Biomass Model Research Articles

Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm

Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas.

Aboveground biomass allocation and additive allometric models of fifteen tree species in northeast China based on improved investigation methods

Tree allometric models are crucial to accurate forest biomass assessment, but the acquisition of biomass data is one of the most important limitations in establishing biomass models. This study aims to propose an improved investigation method to quickly and practically obtain biomass data, establish species-specific allometric equations and evaluate the biomass allocation pattern for various tree species in a broad-leaved Korean pine forest in northeast China. Based on the non-destructive measurement and sampling of 390 trees from 15 tree species, partial and aggregate biomass models with DBH and DBH2 × H as predictors were established. Results indicated that all allometric models could effectively estimate the dependent variables, and almost no antilog correction was required. Specifically, for the stem and bark components of most tree species, using the DBH2 × H predictor improved the prediction performance of the model. However, DBH was the best predictor for branch and leaf biomass models. The biomass allocation of trees was affected by tree diameter. Small-diameter trees (DBH < 8) had more leaf investment than other size trees, medium-diameter trees (8 < DBH < 20) increased the allocation of resources to stems, and large-diameter trees (DBH > 20) paid more attention to the growth and accumulation of branches. Our allometric model and biomass allocation research based on the non-destructive measurement and sampling will help improve the accuracy of biomass assessment in broad-leaved Korean pine forests and advance our understanding of woody plant development and survival strategies.

Dynamic Model for Biomass and Proteins Production by Three Bacillus Thuringiensis ssp Kurstaki Strains

Bacillus thuringiensis is a microorganism used for the production of biopesticides worldwide. In the present paper, different kinetic models were analyzed to study and compare three different strains of Bt ssp kurstaki (LIP, BLB1, and HD1). Bioperformances (vegetative cell, spore, substrate, and protein) and successive culture phases (oxidative growth, limitation and sporulation, and protein release) were depicted with an overarching aim to estimate total protein productivity, yield, and titer. In the end, two models were calibrated using experimental dataset (11 batches culture in 3 L bioreactor with semisynthetic medium), subsequently validated, and statistically compared. Both models satisfactorily followed the dynamics of the experimental data. Finally, a dynamic model was selected following the Akaike information criterion (AIC).

Carbon matrix of biochar from biomass modeling components facilitates electron transfer from zero-valent iron to Cr(VI).

Biochar-harbored zero-valent iron (ZVI/BC) has been extensively used to detoxify hexavalent chromium (Cr(VI)). However, the role played by biochar in promoting electron transfer of ZVI and Cr(VI) reduction was not fully uncovered. Herein, three biomass modeling components (cellulose, hemicellulose, and lignin) and their blends were utilized to synthesize ZVI/BC via co-pyrolysis with hematite. X-ray diffraction analysis showed that hematite was successfully reduced to ZVI in nitrogen ambience. Batch sorption experiment showed that mass ratio (hematite to lignocellulosic component) of 1:20 is most optimal for reduction of Cr(VI) by ZVI/BCs. ZVI supported by BC derived from cellulose, hemicellulose, and their binary mixture demonstrated better Cr(VI) removal capacity (23.8-38.3mgg-1) owing to higher ordered and graphitic carbon structure as revealed by Raman spectrum. In addition, lower Tafel corrosion potentials and smaller electrochemical impedance arc radiuses were observed based on electrochemical analysis, suggesting their higher electrical conductivity and faster electron transfer, whereas the BCs derived from lignin and lignin-containing hybrids were not conducive to electron transfer of ZVI due to lower degree of graphitization, thus compromising Cr(VI) removal by ZVI/BC (7.7-17.7mgg-1). As per X-ray photoelectron spectroscopy analysis, reduction, complexation, and co-precipitation were the main mechanisms for Cr(VI) removal. The present study provided a scientific evidence for screening plant-derived biomass feedstock with high contents of cellulose and hemicellulose and low lignin content to fabricate ZVI/BC to achieve high Cr(VI) removal.

Accommodating heteroscedasticity in allometric biomass models

Allometric models are commonly used to predict forest biomass. These models typically take nonlinear power-law forms that predict individual tree aboveground biomass (AGB) as functions of diameter at breast height (D) and/or tree height (H). Because the residual variance is in most cases heteroscedastic, accommodating the heteroscedasticity (i.e., heterogeneity of variance) becomes necessary when estimating model parameters. We tested several weighting procedures and a logarithmic transformation for nonlinear allometric biomass models. We further evaluated the effectiveness of these procedures with emphasis on how they affected estimates of mean AGB per hectare and their standard errors for large forest areas. Our results revealed that some weighting procedures were more effective for accommodating heteroscedasticity than others and that effectiveness was greater for single predictor models but less for models based on both D and H. Failing to effectively accommodate heteroscedasticity produced small to moderate differences in the estimates of mean AGB per hectare and their standard errors. However, these differences were greater between model forms (models based on D and H versus models based on D only), regardless of the weighting approach. Similar consequences were observed with respect to whether model prediction uncertainty was or was not included when estimating mean AGB per hectare and standard errors. When including model prediction uncertainty, the standard errors of the estimated means increased substantially, by 44–59%. Therefore, to avoid possible negative consequences on large-area biomass estimation, we recommend: (i) testing the effectiveness of a weighting procedure when accommodating heteroscedasticity in allometric biomass models, (ii) incorporating model prediction uncertainty in the total uncertainty estimate and (iii) including H as an additional predictor variable in allometric biomass models.

Energy Stored in Above-Ground Biomass Fractions and Model Trees of the Main Coniferous Woody Plants

The paper considers energy stored in above-ground biomass fractions and in model trees of the main coniferous woody plants (Picea abies (L.) H. Karst., Abies alba Mill., Pinus sylvestris (L.), Larix decidua Mill.), sampled in 22 forest stands selected in different parts of Slovakia. A total of 43 trees were felled, of which there were 12 spruces, 11 firs, 10 pines, and 10 larches. Gross and net calorific values were determined in samples of wood, bark, small-wood, twigs, and needles. Our results show that these values significantly depend on the tree species, biomass fractions, and sampling point on the tree. The energy stored in the model trees calculated on the basis of volume production taken from yield tables increases as follows: spruce &lt; fir &lt; pine &lt; larch. Combustion of tree biomass releases an aliquot amount of a greenhouse gas—CO2, as well as an important plant nutrient, nitrogen—into the atmosphere. The obtained data must be taken into account in the case of the economic utilization of energy stored in the fractions of above-ground tree biomass and in whole trees. The achieved data can be used to assess forest ecosystems in terms of the flow of solar energy, its accumulation in the various components of tree biomass, and the risk of biomass combustion in relation to the release of greenhouse gases.

Mathematical Modelling in Crop Production to Predict Crop Yields

In this study, for remote recognition of crops of agroecosystems in Kazakhstan by methods of comparative and historical analogy with the active use of mathematical modelling, the yield indicator of agricultural crops was determined, their dynamic characteristics were studied to predict productivity. The parameters of the dynamic-statistical biomass model were determined separately for each region of the Republic of Kazakhstan based on training data for 21 y (2000 – 2021). The correlation coefficient between the calculated yield values and the official statistics is 0.84. According to the results of cross-validation, the correlation coefficient between the actual and predicted yield of spring wheat was ~ 0.70, which indicates a sufficient resistance of the model to the variability of meteorological conditions for the formation of the crop.

Biomass Estimation, Nutrient Content, and Decomposition Rate of Shoot Sheath in Moso Bamboo Forest of Yixing Forest Farm, China

The biomass, nutrient content and decomposition rate of shoot sheaths remain unexplored in the study of Moso bamboo forests. The rapid growth of shoots means many bamboo sheaths are produced each year, and therefore should not be neglected in the study of the Moso bamboo ecosystem. In our study, we selected 160 bamboo shoots of different sizes in Yixing forest farm, Jiangsu Province. Our analysis was based on the allometric growth equation, using diameter at breast height (DBH), internode length of bamboo at breast height (IL), and bamboo height (H) as independent variables to establish the biomass model of shoot sheaths using all samples. In addition, we also measured the nutrient content of shoots and estimated the decomposition rate of shoots by setting up litter decomposition bags. Our results found that logarithmic regression should be used to fit the biomass model of shoot sheaths. From the perspective of practical application, model W3 fitting DBH and IL was determined. The order of the nutrient elements in the shoot sheath is C &gt; N &gt; K &gt; P. Decomposition tests showed that it took 0.47 years for 50% of sheaths to decompose, and 3.15 years for all sheaths to decompose.

Modeling of rice crop biomass using Sentinel-1 backscatter coefficients: A case study over Nawagam, Gujarat

Modeling of rice crop biomass using Sentinel-1 backscatter coefficients: A case study over Nawagam, Gujarat

Using TLS-Measured Tree Attributes to Estimate Aboveground Biomass in Small Black Spruce Trees

Research Highlights: This study advances the effort to accurately estimate the biomass of trees in peatlands, which cover 13% of Canada’s land surface. Background and Objectives: Trees remove carbon from the atmosphere and store it as biomass. Terrestrial laser scanning (TLS) has become a useful tool for modelling forest structure and estimating the above ground biomass (AGB) of trees. Allometric equations are often used to estimate individual tree AGB as a function of height and diameter at breast height (DBH), but these variables can often be laborious to measure using traditional methods. The main objective of this study was to develop allometric equations using TLS-measured variables and compare their accuracy with that of other widely used equations that rely on DBH. Materials and Methods: The study focusses on small black spruce trees (&lt;5 m) located in peatland ecosystems of the Taiga Plains Ecozone in the Northwest Territories, Canada. Black spruce growing in peatlands are often stunted when compared to upland black spruce and having models specific to them would allow for more precise biomass estimates. One hundred small trees were destructively sampled from 10 plots and the dry weight of each tree was measured in the lab. With this reference data, we fitted biomass models specific to peatland black spruce using DBH, crown diameter, crown area, height, tree volume, and bounding box volume as predictors. Results: Our best models had crown size and height as predictors and outperformed established AGB equations that rely on DBH. Conclusions: Our equations are based on predictors that can be measured from above, and therefore they may enable the plotless creation of accurate biomass reference data for a prominent tree species in a common ecosystem (treed peatlands) in North America’s boreal.

Allometric equations for selected Acacia species (Vachellia and Senegalia genera) of Ethiopia

BackgroundAllometric equations are used to estimate biomass and carbon stock of forests. In Ethiopia, despite the presence of large floral diversity, only a few site-specific allometric equations have been developed so far. This study was conducted in the Omo-Gibe woodland of south-western Ethiopia to develop an allometric equation to estimate the Above-ground Biomass (AGB) of the four Acacia species (Senegalia polyacantha, Vachellia seyal, Vachellia etbaica and Vachellia tortilis). Fifty-four (54) Acacia trees were sampled and measured within 35 temporarily established square plots. In each plot, dendrometric variables were measured to derive the models based on combinations of Diameter at Breast Height (DBH), height, and wood density as predictor variables. Model performance was evaluated using goodness-of-fit statistics. The biomass was compared using four allometric biomass models that have been widely used in the tropics.ResultsThe model containing DBH alone was more accurate to estimate AGB compared to the use of multiple predictor variables. This study, therefore, substantiated the importance of site-specific allometric equations in estimating the AGB of Acacia woodlands. This is because a site-specific allometric equation recognizes the environmental factors, vegetation types and management practices.ConclusionsThe results of this study contribute to a better understanding of allometric equations and an accurate estimate of AGB of Acacia woodlands in Ethiopia and similar ecosystems elsewhere.

A fractional-order fall armyworm-maize biomass model with naturally beneficial insects and optimal farming awareness

Maize remains an important food crop in Africa. However, the production of this crop, and consequently the livelihood of the growers are threatened by the invasion and widespread infestation of the fall armyworm which causes substantial maize yield losses. In this paper, a fractional-order fall armyworm-maize biomass model with naturally beneficial insects and optimal farming awareness has been formulated. Comprehensive analysis of the model has shown that it contains five equilibrium points which are all locally and globally asymptotically stable if the conditions outlined in Lemma 2.1 and 2.2 are met. We also carried out numerical simulations to support the analytical results and to illustrate different dynamical regimes that can be observed in the model. We have found that time-dependent farming awareness can significantly reduce fall armyworm population if the cost of implementation is relatively low.

Modeling and economic analysis of biomass and plastic conversion to power

AbstractThe viability of converting wood chips and plastic to power via compact integrated catalytic fast/slow pyrolysis, combustor, and Stirling engine technologies was determined using modeling analysis. Aspen simulations followed by sizing analysis were performed to determine the size, weight, and net energy efficiency of upgrading wood chips and plastic to power using a compact energy system with a feedstock processing capacity of 1 kg h−1. The equipment and tooling to fabricate individual components of a compact energy system were identified. The total costs of fabricating all the individual components of compact energy systems were assessed. A discounted cash flow analysis with an internal rate of return of 20% was used to assess the minimum selling price of compact energy systems. The specific energy (Wh kg−1) of the compact catalytic fast pyrolysis system, compact slow pyrolysis system, and compact plastic conversion system were assessed at 20, 29, and 63, respectively. The assessed capital and annual costs of manufacturing 10 000 compact energy systems per year were $51 million and about $11 to $13 million, respectively. The calculated minimum selling price of a catalytic fast pyrolysis system, a compact slow pyrolysis system, and a compact plastic conversion system were $3055, $3021, and $2980, respectively. The sensitivity analysis results showed that the internal rate of return, capital investment, materials cost, annual operating costs, and capital cost have significant impacts on the minimum selling price of compact energy systems. © 2021 Society of Chemical Industry and John Wiley &amp; Sons, Ltd

Allometric models for aboveground biomass of six common subtropical shrubs and small trees

The aboveground biomass (AGB) of shrubs and small trees is the main component for the productivity and carbon storage of understory vegetation in subtropical secondary forests. However, few allometric models exist to accurately evaluate understory biomass. To estimate the AGB of five common shrub (diameter at base < 5 cm, < 5 m high) and one small tree species (< 8 m high, trees’s seedling), 206 individuals were harvested and species-specific and multi-species allometric models developed based on four predictors, height (H), stem diameter (D), crown area (Ca), and wood density (ρ). As expected, the six species possessed greater biomass in their stems compared with branches, with the lowest biomass in the leaves. Species-specific allometric models that employed stem diameter and the combined variables of D2H and ρDH as predictors accurately estimated the components and total AGB, with R2 values from 0.602 and 0.971. A multi-species shrub allometric model revealed that wood density × diameter × height (ρDH) was the best predictor, with R2 values ranging from between 0.81 and 0.89 for the components and total AGB, respectively. These results indicated that height (H) and diameter (D) were effective predictors for the models to estimate the AGB of the six species, and the introduction of wood density (ρ) improved their accuracy. The optimal models selected in this study could be applied to estimate the biomass of shrubs and small trees in subtropical regions.

A Nonlinear Mixed-Effects Height-Diameter Model with Interaction Effects of Stand Density and Site Index for Larix olgensis in Northeast China

Tree height is a basic input variable in various forest models, such as growth and yield models, biomass models, and carbon budget models, which serve as very important tools for the informed decision-making in forestry. The height-diameter model is the most important component of the growth and yield models and forest simulators. We developed the nonlinear mixed-effects height-diameter model with the interaction effects of stand density and site index introduced using data from 765 Larix olgensis trees in Jingouling forest farm of the Wangqing Forest Bureau in northeast China. Among the various basic versatile functions evaluated, a simple exponential growth function fitted the data adequately well, and this was then expanded through the introduction of the variables describing the interaction effects of the stand density and site index on the height-diameter relationship. Sample plot-level random effects were included into this model through mixed-effects modeling. The results showed that the random effect of the stand density on the height-diameter relationship was substantially different at different classes of the site index, and the random effect of the site index was different for the different stand density classes. The nonlinear mixed-effects (NLME) height-diameter model coping with the interaction effects of the stand density and site index had a better performance than those of the NLME models with the random effect of the single variable of stand density or site index. To conclude, the inclusion of the interaction effects of stand density and site index could significantly improve the prediction accuracy of the height-diameter model for Larix olgensis Henry. The proposed model with the interactive random effects included can be applied for the accurate prediction of Larix olgensis tree height in northeast China.

The Effects of Combining the Variables in Allometric Biomass Models on Biomass Estimates over Large Forest Areas: A European Beech Case Study

Effective initiatives for forest-based mitigation of climate change rely on continuous efforts to improve the estimation of forest biomass. Allometric biomass models, which are nonlinear models that predict aboveground biomass (AGB) as a function of diameter at breast height (D) and tree height (H), are typically used in forest biomass estimations. A combined variable D2H may be used instead of two separate predictors. The Q-ratio (i.e., the ratio between the parameter estimates of D and parameter estimates of H, in a separate variable model) was proposed recently as a measure to guide the decision on whether D and H can be safely combined into D2H, being shown that the two model forms are similar when Q = 2.0. Here, using five European beech (Fagus sylvatica L.) biomass datasets (of different Q-ratios ranging from 1.50 to 5.05) and an inventory dataset for the same species, we investigated the effects of combining the variables in allometric models on biomass estimation over large forest areas. The results showed that using a combined variable model instead of a separate variable model to predict biomass of European beech trees resulted in overestimation of mean AGB per hectare for Q &gt; 2.0 (i.e., by 6.3% for Q = 5.05), underestimation for Q &lt; 2.0 (i.e., by –3.9% for Q = 1.50), whereas for Q = 2.03, the differences were minimum (0.1%). The standard errors of mean AGB per hectare were similar for Q = 2.03 (differences up to 0.2%), and the differences increased with the Q-ratio, by up to 10.2% for Q = 5.05. Therefore, we demonstrated for European beech that combining the variables in allometric biomass models when Q ≠ 2.0 resulted in biased estimates of mean AGB per hectare and of uncertainty.

1-08 種々のCa/Pを有するハイドロキシアパタイトを用いたNi担持触媒のバイオマスモデルタール低温分解活性

1-08 種々のCa/Pを有するハイドロキシアパタイトを用いたNi担持触媒のバイオマスモデルタール低温分解活性

Cryogenic land surface processes shape vegetation biomass patterns in northern European tundra

Tundra ecosystems have experienced changes in vegetation composition, distribution, and productivity over the past century due to climate warming. However, the increase in above-ground biomass may be constrained by cryogenic land surface processes that cause topsoil disturbance and variable microsite conditions. These effects have remained unaccounted for in tundra biomass models, although they can impact multiple opposing feedbacks between the biosphere and atmosphere, ecosystem functioning and biodiversity. Here, by using field-quantified data from northern Europe, remote sensing, and machine learning, we show that cryogenic land surface processes substantially constrain above-ground biomass in tundra. The three surveyed processes (cryoturbation, solifluction, and nivation) collectively reduced biomass by an average of 123.0 g m−2 (−30.0%). This effect was significant over landscape positions and was especially pronounced in snowbed environments, where the mean reduction in biomass was 57.3%. Our results imply that cryogenic land surface processes are pivotal in shaping future patterns of tundra biomass, as long as cryogenic ground activity is retained by climate warming.

Transformation of HCl during pyrolysis of biomass and its model compounds

The release behavior of chlorine (Cl) during biomass pyrolysis has not been fully understood yet due to the complex transformation of hydrogen chloride (HCl). Here, we systematically investigate the transformation of HCl in vapor phase and its interactions with pyrolyzing biomass/char particles during biomass pyrolysis. Four biomass model compounds (cellulose, xylan, pectin, and alkali lignin) and an acid-washed wood were pyrolyzed in a fixed-bed reactor at 300–600 ℃ to produce Cl-free volatiles that reacted with the HCl injected downstream of the reactor. To explore the vapor–solid interactions of HCl with pyrolyzing biomass/char particles, the acid-washed wood was also pyrolyzed in a HCl atmosphere. The role of potassium (K) in the transformation of HCl was revealed via pyrolyzing a wood loaded with potassium carbonate (K2CO3). For all the experiments, the Cl distributed in char, heavy oil (condensed at 110 ℃), light oil (condensed at 0 ℃), and pyrolytic gases (as CH3Cl) was quantified. The results demonstrate that the vapor-phase reactions of HCl with the volatiles from pectin, lignin, and the acid-washed wood generate considerable amounts of methyl chloride (CH3Cl) but do not contribute to organic Cl in bio-oil. Increasing the temperatures of both pyrolysis and vapor-phase reaction promotes the generation of CH3Cl. The vapor–solid interactions of HCl with pyrolyzing biomass/char particles substantially enhance the formation of CH3Cl during the pyrolysis of the acid-washed wood at 300–500 ℃ because of the possible combination of HCl with the methyl radicals released in the initial stage of lignin pyrolysis. In addition, the vapor–solid interactions cause 120–490 μg/g feedstock of water-soluble Cl and 410–1120 μg/g feedstock of water-insoluble Cl being retained in the chars, which are produced from the capture of HCl by pores and carbon active sites in chars, respectively. The addition of K2CO3 catalyzes the cracking of lignin and thereby enhances the methylation of HCl in both vapor-phase reactions and vapor–solid interactions. It also increases the retention of Cl in the chars due to its capture by K.

Central composite design for simultaneously optimizing biomass and lutein production by a mixotrophic Chlorella sorokiniana TH01

Central composite design (CCD) was adopted to statistically model algal biomass and lutein production by the Chlorolla sorokiniana TH01 strain on four variables of light intensity, sodium acetate, N/P ratio and CO2 in the mixotrophic mode. Results revealed that the biomass and lutein response surface models had high correlation coefficients (i.e., 98.8% and 98.2%) with the actual data. Moreover, data obtained from confirmatory experiments showed that the biomass and lutein content can be respectively reached maximum of 26.97 g/L and 8.47 mg/g under different optimal conditions. However, practical production could simultaneously achieve high levels of biomass of 26.21 g/L and lutein of 5.01 mg/g under the optimal light intensity of 75 µmol/(m2·s), sodium acetate of 10 g/L, N/P of 35 and CO2 of 2%. The C. sorokiniana TH01 strain was superior to other strains in both biomass and lutein production, demonstrating highly promising for commercial production of lutein.