Range Of Leaf Area Index Research Articles

Quantifying winter wheat evapotranspiration and crop coefficients under sprinkler irrigation using eddy covariance technology in the North China Plain

Sprinkler irrigation has been widely used for winter wheat crops in the North China Plain (NCP) to maintain high crop yield and enhance water use efficiency because water resources are scarce. Determining the accurate crop evapotranspiration (ETc) and crop coefficient (Kc) of winter wheat is critical for optimizing sprinkler irrigation management. In this study, an eddy covariance system was deployed in a sprinkler irrigation field to measure the energy balance components (net radiation Rn, soil heat flux G, sensible heat H, latent heat LE) and calculate ETc and Kc. The wheat growth parameters and yield were also investigated. The results showed that the energy balance closure rate, the rate of daily (LE + H) to (Rn - G), was 0.89, indicating a sufficient energy balance closure. Seasonal total LE flux accounted for 67% of the total available energy (Rn - G). The seasonal ETc values were 434 and 396 mm during the 2019–2020 and 2020–2021 growing seasons, respectively, which are comparable to those (400–470 mm) under surface irrigation. The mean daily ETc was 0.87, 0.46, 3.24, 4.81, and 3.94 mm day−1 in the early growth, overwintering, rapid growth, middle, and late growth stages, respectively. The corresponding Kc values were 0.77, 0.47, 1.09, 1.14 and 0.70. The Kc values were close to those reported in the FAO 56 paper. The Kc is positively related to the leaf area index (LAI), and their relationship was fitted as Kc=0.87+0.14lnLAI(R2=0.96) for an LAI range of 1–7, where ln is a natural logarithm function. The measured wheat grain yield was 9100 and 9857 kg ha−1, and the water productivity (WP) was 2.07 and 2.49 kg m−3 in the two seasons, respectively. Both factors are approximately 30% higher than those (7200 kg ha−1, 1.68 kg m−3) reported by other studies. Considering the high wheat yield and comparable crop ETc, sprinkler irrigation is recommended for application on the NCP. The seasonal ETc of 400–430 mm and the obtained Kc values in this study can be used to make sprinkler irrigation scheduling for winter wheat on the NCP.

Using LiDAR technique and modified Community Land Model for calculating water interception of cherry tree canopy

In precision agriculture, methods for analysing 3D point clouds of plants have been introduced, particularly pointing to the high accuracy of light detection and ranging (LiDAR) laser scanning under field conditions. In the present work, LiDAR-based 3D point clouds of cherry trees (n = 255) were analysed for estimating the leaf area as the main factor for water interception. Canopies were scanned for segmenting leaf area pointing to a high variability of canopy surface. The derived tree-specific data of leaf area index (LAI) were implemented into the Community Land Model (CLM), which takes into account canopy interception processes during rainfall events. During canopy development of perennial trees the LAI increased resulting in increased water interception. Events with low rain fall the interception reached 38–100 % capturing LAI of 0.76 – 2.11 m2/m2, respectively. In high rainfall events, interception varied 10–14 % capturing the same LAI range. An equation for describing the varying effects of rainfall intensity and LAI is proposed. The evapotranspiration and water interception data point to a substantial decrease of effective water supply that varies tree-individually during the season. In commercial fruit production, the proposed method can support precise irrigation management.

A Canopy Transpiration Model Based on Scaling Up Stomatal Conductance and Radiation Interception as Affected by Leaf Area Index

Estimating transpiration as an individual component of canopy evapotranspiration using a theoretical approach is extremely useful as it eliminates the complexity involved in partitioning evapotranspiration. A model to predict transpiration based on radiation intercepted at various levels of canopy leaf area index (LAI) was developed in a controlled environment using a pasture species, tall fescue (Festuca arundinacea var. Demeter). The canopy was assumed to be a composite of two indistinct layers defined as sunlit and shaded; the proportion of which was calculated by utilizing a weighted model (W model). The radiation energy utilized by each layer was calculated from the PAR at the top of the canopy and the fraction of absorbed photosynthetically active radiation (fAPAR) corresponding to the LAI of the sunlit and shaded layers. A relationship between LAI and fAPAR was also established for this specific canopy to aid the calculation of energy interception. Canopy conductance was estimated from scaling up of stomatal conductance measured at the individual leaf level. Other environmental factors that drive transpiration were monitored accordingly for each individual layer. The Penman–Monteith and Jarvis evapotranspiration models were used as the basis to construct a modified transpiration model suitable for controlled environment conditions. Specially, constructed self-watering tubs were used to measure actual transpiration to validate the model output. The model provided good agreement of measured transpiration (actual transpiration = 0.96 × calculated transpiration, R2 = 0.98; p < 0.001) with the predicted values. This was particularly so at lower LAIs. Probable reasons for the discrepancy at higher LAI are explained. Both the predicted and experimental transpiration varied from 0.21 to 0.56 mm h−1 for the range of available LAIs. The physical proportion of the shaded layer exceeded that of the sunlit layer near LAI of 3.0, however, the contribution of the sunlit layer to the total transpiration remains higher throughout the entire growing season.

Does economic optimisation explain LAI and leaf trait distributions across an Amazon soil moisture gradient?

Leaf area index (LAI) underpins terrestrial ecosystem functioning, yet our ability to predict LAI remains limited. Across Amazon forests, mean LAI, LAI seasonal dynamics and leaf traits vary with soil moisture stress. We hypothesise that LAI variation can be predicted via an optimality-based approach, using net canopy C export (NCE, photosynthesis minus the C cost of leaf growth and maintenance) as a fitness proxy. We applied a process-based terrestrial ecosystem model to seven plots across a moisture stress gradient with detailed in situ measurements, to determine nominal plant C budgets. For each plot, we then compared observations and simulations of the nominal (i.e. observed) C budget to simulations of alternative, experimental budgets. Experimental budgets were generated by forcing the model with synthetic LAI timeseries (across a range of mean LAI and LAI seasonality) and different leaf trait combinations (leaf mass per unit area, lifespan, photosynthetic capacity and respiration rate) operating along the leaf economic spectrum. Observed mean LAI and LAI seasonality across the soil moisture stress gradient maximised NCE, and were therefore consistent with optimality-based predictions. Yet, the predictive power of an optimality-based approach was limited due to the asymptotic response of simulated NCE to mean LAI and LAI seasonality. Leaf traits fundamentally shaped the C budget, determining simulated optimal LAI and total NCE. Long-lived leaves with lower maximum photosynthetic capacity maximised simulated NCE under aseasonal high mean LAI, with the reverse found for short-lived leaves and higher maximum photosynthetic capacity. The simulated leaf trait LAI trade-offs were consistent with observed distributions. We suggest that a range of LAI strategies could be equally economically viable at local level, though we note several ecological limitations to this interpretation (e.g. between-plant competition). In addition, we show how leaf trait trade-offs enable divergence in canopy strategies. Our results also allow an assessment of the usefulness of optimality-based approaches in simulating primary tropical forest functioning, evaluated against in situ data.

Structure and parameter uncertainty in centennial projections of forest community structure and carbon cycling.

Secondary forest regrowth shapes community succession and biogeochemistry for decades, including in the Upper Great Lakes region. Vegetation models encapsulate our understanding of forest function, and whether models can reproduce multi-decadal succession patterns is an indication of our ability to predict forest responses to future change. We test the ability of a vegetation model to simulate C cycling and community composition during 100years of forest regrowth following stand-replacing disturbance, asking (a) Which processes and parameters are most important to accurately model Upper Midwest forest succession? (b) What is the relative importance of model structure versus parameter values to these predictions? We ran ensembles of the Ecosystem Demography model v2.2 with different representations of processes important to competition for light. We compared the magnitude of structural and parameter uncertainty and assessed which sub-model-parameter combinations best reproduced observed C fluxes and community composition. On average, our simulations underestimated observed net primary productivity (NPP) and leaf area index (LAI) after 100years and predicted complete dominance by a single plant functional type (PFT). Out of 4,000 simulations, only nine fell within the observed range of both NPP and LAI, but these predicted unrealistically complete dominance by either early hardwood or pine PFTs. A different set of seven simulations were ecologically plausible but under-predicted observed NPP and LAI. Parameter uncertainty was large; NPP and LAI ranged from ~0% to >200% of their mean value, and any PFT could become dominant. The two parameters that contributed most to uncertainty in predicted NPP were plant-soil water conductance and growth respiration, both unobservable empirical coefficients. We conclude that (a) parameter uncertainty is more important than structural uncertainty, at least for ED-2.2 in Upper Midwest forests and (b) simulating both productivity and plant community composition accurately without physically unrealistic parameters remains challenging for demographic vegetation models.

Isotopic and morphologic proxies for reconstructing light environment and leaf function of fossil leaves: a modern calibration in the Daintree Rainforest, Australia.

Within closed-canopy forests, vertical gradients of light and atmospheric CO2 drive variations in leaf carbon isotope ratios, leaf mass per area (LMA), and the micromorphology of leaf epidermal cells. Variations in traits observed in preserved or fossilized leaves could enable inferences of past forest canopy closure and leaf function and thereby habitat of individual taxa. However, as yet no calibration study has examined how isotopic, micro- and macromorphological traits, in combination, reflect position within a modern closed-canopy forest or how these could be applied to the fossil record. Leaves were sampled from throughout the vertical profile of the tropical forest canopy using the 48.5 m crane at the Daintree Rainforest Observatory, Queensland, Australia. Carbon isotope ratios, LMA, petiole metric (i.e., petiole-width2 /leaf area, a proposed proxy for LMA that can be measured from fossil leaves), and leaf micromorphology (i.e., undulation index and cell area) were compared within species across a range of canopy positions, as quantified by leaf area index (LAI). Individually, cell area, δ13 C, and petiole metric all correlated with both LAI and LMA, but the use of a combined model provided significantly greater predictive power. Using the observed relationships with leaf carbon isotope ratio and morphology to estimate the range of LAI in fossil floras can provide a measure of canopy closure in ancient forests. Similarly, estimates of LAI and LMA for individual taxa can provide comparative measures of light environment and growth strategy of fossil taxa from within a flora.

Monitoring of sugar beet growth indicators using wide-dynamic-range vegetation index (WDRVI) derived from UAV multispectral images

The normalized vegetation index (NDVI) is widely used to monitor the spatial, temporal, physiological, and biophysical characteristics of vegetation. However, if the ground biomass is high, the NDVI becomes rapidly saturated. In the leaf area index (LAI) range of 2 to 6, reflectance in the near-infrared band is significantly higher than that in the red band. When reflectance in the near-infrared band exceeds 40%, the contribution of that reflectance to the NDVI is low. Here, we applied a weight coefficient, α (range 0.05 to 0.5), to the near-infrared band, thus developing a wide-dynamic-range vegetation index (WDRVI). We calculated (α * NIR-RED)/(α * NIR + RED) values; these emphasized the Fresh Weight of Beet LAI, the Fresh Weight of Leaves (FWL), and the Fresh Weight of Roots (FWR). We sought a vegetation index that optimally monitored early-stage sugar beet growth. The WDRVI sensitivities using various α coefficients were higher than those of the NDVI in the LAI range of 2 to 6. The determination coefficients (r2 values) of the sugar beet LAI, FWL, and FWT models established using the WDRVI1 were 0.957, 0.950, and 0.963, respectively. Using the WDRVI1 index model to estimate the accuracy of beet growth indicators can improve 1.05% to 5.07%. Use of the WDRVI reduces beet growth indicator saturation when the ground biomass is high, enhancing the accuracy of growth monitoring. This is useful as the sugar beet has huge ground biomass.

Application conditions and impact factors for various vegetation indices in constructing the LAI seasonal trajectory over different vegetation types

Abstract Leaf area index (LAI) is a required input for various ecological and crop models. To investigate the application conditions of various vegetation indices (VIs), especially the VIs constructed by red-edge band (VIRE) for estimating LAI, six VIs derived from Medium Resolution Imaging Spectrometer (MERIS) data were used to construct LAI seasonal trajectory for different vegetation types at 15 sites. The PROSAIL model combined with the Extended Fourier Amplitude Sensitivity Test (EFAST) method was adopted to explore the influences and physical basis of canopy biophysical and non-canopy variables on the construction of LAI seasonal trajectory using VIs. For deciduous forests, the normalized difference vegetation index (NDVI) had the highest sensitivity to LAI when LAI 2. For evergreen forests, there were no obvious differences among the sensitivities of six VIs to LAI when LAI 5. For crops, all the VIs had the similar sensitivities at LAI 3. For all three types of vegetation, the VIRE maintained relatively high sensitivity to LAI over the whole range of LAI, especially at high LAI values. The VIs were most affected by chlorophyll content (Cab) and average leaf inclination angle (ALA); their total contribution was about 85%. However, the influence of ALA on VIRE was relatively weak, implying that the VIRE had the potential to establish a universal model for LAI estimation among different vegetation types. Therefore, the optimal VIs over different ranges of LAI were suggested to estimate LAI. In addition, the VIRE should be a preferred choice for estimating LAI to reduce the simulation errors of seasonal LAI, if the RE band is available.

Black and White-Sky Albedo Values of Snow: In Situ Relationships for AVHRR-Based Estimation Using CLARA-A2 SAL

The relationship of black-sky and white-sky albedo values of snow-covered terrain is studied using empirical measurements of six BSRN sites and the Finnish Meteorological Institute Sodankylä site, where albedo measurements are carried out both in an open area and above coniferous forest. In addition, a forest model was used to provide simulated albedo values to cover a wider leaf area index range. Linear regression formulas for estimating the monthly mean white-sky albedo value on the basis of the monthly statistics of the black-sky albedo were derived separately for open and forested areas. The statistical parameters used were the mean, median, standard deviation, skewness and kurtosis. In addition, the monthly mean solar zenith angle value was used as well. The mean absolute difference between the estimated monthly mean white-sky albedo and the empirical value was 0.027 for open areas and 0.015 for forested areas. The derived formulas were applied to the satellite black-sky albedo product CLARA-A2 SAL to generate white-sky albedo maps. Using the open snow area regression for the sea ice area produced values comparable to measured values both around the Antarctic and in the Arctic sea ice area.

Relationship between Flag Leaf Characteristics and Main Yield Components in Oat (Avena sativa L.)

The objective of this study was to investigate relationships between leaf area index, leaf chlorophyll concentration, yield components and grain yield in oat (Avena sativa L.). Ten oat varieties were analyzed in field conditions regarding those traits. Flag leaf chlorophyll concentration range between 451.51 and 747.79 units of μmol of chlorophyll per m2. Also, leaf area index range between 13.68 to 32.84 cm2. Significant correlation indices were highlighted between yield components and leaf area index, yield/yield components and chlorophyll concentration of flag leaf.

A comparison of different methods for assessing leaf area index in four canopy types

Abstract The agreement of Leaf Area Index (LAI) assessments from three indirect methods, i.e. the LAI–2200 Plant Canopy Analyzer, the SS1 SunScan Canopy Analysis System and Digital Hemispherical Photography (DHP) was evaluated for four canopy types, i.e. a short rotation coppice plantation (SRC) with poplar, a Scots pine stand, a Pedunculate oak stand and a maize field. In the SRC and in the maize field, the indirect measurements were compared with direct measurements (litter fall and harvesting). In the low LAI range (0 to 2) the discrepancies of the SS1 were partly explained by the inability to properly account for clumping and the uncertainty of the ellipsoidal leaf angle distribution parameter. The higher values for SS1 in the medium (2 to 6) to high (6 to 8) ranges might be explained by gap fraction saturation for LAI–2200 and DHP above certain values. Wood area index –understood as the woody light-blocking elements from the canopy with respect to diameter growth– accounted for overestimation by all indirect methods when compared to direct methods in the SRC. The inter-comparison of the three indirect methods in the four canopy types showed a general agreement for all methods in the medium LAI range (2 to 6). LAI–2200 and DHP revealed the best agreement among the indirect methods along the entire range of LAI (0 to 8) in all canopy types. SS1 showed some discrepancies with the LAI–2200 and DHP at low (0 to 2) and high ranges of LAI (6 to 8).

Interannual variability of leaf area index of an evergreen conifer stand was affected by carry-over effects from recent climate conditions

Despite the relevance of leaf area index (LAI) to forest productivity, few studies have focused on the interannual variability of LAI of an evergreen stand and its relationship with stand growth and meteorological factors. We estimated the change in LAI of an evergreen conifer (Chamaecyparis obtusa) stand over 19 years from a dataset using allometric methods. The LAI varied between 7.1 and 8.8 m2 m−2, with a 95% confidence interval of <1.1 m2 m−2 over the 19 years. This LAI range was maintained such that the gradual increase in leaf area (LA) of the largest trees counterbalanced the gradual loss in LA of the smallest trees. Meanwhile, more trees showed a temporary decrease in LA in years with low summer precipitation. The LAI and current-year mean temperature for July and August (TJA) were weakly correlated, whereas the correlation coefficient increased (r = 0.93) when LAI was correlated with the moving average TJA over the previous 6 years, which agreed with the estimated turnover time of canopy foliage. The annual stem biomass growth rate was significantly positively correlated with summer precipitation, but not with LAI. These results will be useful for refining models in studies on forest growth and global climate change.

Adapting the CROPGRO Model to Simulate Alfalfa Growth and Yield

Alfalfa is the main forage legume in crop livestock systems worldwide. There is still a scarcity of perennial crop models for alfalfa simulation. Regrowth and herbage yield depend on reserves, seasonal temperature and daylength. A systematic procedure was followed to develop species and cultivar parameters. CROPGRO‐PFM‐alfalfa is available in the latest DSSAT model version (4.7). Despite alfalfa’s global importance, there is a dearth of crop simulation models available for predicting alfalfa growth and yield with its associated composition. The objectives of this research were to adapt the CSM‐CROPGRO Perennial Forage Model for simulating alfalfa growth and yield and to describe model adaptation for this species. Data from six experimental plots grown under sprinkler irrigation in the Ebro valley (Northeast Spain) were used for model adaptation. Starting with parameters for Bracharia brizantha, the model adaptation was based on values and relationships reported from the literature for cardinal temperatures and dry matter partitioning. A Bayesian optimizer was used to optimize temperature effects on photosynthesis and daylength effects on partitioning and an inverse modeling technique was employed for nitrogen fixation rate and nodule growth. The calibration of alfalfa tissue composition was initiated from soybean composition analogy but was improved with values from alfalfa literature. There was considerable iteration in optimizing parameters for the processes outlined above where comparisons were made to measured data. After adaptation, the Root Mean Square Error and d‐statistic of harvested herbage averaged across 58 harvests (yield range: 990–4617 kg ha−1) were 760 kg ha−1 and 0.75, respectively. In addition, good agreement was observed for Leaf Area Index (LAI) (LAI range: 0.1–6.7) with d‐statistic of 0.71. Simulated belowground mass was within the range of literature values. The results of this study showed that CROPGRO‐PFM‐Alfalfa can be used to simulate alfalfa growth and development. Further testing with more extensive datasets is needed to improve model robustness.

Quantifying the Accuracy of Digital Hemispherical Photography for Leaf Area Index Estimates on Broad-Leaved Tree Species.

Digital hemispherical photography (DHP) has been widely used to estimate leaf area index (LAI) in forestry. Despite the advancement in the processing of hemispherical images with dedicated tools, several steps are still manual and thus easily affected by user’s experience and sensibility. The purpose of this study was to quantify the impact of user’s subjectivity on DHP LAI estimates for broad-leaved woody canopies using the software Can-Eye. Following the ISO 5725 protocol, we quantified the repeatability and reproducibility of the method, thus defining its precision for a wide range of broad-leaved canopies markedly differing for their structure. To get a complete evaluation of the method accuracy, we also quantified its trueness using artificial canopy images with known canopy cover. Moreover, the effect of the segmentation method was analysed. The best results for precision (restrained limits of repeatability and reproducibility) were obtained for high LAI values (>5) with limits corresponding to a variation of 22% in the estimated LAI values. Poorer results were obtained for medium and low LAI values, with a variation of the estimated LAI values that exceeded the 40%. Regardless of the LAI range explored, satisfactory results were achieved for trees in row-structured plantations (limits almost equal to the 30% of the estimated LAI). Satisfactory results were achieved for trueness, regardless of the canopy structure. The paired t-test revealed that the effect of the segmentation method on LAI estimates was significant. Despite a non-negligible user effect, the accuracy metrics for DHP are consistent with those determined for other indirect methods for LAI estimates, confirming the overall reliability of DHP in broad-leaved woody canopies.

Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops

Leaf inclination angle distribution is an important canopy structure characteristic which directly impacts the fraction of the intercepted solar radiation. Together with the leaf area index (LAI) it determines the structure and fractional cover of a homogeneous crop canopy. Unfortunately, this key canopy parameter has usually been ignored when applying common multispectral vegetation indices to the mapping of LAI, although its impact is known from model simulations. Therefore, we measured leaf angles and determined their distribution (quantified using the leaf mean tilt angle, MTA) for six crop species with different structures growing on 162 plots with a broad range of LAI (1.1–5.0) and leaf chlorophyll content (26–94 μg cm−2). Next, we calculated six vegetation indices widely used for LAI monitoring—the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the two band enhanced vegetation index (EVI2), the modified triangular vegetation index (MTVI2), the optimized soil adjusted vegetation index (OSAVI) and the wide dynamic range vegetation index (WDRVI)—from airborne imaging spectroscopy data. We calculated the Spearman’s correlation coefficient R s , a non-parametric statistic chosen because of the non-normal distribution of canopy parameters. All studied indices depended on the LAI ( 0.50 ≤ R s ≤ 0.71 ) , but the dependence on the MTA was of similar magnitude ( − 0.83 ≤ R s ≤ − 0.53 ) with EVI, EVI2, OSAVI and MTVI2 depending more strongly on MTA than on LAI. All studied indices were good proxies ( 0.78 ≤ R s ≤ 0.88 ) for vegetation fractional cover (Fcover) which, for homogeneous crop canopies, is a nonlinear function of LAI and MTA. EVI2 and MTVI2 were the most strongly correlated with Fcover, although the difference to the other studied indices was small. This first study involving a large range of crop structures confirms the results from canopy reflectance simulations and emphasizes the necessity of leaf angle information for the successful mapping of LAI with Earth observation data.

Intercomparison and validation of MODIS and GLASS leaf area index (LAI) products over mountain areas: A case study in southwestern China

The validation study of leaf area index (LAI) products over rugged surfaces not only gives additional insights into data quality of LAI products, but deepens understanding of uncertainties regarding land surface process models depended on LAI data over complex terrain. This study evaluated the performance of MODIS and GLASS LAI products using the intercomparison and direct validation methods over southwestern China. The spatio-temporal consistencies, such as the spatial distributions of LAI products and their statistical relationship as a function of topographic indices, time, and vegetation types, respectively, were investigated through intercomparison between MODIS and GLASS products during the period 2011–2013. The accuracies and change ranges of these two products were evaluated against available LAI reference maps over 10 sampling regions which standed for typical vegetation types and topographic gradients in southwestern China.The results show that GLASS LAI exhibits higher percentage of good quality data (i.e. successful retrievals) and smoother temporal profiles than MODIS LAI. The percentage of successful retrievals for MODIS and GLASS is vulnerable to topographic indices, especially to relief amplitude. Besides, the two products do not capture seasonal dynamics of crop, especially in spring over heterogeneously hilly regions. The yearly mean LAI differences between MODIS and GLASS are within ±0.5 for 64.70% of the total retrieval pixels over southwestern China. The spatial distribution of mean differences and temporal profiles of these two products are inclined to be dominated by vegetation types other than topographic indices. The spatial and temporal consistency of these two products is good over most area of grasses/cereal crops; however, it is poor for evergreen broadleaf forest. MODIS presents more reliable change range of LAI than GLASS through comparison with fine resolution reference maps over most of sampling regions. The accuracies of direct validation are obtained for GLASS LAI (r=0.35, RMSE=1.72, mean bias=−0.71) and MODIS LAI (r=0.49, RMSE=1.75, mean bias=−0.67). GLASS performs similarly to MODIS, but may be marginally inferior to MODIS based on our direct validation results. The validation experience demonstrates the necessity and importance of topographic consideration for LAI estimation over mountain areas. Considerable attention will be paid to the improvements of surface reflectance, retrieval algorithm and land cover types so as to enhance the quality of LAI products in topographically complex terrain.

Do the energy fluxes and surface conductance of boreal coniferous forests in Europe scale with leaf area?

Earth observing systems are now routinely used to infer leaf area index (LAI) given its significance in spatial aggregation of land surface fluxes. Whether LAI is an appropriate scaling parameter for daytime growing season energy budget, surface conductance (Gs ), water- and light-use efficiency and surface-atmosphere coupling of European boreal coniferous forests was explored using eddy-covariance (EC) energy and CO2 fluxes. The observed scaling relations were then explained using a biophysical multilayer soil-vegetation-atmosphere transfer model as well as by a bulk Gs representation. The LAI variations significantly alter radiation regime, within-canopy microclimate, sink/source distributions of CO2 , H2 O and heat, and forest floor fluxes. The contribution of forest floor to ecosystem-scale energy exchange is shown to decrease asymptotically with increased LAI, as expected. Compared with other energy budget components, dry-canopy evapotranspiration (ET) was reasonably 'conservative' over the studied LAI range 0.5-7.0m2 m-2 . Both ET and Gs experienced a minimum in the LAI range 1-2m2 m-2 caused by opposing nonproportional response of stomatally controlled transpiration and 'free' forest floor evaporation to changes in canopy density. The young forests had strongest coupling with the atmosphere while stomatal control of energy partitioning was strongest in relatively sparse (LAI ~2m2 m-2 ) pine stands growing on mineral soils. The data analysis and model results suggest that LAI may be an effective scaling parameter for net radiation and its partitioning but only in sparse stands (LAI <3m2 m-2 ). This finding emphasizes the significance of stand-replacing disturbances on the controls of surface energy exchange. In denser forests, any LAI dependency varies with physiological traits such as light-saturated water-use efficiency. The results suggest that incorporating species traits and site conditions are necessary when LAI is used in upscaling energy exchanges of boreal coniferous forests.

Validation of an efficient visual method for estimating leaf area index in clonal Eucalyptus plantations

Leaf area index (LAI) is a key ecophysiological parameter in forest stands because it characterises the interface between atmospheric processes and plant physiology. Several indirect methods for estimating LAI have been developed. However, these methods have limitations that can affect the estimates. This study aimed to evaluate the accuracy and applicability of a visual method for estimating LAI in clonal Eucalyptus grandis × E. urophylla plantations and to compare it with hemispherical photography, ceptometer and LAI-2000® estimates. Destructive sampling for direct determination of the actual LAI was performed in 22 plots at two geographical locations in Brazil. Actual LAI values were then used to develop a field guide with photographic images representing an LAI range of 1.0–5.0 m2 m−2 (leaf area/ground area). The visual LAI estimation guide was evaluated with 17 observers in the field. The average difference between actual LAI and visual LAI estimation was 12% and the absolute difference between the two methods was less than or equal to 0.5 m2 m−2 in 77% of plots. Pearson’s correlation coefficients were high between actual LAI and hemispherical photographs (0.8), visual estimation (0.93) and LAI-2000® (0.99) and low for the ceptometer (0.18). However, absolute values differed among methods, with the average difference between the actual and estimated LAI of [12]% for visual estimation, 28% for the LAI-2000®, 37% for the ceptometer and −43% for hemispherical photographs. The LAI-2000® and ceptometer overestimated LAI in all plots, whereas hemispherical photographs underestimated the values in all measurements, showing that these methods need calibration to be used. No differences were observed between actual LAI and visual estimates across stand ages of 2–8 years and LAI of 1.5–5.3 m2 m−2 (P > 0.05). The results show that visual estimation of LAI in Eucalyptus stands is a practical method that is unaffected by atmospheric characteristics and can be used on an operational scale.

Transpiration reduction and root distribution functions for a non-crop species Schefflera heptaphylla

Quantifying soil suction induced by plant transpiration is vital for engineers to analyse the performance of geotechnical infrastructure such as landfill covers. Transpiration reduction function (Trf) and root distribution function (Rdf) are the two plant properties that govern root-water uptake ability. These two functions have been quantified for various crop species, but they are sometimes used to study the behaviour of non-crop species, even though these two functions are known to be plant-specific. In this study, specific Trf and Rdf were measured for six replicates of Schefflera heptaphylla that have a range of leaf area index (LAI) from 1.0 to 3.5 in clayey sand with gravel. S. heptaphylla is a non-crop tree species that has been commonly used for ecological restoration in many subtropical regions of the world. Trf of each replicate was obtained by relating normalized transpiration rate with suction. After testing, the root system of each tree individual was imaged to determine the normalized root area index (RAI) profile (i.e., Rdf). The normalized transpiration rate for S. heptaphylla with higher LAI (3 and 3.5) is revealed to have lower tolerance of water stress as their normalized transpiration rate reduced at much lower suctions, as compared to those with lower LAI (i.e., 1–2.5). Only when suction is lower than 50kPa, the measured Trf of S. heptaphylla is similar to some of those presumed in the literature. The measurement of Rdf shows that the maximum amount of roots for S. heptaphylla was at depths of 70–80% of the root depth, in contrast to crops species whose root distribution is typically uniform or linearly decreasing.

LAI Variability as a Habitat Feature Determining Reptile Occurrence: A Case Study in Large Forest Complexes in Eastern Poland

Reptile habitats are described using various indices. The definitions of such indices are crucial, as they are applied to habitat modelling for numerous species on local to continental scales. We examined the Leaf Area Index (LAI) for its value as a tool for determining reptile habitat. During measurements carried out in spring and summer months between 2011 and 2013, LAI values were assessed and surveys were conducted on reptile fauna at 11 survey sites in the Solska Forest and Roztocze National Parks areas in Eastern Poland. In total, six Squamata reptiles occurring in Poland were found. We determined that LAI can be utilized as a reptile habitat index, with reptile species associated with LAI seasonal variability as well as LAI range. Moreover, we found that the higher the LAI median value, the greater the variety of reptile species. These findings are useful for development of spatial models of habitats based on LAI as they point to the importance of its seasonal variation.