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Diagram of leaves showing the positions of length and width measurements for the five broadleaf species.
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Based on a linear mixed-effect model, we propose here a non-destructive, rapid and reliable way for estimating leaf area, leaf mass and specific leaf area (SLA) at leaf scale for broadleaf species. For the construction of the model, the product of leaf length by width (LW) was the optimum variable to predict the leaf area of five deciduous broadlea...
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... each leaf sampled, the petiole was first removed, and we then obtained the following observations: (1) length, L; (2) width, W; and (3) thickness, T. The observation details are provided in Table 1. The length was measured from the apex of the blade to the base of the petiole, and width was measured at the widest point perpendicular to the longitudinal axis of the leaf (Fig. 1). The measurements were made using a ruler (with a precision of 0.1 cm). For each sample leaf, the thickness was measured three times using a Vernier caliper (with precision of 0.01 mm), and the average value was taken as the thickness of leaf. Then, the leaf area was obtained through scanning using a BenQ-5560 image scanner The LSD multiple comparison test was used to compare leaf area among different categories at 0.05 level for each broadleaf species. Additionally, different lowercase letters for each species indicate a significant difference between leaf areas in seasonal or canopy vertical categories at the 0.05 significance level. Error bar was the standard error. The same applies below for Fig. 3. Table 1 Means (standard deviations, SD), maximum (max) and minimum (min) values for the leaf length, width and thickness for the five broadleaf species examined. (BenQ Corporation, China, 300 dpi resolution); the scanner having been calibrated by scanning colored paper with a known area. The leaf area value from the scanner was taken as the actual leaf area to evaluate the accuracy of the methods for predicting the leaf area. After measuring the leaf area value, all the samples were dried at 65 • C for 48 h to a constant mass (with precision of 0.0001 g), which was used as the actual leaf ...
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... simple linear model has been used to predict leaf area by many researchers due to its convenience and brevity (Peksen, 2007;Liu et al., 2015b), but they have often ignored the influences of seasonal and canopy vertical variations on constructing a regression model between leaf area and leaf structural parameters. In this study, leaf area significantly varied with the season and the canopy positions for each broadleaf species (Fig. 2). For all five species, the seasonal variations of leaves had an especially significant effect on con- structing leaf area regression models (Table 2). Therefore, a linear mixed-effect model was more reasonable for predicting leaf area of leaves in different seasons or canopy positions, which was further confirmed by the low MAE% values for predicting leaf area of five species in different season and canopy vertical categories (a total of nine categories). That is to say, the mean MAE% was largest for A. mono with a value of 13.0%, probably due to its palm leaf shape. This case showed that the more symmetric the leaf, the better the prediction of the leaf area ( Fig. 1 and Table ...
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... contrast, fewer studies have estimated broadleaf leaf mass based on leaf structural parameters. To our knowledge, Tondjo et al. (2015) firstly reported that the leaf mass of teak leaves (broadleaf species) could also be predicted by leaf structural parameters (i.e., LW) and highlighted that it could be better estimated by a power model than a linear model. In this study, a non-linear model (i.e., power model) was selected to predict leaf mass not only referring to results reported by Tondjo et al. (2015) but also considering the biological characteristics of leaf mass during growth. For instance, leaf mass increased with leaf emergence in spring and decreased with leaf shedding ( Fife et al., 2008). Meanwhile, many previous studies reported that increased leaf mass continued even after full leaf expansion for most species (Miyazawa et al., 1998;Athokpam et al., 2014), which is in agreement with our results for T. amurensis, F. mandshurica and A. mono (Fig. 3). However, the leaf mass sig- nificantly decreased in September for B. platyphylla and B. costata, probably because the leaves of Betula L. species senesce earlier than those of the other three species (Liu et al., 2015a). These results sug- gest that a power model is reasonable and acceptable for leaf mass ...
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... However, for coniferous plants, these methods require considerable manpower and material resources [28,29] because of their three-dimensional structure and large number of leaves. To address this issue, an increasing number of researchers are estimating LMA by establishing regression models between LMA and plant traits, leaf morphology, or environmental conditions such as leaf length, leaf width [30,31], branch height [32,33], and LDMC [34][35][36]. As the correlation between LMA and vertical direction in a tree crown is significant, variables related to vertical height, such as branch height, depth into crown and relative depth into crown, are often used as the main fitting factors. ...
Leaf mass per area (LMA) is a key structural parameter that reflects the functional traits of leaves and plays a vital role in simulating the material and energy cycle of plant ecosystems. In this study, vertical whorl-by-whorl sampling of LMA was conducted in a young Larix principis-rupprechtii plantation during the growing season at the Saihanba Forest Farm. The vertical and seasonal variations in LMA were analysed. Subsequently, a predictive model of LMA was constructed. The results revealed that the LMA varied significantly between different crown whorls and growing periods. In the vertical direction of the crown, the LMA decreased with increasing crown depth, but the range of LMA values from the tree top to the bottom was, on average, 30.4 g/m2, which was approximately 2.5 times greater in the fully expanded phase than in the early leaf-expanding phase. The LMA exhibited an allometric growth trend that increased during the leaf-expanding phase and then tended to stabilize. However, the range of LMA values throughout the growing period was, on average, 40.4 g/m2. Among the univariate models, the leaf dry matter content (LDMC) performed well (Ra2=0.45, RMSE=13.48 g/m2) in estimating the LMA. The correlation between LMA and LDMC significantly differed at different growth stages and at different vertical crown whorls. The dynamic predictive model of LMA constructed with the RDINC and DOY as independent variables was reliable in both the assessments (Ra2=0.68, RMSE=10.25 g/m2) and the validation (MAE=8.05 g/m2, FI=0.682). Dynamic simulations of crown LMA provide a basis for elucidating the mechanism of crown development and laying the foundation for the construction of an ecological process model..
... This has allowed for the proliferation of field-based measurements in remote areas, particularly when study sites are located far from laboratory facilities. This can also allow for the non-destructive sampling of leaves and forests (Liu et al., 2017). Photographs of each leaf taken on the day of field collection were also passed through the ImageJ program to calculate "leaf greenness" and leaf area (cm 2 ) (Schneider et al., 2012). ...
Nutrient subsidies have significant impacts on ecosystems by connecting disjunct habitats, often through long‐distance animal migrations. Salmon migrations on the North Pacific coasts provide these kinds of nutrient subsidies from senescent fish at the end of their life cycle, which can have significant ecological effects on terrestrial species. This can include impacts on individuals, populations, and communities, where shifts in community composition towards plant species that indicate nitrogen‐rich soils have been documented. We investigated the effects of variation in salmon spawning density on the leaf traits of four common riparian plant species on the central coast of British Columbia, Canada. We found that all plant species had higher foliar salmon‐derived nitrogen on streams with a higher spawning density. Three of the four species had larger leaves, and one species also had higher leaf mass per area on streams with more salmon. However, we found no differences in leaf greenness or foliar percent nitrogen among our study streams. These results demonstrate that nutrient subsidies from spawning salmon can have significant impacts on the ecology, morphology, and physiology of riparian plants, which lends support to a mechanism by which certain plants are more common on productive salmon streams.
... The most important components of a plant's photosynthetic system are its leaves, which are essential for both plant functioning and enduring environmental adaptability [27]. Essential leaf traits include leaf shape, biomass, and water content [28]. ...
Bistorta amplexicaulis is an essential medicinal plant found in the Kashmir Himalaya. Ethnobotanical studies have revealed that this particular species is used to treat fractures, muscle injuries, heart problems, abnormal leucorrhoea, menorrhagia and inflammation of the mouth and tongue. The current study aimed to determine the variation in growth traits and fluctuations in the allocation patterns with respect to different habitats across the altitudinal gradient. In order to adapt to unpredictable and stressful conditions at higher altitudes, phenotypic plasticity plays a crucial role. Our findings revealed considerable variability in the phenotypic traits, indicating that altitude has a defined effect on this specific species’s morphology and reproductive traits. Low altitude plant populations of Kashmir University Botanical Garden (KUBG), Dara and Tangmarg were more robust and taller (98.4±2.36, 83.58±2.69 and 74.08±1.59 cm, respectively) than the populations of Pissu top and Bangus (23.96±3.38 and 30.43±1.12 cm respectively) at higher altitudes. The habitats of KUBG, Dara, and Tangmarg proved to be substantially better for the growth of B. amplexicaulis, as per the Principal component analysis (PCA). The regression analysis demonstrated a negative relation between altitude and plant height. Traits such as leaf length/ breadth, Rhizome length/ breadth and inflorescence length showed a strong correlation with plant height. Our results provide an inclusive description of the phenotypic variability of this significant medicinal plant in response to the habitat variability across different altitudes.
... There are direct and indirect methods to determine the LA of plants (Kishor Kumar et al. 2017). Of the direct methods, the use of conventional planimeters, scanners, or fixed cameras and software for image processing stand out (Granier et al. 2002;Liu et al. 2017;Su arez et al. 2018). However, these methods require leaf extraction (Cristofori et al. 2007;Fallovo et al. 2008). ...
... However, these methods require leaf extraction (Cristofori et al. 2007;Fallovo et al. 2008). On the other hand, most non-destructive methods allow estimation of LA and leaf weight through regression models considering leaf length and width (Pompelli et al. 2012;Tondjo et al. 2015;Weraduwage et al. 2015;Kishor Kumar et al. 2017;Liu et al. 2017). Regression models that consider leaf structural parameters such as length (L), L 2 , width (Wi), Wi 2 , or a combination of length and width (LÂWi) have been found to have high accuracy in estimating LA (Cristofori et al. 2007;Meng et al. 2019). ...
Non-destructive methods that accurately estimate leaf area (LA) and leaf weight (LW) are simple and inexpensive, and represent powerful tools in the development of physiological and agronomic research. The objective of this research is to generate mathematical models for estimating the LA and LW of Cinchona officinalis leaves. A total of 220 leaves were collected from C. officinalis plants 10 months after transplantation. Each leaf was measured for length, width, weight, and leaf area. Data for 80% of leaves were used to form the training set, and data for the remaining 20% were used as the validation set. The training set was used for model fit and choice, whereas the validation set al.lowed assessment of the of the model’s predictive ability. The LA and LW were modeled using seven linear regression models based on the length (L) and width (Wi) of leaves. In addition, the models were assessed based on calculation of the following statistics: goodness of fit (R²), root mean squared error (RMSE), Akaike’s information criterion (AIC), and the deviation between the regression line of the observed versus expected values and the reference line, determined by the area between these lines (ABL). For LA estimation, the model LA = 11.521(Wi) − 21.422 (R² = 0.96, RMSE = 28.16, AIC = 3.48, and ABL = 140.34) was chosen, while for LW determination, LW = 0.2419(Wi) − 0.4936 (R² = 0.93, RMSE = 0.56, AIC = 37.36, and ABL = 0.03) was selected. Finally, the LA and LW of C. officinalis could be estimated through linear regression involving leaf width, proving to be a simple and accurate tool.
... Furthermore, SLA is often positively correlated to photosynthetic capacities, relative growth rate, expanded root system, and mineral uptake. Thus, it is a key trait related to important plant functional properties (Liu et al., 2017). As far as we know, although many studies have been done on predicting LAI, grain, and biomass from remote sensing, the prediction of SLA is scarce. ...
... Leaf length and width were measured using a ruler (precision 0.1 cm). The length was measured from the apex to the base of the leaf, while the width was measured at the widest part of the leaf [18]. Measurements of leaf length and width can be used as a reference to determine leaf shape by comparing the length and width of the leaf [17]. ...
Plants of the genus Bacopa are ornamental water plants used in aquascape design. This study examined the differences in morphological and anatomical characteristics in the roots, stems, and leaves of three Bacopa species, namely B. amplexicaulis, B. lanigera , and B. rotundifolia . The identification of morphological structure was based on a book entitled Morfologi Tumbuhan (Plant Morphology) by Tjitrosoepomo (2016). Anatomical identification referred to the book on Plant Anatomy by Crang et al. (2018). The data collected were analyzed descriptively and quantitatively. Bacopa rotundifolia had the smallest leaf size, while B. amplexicaulis had the largest leaf size. All of the observed species had leaf anatomical characters in the form of anomocytic stomata. Bacopa amplexicaulis had the largest stem diameter, measuring 4.38 cm. Trichome and aerenchyma structures were found in all stems of the test species. Bacopa lanigera stems had the most trichomes, 72 in one cross-section. Meanwhile, B. amplexicaulis had the longest trichome (1.590 µm). The aerenchyma of B . amplexicaulis and B. lanigera was flat, while that of B. rotundifolia was oval. Bacopa amplexicaulis had the longest aerenchyma at 957 µm. Only B. lanigera had a trichome on the root.
... Because of the environmental and economic importance that this species presents and the lack of work on it, further studies are needed that seeks to evaluate its growth, development, and reproduction. Among these studies, the leaf area is important to determine the growth (Fascella et al., 2013;Abreu et al., 2015) and productivity (Walia & Kumar et al., 2017;Liu et al., 2017) of crops. This variable is directly related to the amount of light energy intercepted and the conversion of chemical energy in Mela et al. (2022) Estimation of Thunbergia grandiflora leaf... photosynthesis (Sala et al., 2015). ...
Sky vine (Thunbergia grandiflora Roxb) is a vine with important structural components for forest environments. Studies on growth and development are necessary, because of the environmental and economic importance. The leaf area determination is essential for ecophysiological studies to understand the relationship of the plant with the environment. The objective of this work was to estimate an allometric equation to estimate the leaf area of T. grandiflora from linear dimensions. 200 leaves of different shapes and sizes were collected from adult plants and the length (L), width (W), the product between length and width (LW), and real leaf area (LA) were measured. The linear regression, linear without intercept, quadratic, cubic, power, and exponential models were used to estimate the equations. The criteria for determining the best model were higher determination coefficient (R2), Willmott's agreement index (d), lower Akaike information criterion (AIC), the root of the mean error square (RMSE), and BIAS index closer to zero. The leaf area of T. grandiflora can be estimated satisfactorily by the equation ŷ = 0.58*LW.
... These direct methods involve destructive plant sampling, and multiple measurements on the same leaf cannot be conducted (Suárez Salazar et al., 2018). However, due to their ability to measure leaf parameters relatively accurately (Kostadinov and Moteva, 2014), they are often widely used as the basic data for modeling (Wang et al., 2019;Liu et al., 2017) and canopy structure parameters estimating, such as leaf area index (Ern et al., 2020;Liu et al., 2015). Some non-destructive alternative direct methods that can be used to measure leaf area involves using portable scanning planimeters (Lu et al., 2004), portable area meter (Santiago and Wright, 2007;Olivas et al., 2013), RGB-D sensor (Yau et al., 2021), etc. ...
... Generally, the total increase in LM is the sum of the increase in the mass due to the increase of LA and leaf thickness (Weraduwage et al., 2015), which indicates that the optimal independent variables for predicting LM are more diverse (Wang et al., 2019). Liu et al. (2017) found that for leaves with thickness greater than 0.1 mm, LWT was more suitable for linear mixed-effects models than LW, and for leaves with thickness lesser than 0.1 mm, LW was more suitable for linear mixed effects models than LWT. Similarly, Wang et al. (2019) predicted the LA and LM for broad-leaved trees of two life forms in northeastern China and found that LWT can better predict LM when L:W is greater than 1.5, and either LW or LWT can predict LM for a certain period when L:W is lesser than 1.5. ...
... The season has an important effect on leaf growth and is a non-negligible variable for both LA and LM model estimation. Many studies have concluded that seasonal effects on leaf growth and development (Cai et al., 2017;Liu et al., 2017;Wang et al., 2019). However, fewer reports have estimated the LM of deciduous leaves than the LA. ...
Leaves act as an important bridge between plants and the external environment. Many studies have been conducted to establish more accurate and efficient models for predicting values of leaf traits. However, the model based on tree species is not representative enough for tropical and subtropical forests with abundant tree species and complex structures. Additionally, ordinary models are generally insufficient to describe the spatial and temporal changes of leaves because of the variations between trees. Based on linear mixed-effects models (LMM), we estimated the leaf area (LA), leaf mass (LM), and specific leaf area (SLA) for tree species of four life forms in a karst primary forest. Our results suggested that LMM were reasonable and accurate in fitting and predicting LA and LM for different life forms in different seasons. The most accurate predictions were obtained while using the product of leaf length and leaf width. Specifically, LMM performed better (R² = 0.92 to 0.99 and AIC = 118.5 to 4306.76 for leaf area; R² = 0.88 to 0.94 and AIC = –4389.5 to –969.1 for leaf mass) than the models only considered the fixed effects (R² = 0.89 to 0.99 and AIC = 119.1 to 4464.79 for leaf area; R² = 0.79 to 0.87 and AIC = – 3179.7 to –940.2 for leaf mass). The mean absolute error percent values were 0.9%–14.4% for leaf area and 1.1% to 17.1% for leaf mass for four life forms. Considering the accuracy of the models and the sampling effort, the optimal number of sample leaves for SLA estimation was about 60–80.
... Leaf area is a key characteristic of ecophysiological investigations, since the study of leaves is essential for understanding the functioning of ecosystems by the consistency of their chemical, structural and physiological properties (Díaz et al., 2016;Wright et al., 2004). Besides, leaves are a fundamental unit in processes such as light interception, temperature regulation, and water balance (Ehleringer, 2017), and its measurement is important to evaluate plant development and their photosynthetic processes (Liu et al., 2017). Temperature, climatic and microclimatic factors may limit the anatomy and architecture of leaves (Wright et al., 2004). ...
Studies of leaf surface are important for the ecophysiological context. We tested 12 allometric equations to estimate the leaf area of the endemic species Erythroxylum pauferrense Plowman, using different morphometric measurements (n = 600). The analysis of variance (p < 0.001), determination coefficient (R2adj), standard error of estimates (Sxy), and graphical interpretation of the dispersion of errors were used to select the models. The residual bias was estimated through the confidence interval using t-Student distribution (p = 0.01). Simple linear equations (Ŷ = 0.0244 + 0.7204 × CxL) and power functions (Ŷ = 0.7279 × CxL0.9971) were validated from an independent sample of leaves, confirming their accuracy. The results are important to guide actions of conservation of the endemic species.
... Most TLSs use a level 1 laser, which does not harm the human eye. The tree canopy structure is quickly and accurately measured by a pulsed laser in a non-contact manner, thereby obtaining a large-area, complex, irregular forest point cloud data [19,20]. Another advantage of TLSs is their capability to separate a target tree from other trees using its unique distance information. ...
The accurate estimation of leaf area is of great importance for the acquisition of information on the forest canopy structure. Currently, direct harvesting is used to obtain leaf area; however, it is difficult to quickly and effectively extract the leaf area of a forest. Although remote sensing technology can obtain leaf area by using a wide range of leaf area estimates, such technology cannot accurately estimate leaf area at small spatial scales. The purpose of this study is to examine the use of terrestrial laser scanning data to achieve a fast, accurate, and non-destructive estimation of individual tree leaf area. We use terrestrial laser scanning data to obtain 3D point cloud data for individual tree canopies of Pinus massoniana. Using voxel conversion, we develop a model for the number of voxels and canopy leaf area and then apply it to the 3D data. The results show significant positive correlations between reference leaf area and mass (R2 = 0.8603; p < 0.01). Our findings demonstrate that using terrestrial laser point cloud data with a layer thickness of 0.1 m and voxel size of 0.05 m can effectively improve leaf area estimations. We verify the suitability of the voxel-based method for estimating the leaf area of P. massoniana and confirmed the effectiveness of this non-destructive method.
