Foliage Biomass Research Articles

Use of artificial neural networks with the physiological principles to predict growth model

Process-based models have become essential tools for forest planning and management due to their ability to assess the impacts of climate and operational changes on productivity. Physiological Principles Predicting Growth (3-PG) is a process-based/hybrid model widely used, associated with allometric models to estimate dendrometric variables. However, the accuracy of the model outputs still needs to be improved, especially those of interest to forest management. In this study, we tested the efficiency of artificial neural networks (ANNs) to estimate the average diameter at 1.30 m height (dbh), total height, stand volume, and root, stem, and foliage biomass. We also evaluated the prediction of two parameters that estimate the proportion of biomass allocated aboveground (ap and np). A database from published 3-PG parameterizations was generated and used to train the ANNs. Two networks were validated against observed data from two municipalities in the MG state of Brazil. Potential productivity maps at six and seven years were generated for Brazil, to evaluate the performance of the 3-PG + ANN model. All ANNs presented satisfactory statistical results in predicting the ap and np parameters, foliage biomass, dbh, total height and stand volume. The use of artificial neural networks integrated with the 3-PG model or another process-based model is a promising alternative to improve the accuracy of estimates, especially those of interest for forest management. Artificial neural networks offer a simple, flexible way to insert important variables into a process-based model. The parameters and 3-PG outputs can be estimated efficiently using artificial neural networks.

Sequential Separation of Essential Oil Components during Hydrodistillation of Fresh Foliage from Azorean Cryptomeria japonica (Cupressaceae): Effects on Antibacterial, Antifungal, and Free Radical Scavenging Activities.

Cryptomeria japonica wood industry generates large amounts of foliage biomass residues. Due to the increasing applications and markets for essential oils (EOs), fresh Azorean C. japonica foliage (Az-CJF) residues are used for local EO production. Hydrodistillation (HD), a common process for obtaining EOs, also provides the possibility to fractionate them. Thus, this study evaluated the in vitro antimicrobial and antioxidant activities of six Az-CJF EO fractions (Frs. 1-6), collected at sequential HD timeframes (HDTs: 0-2, 2-10, 10-30, 30-60, 60-120, and 120-240 min), in comparison to the crude EO, obtained from a non-fractionated HD (0-240 min HDT). Antimicrobial activities were assessed via disc diffusion method against seven bacteria (foodborne and/or human pathogens) and two Penicillium spp. (phytopathogenic fungi), and antioxidant activity was estimated using DPPH and ABTS assays. Concerning the antibacterial activity, all the EO samples were effective only toward Gram-positive bacteria. Fractions 1-3 (<30 min HDT) were the most active, with growth inhibition zones (GIZ) of 7.0-23.3 mm (1.4-2.2 times higher than those of the crude EO), being Bacillus spp. (B. licheniformis and B. subtilis) the most sensitive, followed by Staphylococcus aureus and Micrococcus luteus. Regarding the antifungal activity, Frs. 1-3 also displayed the best activities, but only against P. italicum (GIZ around 9.0 mm), while the crude EO showed no antifungal activity. Overall, the best antimicrobial properties of Frs. 1-3 could be attributed, at least in part, to their highest content in α-pinene and bornyl acetate. On the other hand, Frs. 4-6 (>30 min HDT) exhibited the strongest antioxidant activities (EC50 values: 1.5-2.3 and 1.0-1.7 mg mL-1 for DPPH and ABTS, respectively), being at least 1.3-fold higher than those of the crude EO. The presence of nezukol, elemol, and eudesmol isomers could strongly contribute to the best free radical scavenging properties of Frs. 4-6. In conclusion, HD was found to be an efficient process for obtaining new Az-CJF EO fractions with variable and enhanced bioactivities due to their differential composition, as assessed using GC-MS. Hence, these findings could contribute to increasing the commercial potential of the C. japonica EO industry, namely, the Fr2 and Fr6, which presented the most significant activities and can have potential applications in the food, medical, and agriculture sectors.

Aerobic methane production in Scots pine shoots is independent of drought or photosynthesis.

Shoot-level emissions of aerobically produced methane (CH4) may be an overlooked source of tree-derived CH4, but insufficient understanding of the interactions between their environmental and physiological drivers still prevents the reliable upscaling of canopy CH4 fluxes. We utilised a novel automated chamber system to continuously measure CH4 fluxes from the shoots of Pinus sylvestris (Scots pine) saplings under drought to investigate how canopy CH4 fluxes respond to the drought-induced alterations in their physiological processes and to isolate the shoot-level production of CH4 from soil-derived transport and photosynthesis. We found that aerobic CH4 emissions are not affected by the drought-induced stress, changes in physiological processes, or decrease in photosynthesis. Instead, these emissions vary on short temporal scales with environmental drivers such as temperature, suggesting that they result from abiotic degradation of plant compounds. Our study shows that aerobic CH4 emissions from foliage are distinct from photosynthesis-related processes. Thus, instead of photosynthesis rates, it is more reliable to construct regional and global estimates for the aerobic CH4 emission based on regional differences in foliage biomass and climate, also accounting for short-term variations of weather variables such as air temperature and solar radiation.

A Compatible Estimation Method for Biomass Factors Based on Allometric Relationship: A Case Study on Pinus densata Natural Forest in Yunnan Province of Southwest China

Using various biomass factors, such as biomass expansion factor (BEF) and biomass conversion and expansion factor (BCEF), yields different results for estimating forest biomass. Therefore, ensuring compatibility between total biomass and its components when employing different biomass factors is crucial for developing a set of rapid and efficient models for large-scale biomass calculation. In this study, allometric equations were utilized to construct independent models and the proportional values (root-to-shoot ratio (Rra), crown-to-stem ratio (Rcs), bark-to-wood ratio (Rbw), foliage-to-bark ratio (Rfb), and wood biomass-to-wood volume (ρ)) by using the mean height (Hm) and the mean diameter at breast height (Dg) of 98 Pinus densata plots in Shangri-La, Yunnan province, China. The compatible methods were applied to reveal the compatibility between the total biomass and each component’s biomass. The results showed the following: (1) Both the independent model and compatible model had a higher accuracy. The values were greater than 0.7 overall, but the foliage biomass accuracy was only 0.2. The total biomass and the component biomass showed compatibility. (2) The accuracy of BEF and BCEF exceeded 0.87 and the total error was less than 0.1 for most components. (3) The mean BEF (1.6) was greater than that of the Intergovernmental Panel on Climate Change (IPCC) (M = 1.3), and the mean BCEF was smaller than that of the IPCC; the values were 0.6 and 0.7, respectively. The range of BEF (1.4–2.1) and BCEF (0.44–0.89) were all within the range of the IPCC (1.15–3.2, 0.4–1.0). This study provides a more convenient and accurate method for calculating conversion coefficients (BEF and BCEF), especially when only Rcs data is available.

Responses of non-structural carbohydrates and biomass in plant to heavy metal treatment

The contamination of heavy metals profoundly impacts plant metabolic processes and various physiological indicators, such as non-structural carbohydrates (NSC). However, a comprehensive understanding of how NSC in plants respond to heavy metal treatment and how different experimental setting and plant types affect the response of plant NSC is still lacking. Here, we compiled data of 2084 observations of NSC from 85 published studies and conducted a meta-analysis to investigate the responses of soluble sugars, starch, the ratio of soluble sugar to starch, and total non-structural carbohydrates (TNSC) to heavy metal treatment. Our results showed that, under heavy metal treatment, foliar soluble sugars, foliar TNSC, and the ratio of soluble sugars to starch in both foliage and root increased significantly by 21.6 %, 11.6 %, 55.9 %, and 65.1 %, respectively; and foliar starch, root starch, and root TNSC decreased significantly by 10 %, 23.3 %, and 11 %, respectively; while root soluble sugars remained unchanged. The treatment of heavy metals significantly diminished the biomass of foliage, above-ground, and root by 12.3 %, 29.5 %, and 34.3 %, respectively. The responses of foliar NSC to heavy metal treatment were strongly dependent on leaf habit, the duration and concentration of heavy metal treatment, and soil pH value. The magnitude of the response of NSC to heavy metals increased with the duration and concentration of heavy metal treatment. Furthermore, the types of heavy metals modulated the magnitude of the response of foliar NSC to heavy metal treatment. Overall, our findings provide valuable insights into the responses of plant NSC to heavy metal stress and contribute to a comprehensive understanding of this crucial aspect of plant physiology.

Spatial Effects Analysis on Individual-Tree Aboveground Biomass in a Tropical Pinus kesiya var. langbianensis Natural Forest in Yunnan, Southwestern China

It is essential to analyze the spatial autocorrelation and heterogeneity of aboveground biomass (AGB). But it is difficult to accurately describe due to the lack of data in clear-cutting plots. Thus, measuring the AGB directly in a clear-cutting plot can provide a reference for accurately describing the spatial variation. Therefore, a 0.3-hectare clear-cutting sample plot of Pinus kesiya var. langbianensis natural forest was selected, and the AGB was calculated by each component. The intra-group variance was quantitatively described in terms of spatial heterogeneity, and the spatial autocorrelation was explored by global and local Moran’s I. The results indicated that (1) there was different spatial heterogeneity for the different trees and organs. The intra-group variance tended to be stable after 20 m for P. kesiya var. langbianensis (PK) and other upper trees (UPs) and after 10 m for the other lower trees (LTs). (2) The spatial autocorrelation of AGB and wood biomass was similar, while the bark biomass and foliage biomass were consistent. PK and other UPs also exhibited strong spatial autocorrelation, with maximum Moran’s I values of 0.1537 and 0.1644, respectively. (3) There was spatial heterogeneity in the different components except for the bark of PK. The lowest spatial heterogeneity was found for LT.

Effects of Competition on the Vertical Distribution of Foliage Biomass within the Crowns of Planted Korean Pine Trees in Northeast China

The characteristics and distribution of foliage biomass are important aspects of the crown structures of trees. In this study, we developed a foliage biomass distribution model at the branch level and annual shoot level (i.e., all branches on an annual increment of the main tree bole) using a sample of 51 Korean pine trees that were destructively sampled in Liaoning Province, northeast China. A variety of independent variables, including branches, trees, and forest competition, were used as explanatory variables in the model development. Our results indicated that foliage biomass at the branch level increased with an increasing relative distance from the tree tip into the crown. The peak of the foliage biomass distribution shifted to the tree tip with increasing tree age. A power function, including branch diameter and competition variables, showed excellent performance in modeling branch foliage at the branch level. The final model, which included branch diameter, competition variables, and relative distance to branch base for the relative foliage density, showed best performance. The trees that showed advantageous growth ability also had a larger amount of foliage biomass.

Assessment of the Use of Common Juniper (Juniperus communis L.) Foliage following the Cascade Principle.

Juniperus communis L. is a species commonly grown in regions of the Northern Hemisphere, and is a good candidate to be cultivated in marginal lands. Plants coming from a pruning performed in a natural population located in Spain were used to assess the yield and quality of different products obtained following the cascade principle. A total of 1050 kg of foliage biomass were crushed, steam-distilled, and separated into fractions to produce biochar and absorbents for the pet industry using pilot plants. The obtained products were analysed. The essential oil, with a yield of 0.45% dry basis and a qualitative chemical composition similar to that described for the berries in international standards or monographs, showed antioxidant activity with promising CAA results (inhibition of 89% of the cell's oxidation). However, regarding antibacterial and antifungal activities, it only inhibited the growth of microorganisms at the maximum concentration tested, 2.5%. Concerning the hydrolate, it did not show bioactivity. Regarding the biochar, whose yield was 28.79% dry basis, interesting results were obtained for its characterisation as a possible soil improver for agronomic purposes (PFC 3(A)). Finally, promising results were obtained regarding the use of common juniper as absorbent, taking into account the physical characterisation and odour control capacity.

A spatially explicit model of tree leaf litter accumulation in fire maintained longleaf pine forests of the southeastern US

The continuity and depth of litter fuelbeds are major drivers of fire spread and fuel consumption. However, no established approach is available for the spatially explicit prediction of litter loads over large areas. Local fuel heterogeneity introduces large uncertainties on estimates derived from field-based models based on the extrapolation of sparse data samples. In fire-maintained pine forests of the southeastern US, litter accumulation and its distribution over the forest floor are mainly driven by vegetation productivity and by the number of years since the last fire (YSF). Some ecological models that simulate fire effects allow for a time-dependent estimation of litter by accounting for the opposing rates of litter deposition and decomposition as a function of YSF at the landscape level, but they do not account for spatial heterogeneity. We developed a conceptually simple approach for wall-to-wall estimation of tree leaf litter loads at high spatial resolution (5 m). The approach involved, first, estimating spatial patterns of tree annual litterfall. We mapped individual tree crowns through segmentation of airborne laser scanning (ALS) data, and we estimated crown foliage biomass using tree inventory data and ALS derived tree crown attributes. Tree annual litterfall was calculated as a fraction of the crown foliage biomass based on leaf turnover rates. We then quantified tree leaf litter accumulation through a spatially explicit implementation of the established Olson (1963) accumulation and negative decay model. We tested and validated our model in several management and research units at Eglin Air Force Base (Florida), Pebble Hill Plantation (Georgia), and Osceola National Forest (Florida), where managers maintain predominantly longleaf pine forests using frequent fire. Pixel-level RMSD and BIAS between tree leaf litter biomass estimated by the proposed model and reference field measurements were 0.21 and 0.01 kg m−2, and area-level RMSD and BIAS were 0.09 and 0.01 kg m−2. We concluded that linking patterns of litterfall and tree leaf litter accumulation to tree crown objects provides a means to characterizing the discontinuity of the litter layer, accounting for spatial heterogeneity largely traceable to tree crown foliage inputs.

Crown-Level Structure and Fuel Load Characterization from Airborne and Terrestrial Laser Scanning in a Longleaf Pine (Pinus palustris Mill.) Forest Ecosystem

Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (&lt;2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management.

Construction and precision analysis of individual tree biomass model of Larix olgensis considering random effects

Accurate estimation of forest biomass in China is crucial for the study of carbon cycle and mechanisms underlying carbon storage in global terrestrial ecosystems. Based on the biomass data of 376 individuals of Larix olgensis in Heilongjiang Province, we used seemingly unrelated regression (SUR) method to build a univariate biomass SUR model with diameter at breast height as the independent variable and considering the random effect at the sampling site level. Then, a seemingly unrelated mixed effect (SURM) model was constructed. As the calculation of random effects of SURM model did not require the empirically measured values of all dependent variables, we analyzed the deviations from the following four types in detail: 1) SURM1, the random effect was calculated according to the measured biomass of stem, branch and foliage; 2) SURM2, the random effect was calculated according to the measured value of tree height (H); 3) SURM3, the random effect was calculated according to the measured crown length (CL); 4) SURM4, the random effect was calculated according to the measured values of H and CL. The results showed that the fitting effect of branch and foliage biomass models was improved significantly after considering the horizontal random effect of the sampling plot, with R2 being increased by more than 20%. The fitting effect of stem and root biomass models were improved slightly, with R2 being increased by 4.8% and 1.7%, respectively. When using five randomly selected trees to calculate the horizontal random effect of the sampling plot, the prediction performance of SURM model was better than that of SUR model and SURM model considering only fixed effects, especially SURM1 model (MAPE% of stem, branch, foliage and root was 10.4%, 29.7%, 32.1% and 19.5%, respectively). Except for SURM1 model, the deviation of SURM4 in predicting stem, branch, foliage and root biomass was smaller than that of SURM2 and SURM3 models. In actual prediction, although the prediction accuracy of SURM1 model was the highest, it needed to measure aboveground biomass of several trees, and the use cost was relatively high. Therefore, the SURM4 modelled on measured H and CL was recommended to predict the standing tree biomass of L. olgensis.

Linking frass and insect phenology to optimize annual forest defoliation estimation

It is often logistically impractical to measure forest defoliation events in the field due to seasonal variability in larval feeding phenology (e.g., start, peak, and end) in any given year. As such, field data collections are either incomplete or at coarse temporal resolutions, both of which result in inaccurate estimation of annual defoliation (frass or foliage loss). Using Choristoneura pinus F. and Lymantria dispar dispar L., we present a novel approach that leverages a weather-driven insect simulation model (BioSIM) and defoliation field data. Our approach includes optimization of a weighting parameter (w) for each instar and imputation of defoliation. Results show a negative skew in this weighting parameter, where the second to last instar in a season exhibits the maximum consumption and provides better estimates of annual frass and foliage biomass loss where sampling data gaps exist. Respective cross-validation RMSE (and normalized RMSE) results for C. pinus and L. dispar dispar are 77.53 kg·ha−1 (0.16) and 38.24 kg·ha−1 (0.02) for frass and 74.85 kg·ha−1 (0.10) and 47.77 kg·ha−1 (0.02) for foliage biomass loss imputation. Our method provides better estimates for ecosystem studies that leverage remote sensing data to scale defoliation rates from the field to broader landscapes and regions.•Utilize fine temporal resolution insect life cycle data derived from weather-driven insect simulation model (BioSIM) to bridge critical gaps in coarse temporal resolution defoliation field data.•Fitting distributions to optimize the instar weighting parameter (w) and impute frass and foliage biomass loss based on a cumulative density function (CDF).•Enables accurate estimation of annual defoliation impacts on ecosystems across multiple insect taxa that exhibit distinct but seasonally variable feeding phenology.

Разработка модели биомассы Picea L. и Abies L. для региональных условий Евразии

Forest ecosystems play a major role in climate stabilization, and continuous monitoring of their biomass is of paramount importance. Airborne laser scanning technology has become widespread in assessing tree biomass by remote sensing of such inventory indices of trees and stands as crown width and projection area, tree and stand height. The work uses the author’s observation database of 1550 model trees of spruce (Picea L.) and 535 model trees of fir (Abies L.) growing in Eurasia. The database provides information on tree height, crown width and length, as well as biomass of trunk, foliage, branches and roots. It was found that two-factor allometric models of biomass components, including crown width and tree height as independent variables, are the most informative. A positive correlation with crown width and tree height has been identified for all biomass components. Biomass of components of similar-sized fir trees was found to be 45–71 % higher than that of spruce at the statistically reliable level. This is possibly due to the fact that with the same height the crown width of the fir is 11 % less compared to the spruce. The contribution of winter temperatures and precipitation to the variability of biomass components was estimated. The biomass of all components of equal-sized trees is described by a propeller-like 3D dependence. In warm regions, the biomass increases with increasing precipitation; the dependence is characterized by an opposite or neutral trend with the transition to cold regions. The biomass increases with increasing temperature in humid regions, and decreases with the transition to dry climatic conditions. Inclusion of crown length as an additional independent variable in the allometric model practically did not improve its predictive ability. The contribution of inventory indices of trees, their species, and climatic variables to explaining the variability of biomass components is 72.9, 13.5 and 13.6 %, respectively. Climatic variables explain most of the variability in trunk and above-ground biomass (18 to 20 %), least of all in needles and branches (8 to 9 %). The findings can be useful for laser monitoring of forest biomass and predicting possible changes in tree biomass structure in case of climatic deviations. Acknowledgments: The research was carried out within the framework of the state assignment FEUZ-2021-0014. For citation: Usoltsev V.А., Tsepordey I.S., Danilin I.M. Designing a Model of the Picea L. and Abies L. Biomass for Regional Climatic Conditions in Eurasia. Lesnoy Zhurnal = Russian Forestry Journal, 2022, no. 6, pp. 38–54. (In Russ.). https://doi.org/10.37482/0536-1036-2022-6-38-54

Growth characteristics of Cunninghamia lanceolata in China

Chinese fir (Cunninghamia lanceolata) is one of southern China's most important native tree species, which has experienced noticeable climate-induced changes. Published papers (1978–2020) on tree growth of Chinese fir forests in China were collected and critically reviewed. After that, a comprehensive growth data set was developed from 482 sites, which are distributed between 102.19° and 130.07°E in longitude, between 21.87° and 37.24°N in latitude and between 5 and 2260 m in altitude. The dataset consists of 2265 entries, including mean DBH (cm), mean H (m), volume (m3), biomass (dry weight) (kg) (stem (over bark) biomass, branches biomass, leaves biomass, bark biomass, aboveground biomass, roots biomass, total trees biomass) and related information, i.e. geographical location (Country, province, study site, longitude, latitude, altitude, slope, and aspect), climate (mean annual precipitation-MAP and mean annual temperature-MAT), stand description (origin, age, canopy density and stand density), and sample regime (plot size, number and investigation year). Our results showed that (1) the best prediction of height was obtained using nonlinear composite model Height = 1.3 + 34.23*(1 - {text{e}}^{{left( { - 0.01025*{text{DBH}}^{1.347} } right)}} ), (R2 = 0.8715, p < 0.05), (2) the equation Volume = DBH2/(387.8 + 19,190/Height) (R2 = 0.9833, p < 0.05) was observed to be the most suitable model for volume estimation. Meanwhile, when the measurements of the variables are difficult to carry out, the volume model Volume = 0.03957 − 0.01215*DBH + 0.00118*DBH2 (R2 = 0.9573, p < 0.05) is determined from DBH only has a practical advantage, (3) the regression equations of component biomass against DBH explained more significant than 86% variability in almost all biomass data of woody tissues, which were ranked as total trees (97.25%) > aboveground (96.55%) > stems (with bark) (96.17%) > barks (88.95%) > roots (86.71%), and explained greater than 64% variability in branch biomass. The foliage biomass equation was the poorest among biomass components (R2 = 0.6122). The estimation equations derived in this study are particularly suitable for the Chinese fir forests in China. This dataset can provide a theoretical basis for predicting and assessing the potential of carbon sequestration and afforestation activities of Chinese fir forests on a national scale.

Additive Allometric Equations to Improve Aboveground Biomass Estimation for Mongolian Pine Plantations in Mu Us Sandy Land, Inner Mongolia, China

Afforestation is conducive to improving ecosystem service functions and ecosystem diversity in the Mu Us Sandy Land, however, the important attribute of biomass for Mongolian pine (Pinus sylvestris var. mongolica Litv.) plantations has yet to be accurately evaluated. This study aimed to develop additive allometric biomass equations for the species and evaluate biomass partitioning patterns within tree components. A total of 131 trees were measured for stem, branch, and leaf biomass by destructively sampling and tree climbing, with the latter as a supplement. For each biomass component, we tested three equations with the diameter at breast (D) alone, height (H) as additional, and diameter in combination with height (D2H) as predictors using the weighted least squared method. Weighted nonlinear seemingly unrelated regression was adopted to fit a system of additive allometric biomass equations utilizing the selected equations. A leave-one-out cross-validation method (the jackknife procedure) was used to assess the predictive ability. The biomass partitioning pattern was evaluated by calculating the ratios. The results revealed that the diameter alone is a good predictor for branches and foliage biomass estimates, while the stem requires H included to improve estimation accuracy. Mongolian pine allocates relatively more biomass to the crown (51.4%) compared to the stem (48.6%). Branch biomass fraction increased monotonously with increasing tree size while a reverse trend was observed for foliage. In conclusion, the additive models developed in this study provide a robust biomass estimation and can be extensively used to estimate Mongolian pine forests biomass in Mu Us Sandy Land.

Determination of foliar traits in an ecologically distinct conifer species in Maine using Sentinel-2 imagery and site variables: Assessing the effect of leaf trait expression and upscaling approach on prediction accuracy

Leaf traits are expressed either as area-based content (g m−2) or mass-based concentration (mg/g or %) and subsequently scaled to canopy level using leaf area index (LAI) and foliage biomass, respectively. Despite their wide use, it remains unclear whether the two trait expressions and scaling options (by LAI or foliage biomass) yield the same prediction accuracy using multispectral remote sensing data and additional site variables. In this study, we used LAI (m2 m−2) and foliage biomass (kg) to upscale area-based content [leaf mass per area (LMA), equivalent water thickness (EWT), nitrogen (Narea) and potassium (Karea)] and mass-based concentration [leaf dry matter content (LDMC), leaf water content (LWC), Nmass, and Kmass)], respectively in two contrasting study sites (coastal vs inland) for an ecologically important yet distinct regional species (red spruce, Picea rubens Sarg.) in Maine, USA. We used partial least squares regression (PLSR) and random forest (RF) to examine the effect of these metrics on model performance and prediction accuracy at both leaf- and canopy-scales using Sentinel-2 satellite bands, vegetation indices, as well as several topographical variables and depth to water table (DWT). The best-performing models (based on cross-validated normalized root mean square error: nRMSE) were used to generate high-resolution canopy trait maps. Results demonstrated that prediction accuracies of area-based leaf traits were generally higher (R2 = 0.41–0.65, nRMSE = 0.14–0.20) than the mass-based concentration (R2 = 0.42–0.65, nRMSE = 0.15–0.25) for both sites and modelling techniques. We also observed that canopy traits upscaled by LAI yield better prediction accuracies (R2 = 0.44–0.70, nRMSE = 0.17–0.20) compared to a foliage biomass upscaling approach (R2 = 0.41–0.57, nRMSE = 0.19–0.24). Among site variables, DWT was a significant variable in modeling canopy traits and showed robust behavior across the contrasting study sites. Overall, our study highlights the importance of trait expression and how it affects foliar trait retrieval accuracy at both leaf- and canopy-scale using satellite multispectral data and site variables. These findings have implications on leaf trait modelling especially in the context of monitoring dynamics in forest health and productivity from space.

An allometric model-based approach for estimating biomass in seven Indian bamboo species in western Himalayan foothills, India

The rapid growth rate, high biomass production, and annual harvesting make bamboo a suitable species for commercial production. Allometric equations for many broadleaf and conifer tree species are available. However, knowledge of biomass production and allometric equations of bamboos is limited. This study aims to develop species- specific allometric models for predicting biomass and synthetic height values as a proxy variable for seven bamboo species in Himalayan foothills. Two power form-based allometric models were used to predict aboveground and culm biomass using diameter at breast height (D) alone and D combined with culm height (H) as an independent variable. This study also extended to establishing an H–D allometric model that can be used to generate synthetic H values as a proxy to missing H. In the seven bamboo species studied, among three major biomass components (culm, branch and foliage), culm is the most important component with the highest share (69.56–78.71%). The distribution of percentage (%) share of culm, branch and foliage to above-ground fresh weight varies significantly between different bamboo species. D. hamiltonii has the highest productivity for above-ground biomass components. Ratio of dry to fresh weight of seven bamboo species was estimated for culm, branch, foliage and above-ground biomass to convert fresh weight to dry weight.

Sapwood growth and tolerance of Pinus taeda trees to Leptographium terebrantis inoculation

AbstractLeptographium terebrantis is an opportunistic root pathogen that has been implicated as a contributing factor of Pinus taeda decline and mortality over the past several decades in central parts of Alabama and Georgia, USA. We assessed the potential of L. terebrantis to initiate crown thinning in young P. taeda trees and hypothesized that L. terebrantis infestation will impose moisture stress on foliage, induce premature senescence, and cause loss of foliage biomass. The study was undertaken in a naturally regenerated 5–7‐year‐old P. taeda stand at Andalusia, Alabama in a completely randomized design using artificial inoculations of L. terebrantis colonized toothpicks. After four years of infestation, the pathogen caused sapwood occlusions and loss of sapwood function but failed to impose moisture stress on needles to induce premature senescence and loss of biomass. The new sapwood formed around the occluded area was devoid of pathogen infestation. The new growth was approximately twice the size of occluded tissue and compensated for the loss of old sapwood function to sustain tree growth. Results demonstrated that young P. taeda trees can tolerate L. terebrantis infection when stand conditions sustain the formation of new sapwood.

The effect of growth conditions on the relationship between fine root and foliage biomass

The effect of growth conditions on the relationship between fine root and foliage biomass

Changes in foliage biomass of the genera Larix and Pinus along gradients of temperature and precipitation in Eurasia

Changes in foliage biomass of the genera Larix and Pinus along gradients of temperature and precipitation in Eurasia