Leaf Area Distribution Research Articles

Trait‐based projections of climate change effects on global biome distributions

AbstractAimClimate change will likely modify the global distribution of biomes, but the magnitude of change is debated. Here, we followed a trait‐based, statistical approach to model the influence of climate change on the global distribution of biomes.LocationGlobal.MethodsWe predicted the global distribution of plant community mean specific leaf area (SLA), height and wood density as a function of climate and soil characteristics using an ensemble of statistical models. Then, we predicted the probability of occurrence of biomes as a function of the three traits with a classification model. Finally, we projected changes in plant community mean traits and corresponding changes in biome distributions to 2070 for low (RCP 2.6; +1.2°C) and extreme (RCP 8.5; +3.5°C) future climate change scenarios.ResultsWe estimated that under the low climate change scenario (sub)tropical biomes will expand (forest by 18%–22%, grassland by 9%–14% and xeric shrubland by 5%–8%), whereas tundra and temperate broadleaved and mixed forests contract by 30%–34% and 16%–21%, respectively. Our results also indicate that over 70%–75% of the current distribution of temperate broadleaved and mixed forests and temperate grasslands is projected to shift northwards. These changes become amplified under the extreme climate change scenario in which tundra is projected to lose more than half of its current extent.Main conclusionsOur results indicate considerable imminent alterations in the global distribution of biomes, with possibly major consequences for life on Earth. The level of accuracy of our model given the limited input data and the insights on how trait–environment relationships can influence biome distributions suggest that trait‐based correlative approaches are a promising tool to forecast vegetation change and to provide an independent, complementary line of evidence next to process‐based vegetation models.

Forest stand and canopy development unaltered by 12years of CO2 enrichment.

Canopy structure—the size and distribution of tree crowns and the spatial and temporal distribution of leaves within them—exerts dominant control over primary productivity, transpiration and energy exchange. Stand structure—the spatial arrangement of trees in the forest (height, basal area and spacing)—has a strong influence on forest growth, allocation and resource use. Forest response to elevated atmospheric CO2 is likely to be dependent on the canopy and stand structure. Here, we investigated elevated CO2 effects on the forest structure of a Liquidambar styraciflua L. stand in a free-air CO2 enrichment experiment, considering leaves, tree crowns, forest canopy and stand structure. During the 12-year experiment, the trees increased in height by 5 m and basal area increased by 37%. Basal area distribution among trees shifted from a relatively narrow distribution to a much broader one, but there was little evidence of a CO2 effect on height growth or basal area distribution. The differentiation into crown classes over time led to an increase in the number of unproductive intermediate and suppressed trees and to a greater concentration of stand basal area in the largest trees. A whole-tree harvest at the end of the experiment permitted detailed analysis of canopy structure. There was little effect of CO2 enrichment on the relative leaf area distribution within tree crowns and there was little change from 1998 to 2009. Leaf characteristics (leaf mass per unit area and nitrogen content) varied with crown depth; any effects of elevated CO2 were much smaller than the variation within the crown and were consistent throughout the crown. In this young, even-aged, monoculture plantation forest, there was little evidence that elevated CO2 accelerated tree and stand development, and there were remarkably small changes in canopy structure. Questions remain as to whether a more diverse, mixed species forest would respond similarly.

Plant ecophysiological processes in spectral profiles: perspective from a deciduous broadleaf forest

The need for progress in satellite remote sensing of terrestrial ecosystems is intensifying under climate change. Further progress in Earth observations of photosynthetic activity and primary production from local to global scales is fundamental to the analysis of the current status and changes in the photosynthetic productivity of terrestrial ecosystems. In this paper, we review plant ecophysiological processes affecting optical properties of the forest canopy which can be measured with optical remote sensing by Earth-observation satellites. Spectral reflectance measured by optical remote sensing is utilized to estimate the temporal and spatial variations in the canopy structure and primary productivity. Optical information reflects the physical characteristics of the targeted vegetation; to use this information efficiently, mechanistic understanding of the basic consequences of plant ecophysiological and optical properties is essential over broad scales, from single leaf to canopy and landscape. In theory, canopy spectral reflectance is regulated by leaf optical properties (reflectance and transmittance spectra) and canopy structure (geometrical distributions of leaf area and angle). In a deciduous broadleaf forest, our measurements and modeling analysis of leaf-level characteristics showed that seasonal changes in chlorophyll content and mesophyll structure of deciduous tree species lead to a seasonal change in leaf optical properties. The canopy reflectance spectrum of the deciduous forest also changes with season. In particular, canopy reflectance in the green region showed a unique pattern in the early growing season: green reflectance increased rapidly after leaf emergence and decreased rapidly after canopy closure. Our model simulation showed that the seasonal change in the leaf optical properties and leaf area index caused this pattern. Based on this understanding we discuss how we can gain ecophysiological information from satellite images at the landscape level. Finally, we discuss the challenges and opportunities of ecophysiological remote sensing by satellites.

Phosphorus Supply Increases Internode Length and Leaf Characteristics, and Increases Dry Matter Accumulation and Seed Yield in Soybean under Water Deficit

Phosphorus (P) addition ameliorates the adverse effects of water stress on the seed yield of soybean (Glycine max L.). Previous studies focused on the effect of P on root traits, but little information is available on changes to aboveground traits. In this paper, we show how P addition affects shoot traits and reduces the adverse effects of water stress on the yield. Two soybean genotypes, with contrasting aboveground architectures, were grown in pots to compare the canopy architecture, leaf traits, aboveground dry matter accumulation and yield under two water and three P levels. The addition of P to two soybean genotypes, one with a larger number of branches and greater leaf area on the branches than the other, showed that the increased leaf area distribution on the main stem and branches was associated with increased shoot and root dry weights, which were positively correlated with the number of filled pods, seed number and seed yield and negatively correlated with seed size at maturity under well-watered and cyclic water stress treatments. The leaf P concentration at 65 DAS (flowering stage) and leaf photosynthesis measured shortly after re-watering increased with P addition, while the leaf mass area on the main stem at 65 DAS and maturity and on the branches at maturity increased modestly with P supply and water stress. Evidence is presented that P addition can ameliorate the adverse effects of water stress on yield through increased leaf area, leaf function and aboveground shoot production. We conclude that the increased yields of soybean resulting from increased P and water supplies that were previously shown to be associated with increased root growth and function are mediated through increased shoot growth and function, particularly the greater number of sites for pod production.

Modeling Profiles of Micrometeorological Variables in a Tropical Premontane Rainforest Using Multi‐Layered CLM (CLM‐ML)

AbstractThis study updates the multi‐layered Community Land Model (CLM‐ml) for hillslopes and compares predictions from against observations collected in tropical montane rainforest, Costa Rica. Modifications are made in order to capture a wider array of vertical leaf area distributions, predict CO2 profiles, account for soil respiration, and adjust wind forcings for difficult topographic settings. Test results indicate that the modified multi‐layer CLM model can successfully replicate the shape of various micrometeorological profiles (humidity, CO2, temperature, and wind speed) under the canopy. In the single‐layer models (CLM4.5 and CLM5), excessive day‐to‐night differences in leaf temperature and leaf wetness were originally noted, but CLM‐ml significantly improved these issues, decreasing the amplitudes of diurnal cycles by 67% and 47%. Sub‐canopy considerations, such as canopy shapes and turbulent transfer parameters, also played a significant role in model performance. More importantly, unlike single layer models, the results that CLM‐ml produces can be compared to variables measured within the canopy to provide far more detailed diagnostic information. Further observations and model developments, aimed at reflecting surface heterogeneity, will be necessary to adequately capture the complexity and the features of the tropical montane rainforest.

UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

Mapping forest leaf area density from multiview terrestrial lidar

Abstract Terrestrial lidar data are known to be useful for estimating the three‐dimensional (3D) distribution of leaf area in forests. This type of product holds great potential for modelling canopy reflectance and light interception to study the links between structure and function. However, little is currently known about its potential and limits in dense forests. Higher leaf area density implies that more laser pulses emitted by the ground‐based instrument are intercepted in lower canopy levels, and the implications of such occlusion effects on radiative transfer simulations are unknown. Occlusion effects can be minimized by increasing the number of locations lidar data is acquired from; how many locations are required for a forest with a given structure? This paper aims to address these knowledge gaps. We acquired terrestrial lidar data using a very high density of scanning positions (5 m between positions) over four dense forest 60 m × 60 m plots along a structural gradient. Occlusion effects were quantified, and the 3D distribution of leaf area density was mapped using voxels (cubic volumes) for four different scan densities (one original and three downsampled). The voxel arrays were then input into a radiative transfer model to simulate bidirectional reflectance factors and vertical fraction of absorbed radiation. We found that the summation of leaf area estimates for all voxels within the plot provided leaf area index (LAI) values close to LAI values estimated using traditional methods at each site. Occluded areas occurred mostly at the top of bottom heavy canopies. Radiative transfer simulations suggest that modelling small scale (<1 m) bidirectional reflectance factors (BRF) and light interception requires the highest scan position density used (5 m between scan positions), particularly at bottom heavy sites, and that 10 m between scan positions can be used for plot scale BRF simulations in forests with foliage density and vertical profiles similar to those tested here. This work establishes some initial guidelines for establishing terrestrial lidar survey protocols for mapping leaf area density in forests. The leaf area density voxel arrays derived are among the most accurate plot‐level 3D characterizations of foliage arrangement produced to date.

Leaf area index estimation using top-of-canopy airborne RGB images

Leaf Area Index (LAI) is one of the most important biophysical properties of a crop, used in detecting long-term water stress, estimating biomass, and identifying crop growth stage. Remote sensing based LAI estimation techniques perform well for early growth stages but tend to produce high error during the crop reproductive stage due to canopy closure. Moreover, estimation of the true LAI from individual leaf measurements remains a challenge. Consequently, two alternate methods have been developed and compared for estimating the LAI of a maize crop using top-of-canopy RGB images collected throughout the growing season using a hexacopter. Both methods used the RGB images to estimate the canopy height and the green-canopy cover together with a ‘vertical leaf area distribution factor’ (VLADF) from allometric relations (using crop height from RBG images and days after sowing). The first method used an empirical approach to estimate the LAI from training a linear function of the above inputs to Licor canopy analyser values of LAI. The method was trialled for a research farm located in a semi-arid area of southern peninsula India and found to have validation results with an R2 of 0.84 and RMSE of 0.36 for the unused portion of the Rabi (post-monsoon) season data of 2018–19, and R2 of 0.77 and RMSE of 0.45 for the Rabi 2019–20 season data when compared with Licor LAI values. While seemingly acceptable, the Licor canopy analyser gives a foliage area index and so the accuracy of this model was very low (R2 of 0.56 and RMSE of 1.34) when evaluated with true LAI from manual measurements of the leaf area. Consequently, a refinement was introduced using only VLADF, green-canopy cover estimates from the RBG images, and a field measured top leaf angle. The output derived from this conceptual model had an R2 of ~0.6 and RMSE of 0.73 when compared with the true LAI values. Importantly, the LAI from this conceptual model was found to be unaffected by canopy closure during the reproductive stage of the crop.

A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images

Plant architectural traits are important selection criteria in plant breeding that relate to photosynthetic efficiency and crop productivity. Conventional manual measures of architectural traits for large breeding trials are labour- and time-consuming. In this study, we proposed a new method to reconstruct three-dimensional (3D) canopy architectural models for high-throughput phenotyping of canopy architectural traits using image sequences acquired by an unmanned aerial vehicle (UAV) platform. The accuracy of UAV-derived models is evaluated by comparisons with models from 3D digitizing and measured values. The results indicated that the proposed method could obtain full canopy architecture in the early growth stages and the upper parts of the canopy architecture in the later growth stages. The leaf number, plant height, individual leaf area, and vertical and horizontal distributions of the leaf area estimated from UAV-derived models were in good agreements with the reference values for maize. The derived length and maximum width of individual leaves were close to the field measurements for maize (R2 > 0.92 for both, RMSE < 4.16 cm and 0.64 cm for blade length and maximum width, respectively) and soybean (R2 > 0.85 and RMSE < 0.77 cm). The newly proposed method has promising prospects for high-throughput phenotyping of the canopy architectural traits of field-grown crops and could facilitate the genotype selection in crop breeding and 3D plant modelling.

Simulating the effect of flowering time on maize individual leaf area in contrasting environmental scenarios.

The quality of yield prediction is linked to that of leaf area. We first analysed the consequences of flowering time and environmental conditions on the area of individual leaves in 127 genotypes presenting contrasting flowering times in fields of Europe, Mexico, and Kenya. Flowering time was the strongest determinant of leaf area. Combined with a detailed field experiment, this experiment showed a large effect of flowering time on the final leaf number and on the distribution of leaf growth rate and growth duration along leaf ranks, in terms of both length and width. Equations with a limited number of genetic parameters predicted the beginning, end, and maximum growth rate (length and width) for each leaf rank. The genotype-specific environmental effects were analysed with datasets in phenotyping platforms that assessed the effects (i) of the amount of intercepted light on leaf width, and (ii) of temperature, evaporative demand, and soil water potential on leaf elongation rate. The resulting model was successfully tested for 31 hybrids in 15 European and Mexican fields. It potentially allows prediction of the vertical distribution of leaf area of a large number of genotypes in contrasting field conditions, based on phenomics and on sensor networks.

Unsupervised semantic and instance segmentation of forest point clouds

Terrestrial Laser Scanning (TLS) has been increasingly used in forestry applications including forest inventory and plant ecology. Tree biophysical properties such as leaf area distributions and wood volumes can be accurately estimated from TLS point clouds. In these applications, a prerequisite is to properly understand the information content of large scale point clouds (i.e., semantic labelling of point clouds), so that tree-scale attributes can be retrieved. Currently, this requirement is undergoing laborious and time consuming manual works. In this work, we jointly address the problems of semantic and instance segmentation of forest point clouds. Specifically, we propose an unsupervised pipeline based on a structure called superpoint graph, to simultaneously perform two tasks: single tree isolation and leaf-wood classification. The proposed method is free from restricted assumptions of forest types. Validation using simulated data resulted in a mean Intersection over Union (mIoU) of 0.81 for single tree isolation, and an overall accuracy of 87.7% for leaf-wood classification. The single tree isolation led to a relative root mean square error (RMSE%) of 2.9% and 19.8% for tree height and crown diameter estimations, respectively. Comparisons with existing methods on other benchmark datasets showed state-of-the-art results of our method on both single tree isolation and leaf-wood classification tasks. We provide the entire framework as an open-source tool with an end-user interface. This study closes the gap for using TLS point clouds to quantify tree-scale properties in large areas, where automatic interpretation of the information content of TLS point clouds remains a crucial challenge.

Assessing the reliability of predicted plant trait distributions at the global scale.

AimPredictions of plant traits over space and time are increasingly used to improve our understanding of plant community responses to global environmental change. A necessary step forward is to assess the reliability of global trait predictions. In this study, we predict community mean plant traits at the global scale and present a systematic evaluation of their reliability in terms of the accuracy of the models, ecological realism and various sources of uncertainty.LocationGlobal.Time periodPresent.Major taxa studiedVascular plants.MethodsWe predicted global distributions of community mean specific leaf area, leaf nitrogen concentration, plant height and wood density with an ensemble modelling approach based on georeferenced, locally measured trait data representative of the plant community. We assessed the predictive performance of the models, the plausibility of predicted trait combinations, the influence of data quality, and the uncertainty across geographical space attributed to spatial extrapolation and diverging model predictions.ResultsEnsemble predictions of community mean plant height, specific leaf area and wood density resulted in ecologically plausible trait–environment relationships and trait–trait combinations. Leaf nitrogen concentration, however, could not be predicted reliably. The ensemble approach was better at predicting community trait means than any of the individual modelling techniques, which varied greatly in predictive performance and led to divergent predictions, mostly in African deserts and the Arctic, where predictions were also extrapolated. High data quality (i.e., including intraspecific variability and a representative species sample) increased model performance by 28%.Main conclusionsPlant community traits can be predicted reliably at the global scale when using an ensemble approach and high‐quality data for traits that mostly respond to large‐scale environmental factors. We recommend applying ensemble forecasting to account for model uncertainty, using representative trait data, and more routinely assessing the reliability of trait predictions.

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

Abstract Leaf‐wood separation in terrestrial LiDAR data is a prerequisite for non‐destructively estimating biophysical forest properties such as standing wood volumes and leaf area distributions. Current methods have not been extensively applied and tested on tropical trees. Moreover, their impacts on the accuracy of subsequent wood volume retrieval were rarely explored. We present LeWoS, a new fully automatic tool to automate the separation of leaf and wood components, based only on geometric information at both the plot and individual tree scales. This data‐driven method utilizes recursive point cloud segmentation and regularization procedures. Only one parameter is required, which makes our method easily and universally applicable to data from any LiDAR technology and forest type. We conducted a twofold evaluation of the LeWoS method on an extensive dataset of 61 tropical trees. We first assessed the point‐wise classification accuracy, yielding a score of 0.91 ± 0.03 in average. Second, we evaluated the impact of the proposed method on 3D tree models by cross‐comparing estimates in wood volume and branch length with those based on manually separated wood points. This comparison showed similar results, with relative biases of less than 9% and 21% on volume and length respectively. LeWoS allows an automated processing chain for non‐destructive tree volume and biomass estimation when coupled with 3D modelling methods. The average processing time on a laptop was 90s for 1 million points. We provide LeWoS as an open‐source tool with an end‐user interface, together with a large dataset of labelled 3D point clouds from contrasting forest structures. This study closes the gap for stand volume modelling in tropical forests where leaf and wood separation remain a crucial challenge.

Dynamic Simulation on Canopy Light Distribution and Dry Matter Accumulation of Natural Colored Cotton in Alar Reclamation Area of Xinjiang

Based on Ross’s theory of optical radiation transmission and full consideration of influences of vertical distribution of canopy leaf area and leaf inclination angle distribution of colored cotton on the light distribution, the Gaussian 5-point distance was used to divide the canopy into 5 layers on basis of the leaf area index. The leaf inclination angle on each layer was divided into 6 equal parts by 15°. The types of radiation in canopy, spatial distribution of light radiation, as well as diurnal variation with solar hour angles were quantified in detail. After comprehensively considering influences of temperature, physiological age and other factors on photosynthesis and respiration, the canopy light distribution, photosynthetic production and dry matter accumulation of colored cotton were simulated with strong mechanistic and physiological &amp; ecological significance. The colored cotton samples sown on April 16, 2019 were used to verify the model. The RMSEs of simulated and measured canopy PAR values at Beijing time 10:00, 12:00, 14:00 and 16:00 on July 30 were 58.2, 64.1, 43.4 and 39.7 µmol•m-2•s-1, respectively. The RMSE of simulated and observed values of the dry matter accumulation above ground was 412.6 kgDM•hm-2, reflecting the good predictability of the model.

In Situ Cotton Leaf Area Index by Height Using Three‐Dimensional Point Clouds

Three‐dimensional (3‐D) high‐throughput crop phenotyping may benefit plant research and breeding programs by providing a rapid, nondestructive method of determining in‐season crop growth and development. In this study, a set of three inexpensive, structured, near‐infrared laser projectors mounted on a robotic platform were used to generate high‐density color point clouds (PCs) in cotton (Gossypium hirsutum L.) in 2016 and 2017. The PCs were calibrated based on destructive leaf area measurements, and the transformed PCs were analyzed to quantify leaf area by canopy height in 1‐cm intervals. We conducted weekly scans of 8‐m plots throughout the growing season for two cotton cultivars and two irrigation treatments during the 2 yr. The calibrated PCs were used to accurately measure leaf area index and leaf area index by canopy height. The analysis of leaf area distribution throughout the growing season indicated significant cultivar and irrigation effects by canopy height as well as significant interactions between cultivar and irrigation. Based on the results, it is possible to use a 3‐D sensor system to distinguish growth habits that are not easily measured using traditional growth measurements. The information obtained from 3‐D measurements may provide additional information about several canopy characteristics that are currently difficult to measure, including radiation capture parameters and biomass partitioning.Core Ideas Leaf area index (LAI) has not traditionally been separated in plants by height. Three‐dimensional sensing was used to accurately measure LAI by height in cotton. Cotton cultivar, irrigation, and growth stage measurably affected LAI by height. Three‐dimensional sensing may complement measures of radiation capture and other spatial factors.

Tree-species interactions increase light absorption and growth in Chinese subtropical mixed-species plantations.

Light-related interactions can increase productivity in tree-species mixtures compared with monocultures due to higher stand-level absorption of photosynthetically active radiation (APAR) or light-use efficiency (LUE). However, the effects of different light-related interactions, and their relative importance, have rarely been quantified. Here, measurements of vertical leaf-area distributions, tree sizes, and stand density were combined with a tree-level light model (Maestra) to examine how crown architecture and vertical or horizontal canopy structure influence the APAR of 16 monocultures and eight different two-species mixtures with 16 different species in a Chinese subtropical tree diversity experiment. A higher proportion of crown leaf area occurred in the upper crowns of species with higher specific leaf areas. Tree-level APAR depended largely on tree leaf area and also, butto a lesser extent, on relative height (i.e., tree dominance) and leaf-area index (LAI). Stand-level APAR depended on LAI and canopy volume, but not on the vertical stratification or canopy leaf-area density. The mixing effects, in terms of relative differences between mixtures and monocultures, on stand-level APAR were correlated with the mixing effects on basal area growth, indicating that light-related interactions may have been responsible for part of the mixing effects on basal area growth. While species identity influences the vertical distributions of leaf area within tree crowns, this can have a relatively small effect on tree and stand APAR compared with the size and vertical positioning of the crowns, or the LAI and canopy volume.

Crown architecture, crown leaf area distribution, and individual tree growth efficiency vary across site, genetic entry, and planting density

We examined crown architecture and within crown leaf area distribution effects on Pinus taeda L. growth in North Carolina (NC), Virginia (VA), and Brazil (BR) to better understand why P. taeda can grow much better in Brazil than in the southeastern United States. The NC, VA, and BR sites were planted in 2009, 2009, and 2011, respectively. At all sites, we planted the same two genetic entries at 618, 1236, and 1854 trees ha−1. In 2013, when trees were still open grown, the VA and NC sites had greater branch diameter (24%), branch number (14%), live crown length (44%), foliage mass (82%), and branch mass (91%), than the BR site. However, in 2017, after crown closure and when there was no significant difference in tree size, site did not significantly affect these crown variables. In 2013, site significantly affected absolute leaf area distribution, likely due to differences in live crown length and leaf area, such that there was more foliage at a given level in the crown at the VA and NC sites than at the BR site. In 2017, site was still a significant factor explaining leaf area distribution, although at this point, with crown closure and similar sized trees, there was more foliage at the BR site at a given level in the crown compared to the VA and NC sites. In 2013 and 2017, when including site, genetic entry, stand density, and leaf area distribution parameters as independent variables, site significantly affected individual tree growth efficiency, indicating that something other than leaf area distribution was influencing the site effect. Better BR P. taeda growth is likely due to a combination of factors, including leaf area distribution, crown architecture, and other factors that have been identified as influencing the site effect (heat sum), indicating that future work should include a modeling analysis to examine all known contributing factors.

Estimating Crown Structure Parameters of Moso Bamboo: Leaf Area and Leaf Angle Distribution

Both leaf area (LA) and leaf angle distribution are the most important eco-physiological measures of tree crowns. However, there are limited published investigations on the two parameters of Moso bamboo (Phyllostachys edulis (Carrière) J. Houz., abbreviated as MB). The aim of this study was to develop allometric equations for predicting crown LA of MB by taking the diameter at breast height (DBH) and tree height (H) as predictors and to investigate the leaf angle distribution of a MB crown based on direct leaf angle measurements. Data were destructively sampled from 29 MB crowns including DBH, H, biomass and the area of sampled leaves, biomass of total crown leaves, and leaf angles. The results indicate that (1) the specific leaf area (SLA) of a MB crown decreases from the bottom to the top; (2) the vertical LA distribution of MB crowns follow a “Muffin top” shape; (3) the LA of MB crowns show large variations, from 7.42 to 74.38 m2; (4) both DBH and H are good predictors in allometry-based LA estimations for a MB crown; (5) linear, exponential, and logarithmic regressions show similar capabilities for the LA estimations; (6) leaf angle distributions from the top to the bottom of a MB crown can be considered as invariant; and (7) the leaf angle distribution of a MB crown is close to the planophile case. The results provide an important tool to estimate the LA of MB on the standing scale based on DBH or H measurements, provide useful prior knowledge for extracting leaf area indexes of MB canopies from remote sensing-based observations, and, therefore, will potentially serve as a crucial reference for calculating carbon balances and other ecological studies of MB forests.

Modelling Approach for Predicting the Impact of Changing Temperature Conditions on Grapevine Canopy Architectures

Future climatic conditions might have severe effects on grapevine architecture, which will be highly relevant for vineyard management decisions on shoot positioning, pruning or cutting. This study was designed to help gaining insight into how, in particular, increasing temperatures might affect grapevine canopies. We developed a functional-structural model for Riesling, Virtual Riesling, based on digitised data of real plants and a comprehensive state-of-the-art data analysis. The model accounts for the variability in temperature-sensitive morphological processes, such as bud break and appearance rates. Our simulation study using historical weather data revealed significant effects of the thermal time course over the year on bud burst of the cane and on primary shoots. High variabilities in these events affect canopy growth and leaf area distribution. This report shows that Virtual Riesling can be useful in assessing the significance of changing temperatures for grapevine architecture and thereby considering management techniques such as vertical shoot positioning. Further developments of Virtual Riesling might support the knowledge gain for developing necessary adaptations in future vineyard management and, thus, facilitate future work on climate change research using functional-structural model approaches.

Vertical Distribution of Leaf Area of European Larch (Larix decidua Mill.) and Norway Spruce (Picea abies (L.) Karst.) in Pure and Mixed Stands

The growth effects of mixtures are generally assumed to be a result of canopy structure and crown plasticity. Thus, the distribution of leaf area at tree and stand level helps to explain these mixing effects. Therefore, we investigated the leaf area distribution in 12 stands with a continuum of proportions of European larch (Larix decidua Mill.) and Norway spruce (Picea abies (L.) Karst.). The stands were between 40 and 170 years old and located in the northern part of the Eastern Intermediate Alps in Austria at elevations between 900 and 1300 m asl A total of 200 sample trees were felled and the leaf area distribution within their crowns was evaluated. Fitting beta distributions to the individual empirical leaf area distributions, the parameters of the beta distributions were shown to depend on the leaf area of the individual trees and, for spruce, on the proportion of spruce in the stands. With the equations determined, the leaf area distribution of all trees in the stand, and thus its distribution in the stands, was calculated by species and in 2 m height classes. For the individual trees, we found that the leaf area distribution of larch is more symmetric, and its peak is located higher in the crown than it is the case for spruce. Furthermore, the leaf area distribution of both species becomes more peaked and skewed when the leaf area of the trees increases. The mixture only influences the leaf area distribution of spruce in such a way that the higher the spruce proportion of the stand, the higher the leaf area is located within the crown. At the stand level, a strong relationship was found between the proportion of spruce and the distance between the peaks of the leaf area distributions of larch and spruce.