) leaf (or shoot) (Ω e( θ)) and branch (Ω b( θ)) non-random spatial distributions in the canopy, and (4) the fraction of maximum LAI resulting from defoliation in the canopy. In boreal aspen, MVI image analysis shows that 95% of the effective branch hemi-surface area is masked by other foliage in the canopy. In jack pine and black spruce forests, 80–90% of the effective branch hemi-surface area is masked by other foliage in the canopy. These estimates suggest the fraction of indirect LAI that consists of branches intercepting light is less than 10%. Therefore, branches generally do not intercept a significant amount of beam radiation in boreal forests, and do not significantly bias indirect LAI measurements. However, stems, which comprise 30–50% of the total woody area in this study, may not be preferentially shaded by leafy foliage. Therefore, stem contribution to indirect LAI estimates measured with the LAI-2000 or a similar instrument cannot be overlooked. MVI estimates of the total branch hemi-surface area index agree to within 10–40% of direct measurements made in similar species; however, the error between indirect and direct measurements may be due largely to difficulties associated with obtaining adequate sampling so that the error may fall within the noise level of measurements.