Application of site-specific biomass models to quantify spatial distribution of stocks and historical emissions from deforestation in a tropical forest ecosystem

Allometric equations developed for the Lama forest, located in southern Benin, West Africa, were applied to estimate carbon stocks of three vegetation types：undisturbed forest, degraded forest, and fallow. Carbon stock of the undisturbed forest was 2.7 times higher than that in the degraded forest and 3.4 times higher than that in fallow. The structure of the forest suggests that the individual species were generally concentrated in lower diameter classes. Carbon stock was positively correlated to basal area and negatively related to tree density, suggesting that trees in higher diameter classes contributed significantly to the total carbon stock. The study demonstrated that large trees constitute an important component to include in the sampling approach to achieve accurate carbon quantification in forestry. Historical emissions from deforestation that converted more than 30% of the Lama forest into cropland between the years 1946 and 1987 amounted to 260,563.17 tons of carbon per year（t CO2/year） for the biomass pool only. The study explained the application of biomass models and ground truth data to estimate reference carbon stock of forests.

Total Volume and Aboveground Biomass Models for <i>Juniperus procera</i>Plantation in Wondo Genet, Southern Ethiopia

Species specific allometric equations are important for estimation and quantification of net volume and aboveground biomass of living trees. This study was basically focused on fitting total volume and aboveground biomass models for Juniperus procera plantations in Wondo Genet, Sidama Zone, Ethiopia. Data for fitting the total volume and aboveground biomass models were obtained by destructively sampling of trees from the ten diameter classes of the Juniperus procera plantation in the study area. A total of one hundred ten and fifty-one trees were destructively sampled to fit six total volume and six aboveground biomass models respectively. After important measurements of parameters have completed, model performance evaluation and selecting of best fit models were undertaken using standard error of estimates (SEE), coefficient of determination (R2), bias (B) and mean of the absolute value of errors (MAE). Accordingly, the total volume model Vt = -5.466 + 0.959Dbh0.005H003 and aboveground biomass model of B = 0.348Dbh0.57H0.032 were found to be the best predictive models for total tree volume and aboveground biomass respectively. In addition to the above results, diameter at breast height and total tree height data obtained from 69 circular sample plots of 0.01 ha area drawn from the plantation were used to estimate the total volume and aboveground biomass per hectare BEF which was estimated to be 0.64 Mg/m3. Generally, the selected models and computed BEF in this study are believed to be applied by different organisations and researches to estimate the total volume and aboveground biomass of the J. procera.

Construction of Biomass Model of Phylloctachys pubescens Forest in Hunan

To accurately calculate carbon fixation capacity of P.pubescens forest in Hunan Province and promote the balanced and stable development of P.pubescens forestry in Hunan Province,standard sample plot was set in major producing area of P.pubescens in Hunan Province,and biomass of 258 P.pubescens was measured according to DBH and age class.Multivariate regression method was used to establish univariate (DBH as independent variable) and bivariate (DBH and age class as independent variables) models of total aboveground biomass and organ biomass of P.pubescens .Model evaluation and test showed that each model accorded with the applicable precision and had the appropriate prediction level.

Development and Evaluation of Species-Specific Biomass Models for Most Common Timber and Fuelwood Species of Bangladesh

Allometric biomass models are efficient tools to estimate biomass of trees and forest stands in a non-destructive way. Development of species-specific allometric biomass models requires extensive fieldwork and time. Our study aimed to generate species-specific allometric biomass models for the most common fuelwood and timber species of Bangladesh. We also wanted to evaluate the performances of our models relative to the performances of regional and commonly used pan-tropical biomass models. We used semi-destructive method that incorporates tree-level volume, species-specific biomass expansion factor (BEF), and wood density. We considered four base models, 1) Ln (biomass) = a + bLn (D);2) Ln (biomass) = a + bLn (H);3) Ln (Biomass) = a + bLn (D^2H);4) Ln (Biomass) = a + bLn (D) + cLn (H) to develop species-specific best-fitted models for Total Above-Ground Biomass (TAGB) and stem biomass. The best-fitted model for each species was selected by the lowest value of Akaike Information Criterion (AIC), Residual Standard Error (RSE) and Root Mean Square Error (RMSE). The derived best-fitted models were then evaluated with respect to regional and pan-tropical models using a separate set of observed data. This evaluation was conducted by computing ME (Model Efficiency) and MPE (Model Prediction Error). The best-fitted allometric biomass models have shown higher model efficiency (0.85 to 0.99 at scale 1) and the lowest model prediction error (-8.94% to 5.27%) compared to the regional and pan-tropical models. All the examined regional and pan-tropical biomass models showed different magnitude of ME and MPE. Some models showed higher level (>0.90 at scale 1) of ME compared to the best-fitted specific species biomass model.

Are allometric model parameters of aboveground biomass for trees phylogenetically constrained?

Knowledge of which biological and functional traits have,or lack,phylogenetic signal in a particular group of organisms is important to understanding the formation and functioning of biological communities.Allometric biomass models reflecting tree growth characteristics are commonly used to predict forest biomass.However,few studies have examined whether model parameters are constrained by phylogeny.Here,we use a comprehensive database(including 276 tree species) compiled from 894 allometric biomass models published in 302 articles to examine whether parameters a and b of the model W=aD~b(where W stands for aboveground biomass,D is diameter at breast height) exhibit phylogenetic signal for all tree species as a whole and for different groups of tree species.For either model parameter,we relate difference in model parameter between different tree species to phylogenetic distance and to environmental distance between pairwise sites.Our study shows that neither model parameter exhibits phylogenetic signals(Pagel's λ and Blomberg's K both approach zero).This is the case regardless of whether all tree species in our data set were analyzed as a whole or tree species in different taxonomic groups(gymnosperm and angiosperm),leaf duration groups(evergreen and deciduous),or ecological groups(tropical,temperate and boreal) were analyzed separately.Our study also shows that difference in each parameter of the allometric biomass model is not significantly related to phylogenetic and environmental distances between tree species in different sites.

Fixed-Bed Column Adsorption Modeling of MnO4- Ions from Acidic Aqueous Solutions on Activated Carbons Prepared with the Biomass

Activated carbons calcined at 400˚C and 600˚C (AC-400 and AC-600), prepared using palm nuts, collected in the town of Franceville in Gabon, were used to study the dynamic adsorption of MnO4- ions in acidic media on fixed bed column and on the kinetic modeling of experimental data of breakthrough curves of MnO4- ions obtained. Results on the adsorption of MnO4- ions in fixed-bed dynamics obtained on AC-400 and AC-600 adsorbents beds indicated that the AC-400 bed appears to be the most efficient in removing MnO4- ions in acidic media. Indeed, the adsorbed amounts, the adsorbed capacities at saturation and the elimination percentage of MnO4- ions obtained with AC-400 (31.24 mg;52.06 mg·g-1 and 41.65% respectively) were higher compared to those obtained with AC-600 (9.87 mg;16.45 mg·g-1 and 17.79% respectively). The breakthrough curves kinetic modeling revealed that the Thomas model and the pseudo-first-order kinetic model were the most suitable models to describe the adsorption of MnO4- ions on adsorbents studied in our experimental conditions. The results of the intraparticle diffusion model showed that intraparticle diffusion was involved in the adsorption mechanism of MnO4- ions on investigated adsorbents and was not the limiting step and the only process controlling MnO4- ions adsorption. In contrast to AC-400, the intraparticle diffusion on AC-600 bed plays an important role in the adsorption mechanism of MnO4- ions.

Developing individual tree-based models for estimating aboveground biomass of five key coniferous species in China

Estimating individual tree biomass is critical to forest carbon accounting and ecosystem service modeling.In this study,we developed one-（tree diameter only） and two-variable（tree diameter and height） biomass equations,biomass conversion factor（BCF） models,and an integrated simultaneous equation system（ISES） to estimate the aboveground biomass for five conifer species in China,i.e.,Cunninghamia lanceolata（Lamb.） Hook.,Pinus massoniana Lamb.,P.yunnanensis Faranch,P.tabulaeformis Carr.and P.elliottii Engelm.,based on the field measurement data of aboveground biomass and stem volumes from 1055 destructive sample trees across the country.We found that all three methods,including the one-and two-variable equations,could adequately estimate aboveground biomass with a mean prediction error less than 5%,except for Pinus yunnanensis which yielded an error of about 6%.The BCF method was slightly poorer than the biomass equation and the ISES methods.The average coefficients of determination（R^2） were 0.944,0.938 and 0.943 and the mean prediction errors were 4.26,4.49 and 4.29% for the biomass equation method,the BCF method and the ISES method,respectively.The ISES method was the best approach for estimating aboveground biomass,which not only had high accuracy but also could estimate stocking volumes simultaneously that was compatible with aboveground biomass.In addition,we found that it is possible to develop a species-invariant one-variable allometric model for estimating aboveground biomass of all the five coniferous species.The model had an exponent parameter of 7/3 and the intercept parameter a_0 could be estimated indirectly from stem basic density（a_0= 0.294 q）.

Individual tree segmentation and biomass estimation based on UAV Digital aerial photograph

Digital aerial photograph(DAP)data is processed based on Structure from Motion(Sf M)algorithm and regional net adjustment method to generate digital surface discrete point clouds similar to Light Detection and Ranging(LiDAR)and digital orthophoto mosaic(DOM)similar to optical remote sensing image.In this study,we obtained highresolution images of mature forests of Chinese fir by unmanned aerial vehicle(UAV)flying through crossroute flight,and then reconstructed the threedimensional point clouds in the UAV aerial area by SfM technique.The point cloud segmentation(PCS)algorithm was used for the individual tree segmentation,and the F-score of the three sample plots were 0.91,0.94,and 0.94,respectively.Individual tree biomass modeling was conducted using 155 mature Chinese fir forests which were correctly segmented.The relative root mean squared error(rRMSE)values of random forest(RF),bagged tree(BT)and support vector regression(SVR)were 34.48%,35.74%and 40.93%,respectively.Our study demonstrated that DAP point clouds had great potential to extract forest vertical parameters and could be applied successfully in individual tree segmentation and individual tree biomass modeling.

Comparison of kinetic models for isothermal CO_2 gasification of coal char–biomass char blended char

This study investigated the isothermal gasification reactivity of biomass char （BC） and coal char （CC） blended at mass ratios of 1：3, 1：1, and 3：1 via isothermal thermogravimelric analysis （TGA） at 900, 950, and 1000℃ under CO2. With an increase in BC blending ra- tio, there were an increase in gasification rate and a shortening of gasification time. This could be attributed to the high specific surface area of BC and the high uniformity of carbon structures in CC when compared to those in BC. Three representative gas-solid kinetic models, namely, the volumetric model （VM）, grain model （GM）, and random pore model （RPM）, were applied to describe the reaction behavior of the char. Among them, the RPM model was considered the best model to describe the reactivity of the char gasification reaction. The activa- tion energy of BC and CC isothermal gasification as determined using the RPM model was found to be 126.7 kJ/mol and 210.2 kJ/mol, re- spectively. The activation energy was minimum （123.1 kJ/mol） for the BC blending ratio of 75%. Synergistic effect manifested at all mass ratios of the blended char, which increased with the gasification temperature.

An analytical model for pyrolysis of a single biomass particle

Decreasing in emissions of greenhouse gases to confront the global warming needs to replace fossil fuels as the main doer of the world climate changes by renewable and clean fuels produced from biomass like wood waste which is neutral on the amount of CO2. An analytical and engineering model for pyrolysis process of a single biomass particle has been presented. Using a two-stage semi global kinetic model which includes both primary and secondary reactions, the effects of parameters like shape and size of particle as well as porosity on the particle temperature profile and product yields have been investigated. Comparison of the obtained results with experimental data shows that our results are in a reasonable agreement with previous researchers' works. Finally, a sensitivity analysis is done to determine the importance of each parameter on pyrolysis of a single biomass particle which is affected by many constant parameters.

Biomass-Based Rice (Oryza sativa L.) Aboveground Architectural Parameter Models

To quantify the relationships between rice plant architecture parameters and the corresponding organ biomass, and to research on functional structural plant models of rice plant, this paper presented a biomass-based model of aboveground architectural parameters of rice （Oryza sativa L.） in the young seedling stage, designed to explain effects of cultivars and environmental conditions on rice aboveground morphogenesis at the individual leaf level. Various model variables, including biomass of blade and blade length, were parameterized for rice based on data derived from an outdoor experiment with rice cv. Liangyou 108, 86You 8, Nanjing 43, and Yangdao 6. The organ dimensions of rice aboveground were modelled taking corresponding organ biomass as an independent variable. Various variables in rice showed marked consistency in observation and simulation, suggesting possibilities for a general rice architectural model in the young seedling stage. Our descriptive model was suitable for our objective. However, they can set the stage for connection to physiological model via biomass and development of functional structural rice models （FSRM）, and start with the localized production and partitioning of assimilates as affected by abiotic growth factors. The finding of biomass-based rice architectural parameter models also can be used in morphological models of blade, sheath, and tiller of the other stages in rice life.

Modeling the biomass of energy crops：Descriptions, strengths and prospective

The assessment of the biomass of energy crops has garnered widespread interest since renewable bioenergy may become a substantial proportion of the future energy supply, and modeling has been widely used for the simulation of energy crops yields. A literature survey revealed that 23 models have been developed or adapted for simulating the biomass of energy crops, including Miscanthus, switchgrass, maize, poplar, willow, sugarcane, and Eucalyptus camaldulensis. Three categories（radiation model, water-controlled crop model, and integrated model with biochemical and photosynthesis and respiration approaches） were addressed for the selected models according to different principles or approaches used to simulate biomass production processes. EPIC, ALMANAC, APSIM, ISAM, MISCANMOD, MISCANFOR, SILVA, DAYCENT, APEX and SWAT are radiation models based on a radiation use efficiency approach（RUE） with few empirical and statistical parameters. The Aqua Crop model is a typical water-crop model that emphasizes crop water use, the expression of canopy cover, and the separation of evapotranspiration to soil evaporation and plant transpiration to drive crop growth. CANEGRO, 3PG, Crop Syst and DSSAT are integrated models that use photosynthesis and respiration approaches. SECRETS, LPJm L, Agro-BGC, Agro-IBIS, and WIMOVAC/Bio Cro, DNDC, DRAINMOD-GRASS, and Ag TEM are integrated models that use biochemical approaches. Integrated models are mainly mechanistic models or combined with functional models, which are dynamic with spatial and temporal patterns but with complex parameters and large amounts of input data. Energy crop models combined with process-based models, such as EPIC in SWAT and CANEGRO in DSSAT, provide good examples that consider the biophysical, socioeconomic, and environmental responses and address the sustainability and socioeconomic goals for energy crops. The use of models for energy crop productivity is increasing rapidly and encouraging; however, relevant databases, such as climate, land use/land cover, soil, topography, and management databases, arescarce. Model structure and design assumptions, as well as input parameters and observed data, remain a challenge for model development and validation. Thus, a comprehensive framework, which includes a high-quality field database and an uncertainty evaluation system, needs to be established for modeling the biomass of energy crops.

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Background：Information on above-ground biomass（AGB） is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels.In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels.There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements.Model-based methods provide an efficient framework to estimate AGB.Methods：Using National Forest Inventory（NFI） data for a^1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery.We assessed different combinations of these datasets using three models,a semiparametric generalized additive model（GAM） and two nonlinear models（sigmoidal and exponential）,employing a genetic algorithm for variable selection that minimized root mean square prediction error（RMSPE）,calculated through cross-validation.We compared model fit statistics to a null model as a baseline estimation method.Using bootstrap resampling methods,we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.Results：Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables.The sigmoidal model and the GAM performed similarly;for both models the RMSPE was -36.8 tonnes per hectare（i.e.,57% of the mean）.However,the sigmoidal model was approximately 30% more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model.After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes（＋/- 477,730）,with a confidence interval 20 times more precise than a simple designbased estimate.Conclusions：Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.

Estimation and Characteristic Analysis of Biomass within the Haihe River Basin Based on CASA Model

Using CASA model, biomass within the Haihe River basin during 2002 -2007 was estimated based on remote sensing images, corresponding data of temperature, precipitation and solar radiation, and 1：400 000 0 maps of vegetation coverage in China. Variations in the biomass with vegetation type and vegetation coverage in 2007 were analyzed. Meanwhile, its temporal and spatial changes were discussed. The results validate the applicability of CASA model in the estimation of biomass within the Haihe River basin. During the past 6 years, annual average biomass within the basin was 405.5 Tg in total; annual average biomass in the basin was high in the southeast but low in the northwest, namely plains 〉 mountains 〉 plateaus.

Allometric models for estimating aboveground biomass and carbon in Faidherbia albida and Prosopis africana under agroforestry parklands in drylands of Niger

This study developed allometric models to estimate aboveground biomass and carbon of Prosopis africana and Faidherbia albida. The destructive method was used with a sample of 20 trees per species for the two parkland sites. Linear regression with log transformation was used to model aboveground biomass according to dendrometric parameters. Error analysis, including mean absolute percentage of error(MAPE) and root mean square of error(RMSE), was used to select and validate the models for both species. Model 1(biomass according to tree diameter) for P. africana and F. albida were considered more representative. The statistical parameters of these models were R2 = 0.99, MAPE 0.98% and RMSE1.75% for P. africana, and R2 = 0.99, MAPE 1.19%,RMSE 2.37% for F. albida. The average rate of carbon sequestered was significantly different for the two species(P ≤ 0.05). The total amount sequestered per tree averaged0.17 × 10-3 Mg for P. africana and 0.25 × 10-3 Mg for F. albida. These results could be used to develop policies that would lead to the sustainable management of these resources in the dry parklands of Niger.

Forward heuristic breadth-first reasoning based on rule match for biomass hybrid soft-sensor modeling in fermentation process

Biomass is a key parameter in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Hybrid soft-sensor modeling is a good method for on-line estimation of biomass. Structure of hybrid soft-sensor model is a key to improve the estimating accuracy. In this paper, a forward heuristic breadth-first reasoning approach based on rule match is proposed for constructing structure of hybrid model. First, strategy of forward heuristic reasoning about facts is introduced, which can reason complex hybrid model structure in the event of few known facts. Second, rule match degree is defined to obtain higher esti- mating accuracy. The experiment results of Nosiheptide fermentation process show that the hybrid modeling process can estimate biomass with higher accuracy by adding transcendental knowledge and partial mechanism to the process.

Pyrolysis Model of Single Biomass Pellet in Downdraft Gasifier

By coupling the heat transfer equation with semi-global chemical reaction kinetic equations, a onedimensional, unsteady mathematical model is developed to describe the pyrolysis of single biomass pellet in the pyrolysis zone of downdraft gasifier. The simulation results in inert atmosphere and pyrolysis zone agree well with the published experimental results. The pyrolysis of biomass pellets in pyrolysis zone is investigated, and the results show that the estimated convective heat transfer coefficient and emissivity coefficient are suitable. The mean pyrolysis time is 15.22%, shorter than that in inert atmosphere, and the pellet pyrolysis process in pyrolysis zone belongs to fast pyrolysis. Among the pyrolysis products, tar yield is the most, gas the second, and char the least. During pyrolysis, the temperature change near the center is contrary to that near the surface. Pyrolysis gradually moves inwards layer by layer. With the increase of pyrolysis temperature and pellet diameter, the total pyrolysis time, tar yield, char yield and gas yield change in different ways. The height of pyrolysis zone is calculated to be 1.51—3.51 times of the characteristic pellet diameter.

Quantification of Above-Ground Biomass and Carbon Sequestration Potential of Roadside Trees in the Plateau Department of Benin Republic

Roadside trees are effective natural solutions for mitigating climate change. Despite the usefulness of trees to carbon sequestration, there is a dearth of information on the estimation of biomass and carbon stock for roadside trees in the study area. This study aimed to estimate the carbon stock and carbon dioxide equivalent of roadside trees. A complete enumeration of trees was carried out in Kétou, Pobè and Sakété within the communes of the Plateau Department, Bénin Republic. Total height and diameter at breast height were measured from trees along the roads while individual wood density value was obtained from wood density database. The allometric method of biomass estimation was adopted for the research. The results showed that the total estimations for above-ground biomass, carbon stock and carbon equivalent from all the enumerated roadside trees were 154.53 mt, 72.63 mt and 266.55 mt, respectively. The results imply that the roadside trees contain a substantial amount of carbon stock that can contribute to climate change mitigation through carbon sequestration.

Mathematical Modelling of Biomass Gasification in a Circulating Fluidized Bed CFB Reactor

The scope of the present paper is to investigate the suitability of a mathematical model for Circulating Fluidized Bed (CFB) coal combustion (developed by the International Energy Agency), to predict and simulate the performance of the 100 kWth CFB for air-blown biomass gasification. The development of a mathematical model allows to simulate the operative conditions during biomass gasification, control the quality of the synthesis gas and improve the gasifier design. The geometrical, mechanical, hydro dynamical and thermo chemical features were introduced in the model by properly setting the input file and, some changes have been made in the code to assure the final convergence. A sensitivity analysis has been performed to study the variation in the input parameters of the program, and it has been finally verified by comparing the results with the empirical data collected during coal and wood combustion tests. The program, in the same case, could not successfully run;probably depending on wood char density value. For these reason the influence of char density will be investigated. The model predicts the development of tar and other hydrocarbons, valuating the agreement between the measured and calculated efficiency. A further development, to consider solid biomass, with a certain volatile percentages (20% - 40%), as a fuel has been previewed and analyzed. Finally some investigations have been carried out to provide some useful indications for future developments of the code, in the biomass gasification

Network Modeling of Inammatory Dynamics Induced by Biomass Smoke Leading to Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease(COPD)is a chronic inammatory disorder characterized by airow obstruction and progressive damage of lung tissues.As currently more than 3 billion people use biomass fuel for cooking and heating worldwide,exposure to biomass smoke(BS)is recognized as a significant risk factor for COPD.Recent clinical data have shown that BS-COPD patients have a Th2-type inammatory profile significantly different from that in COPD induced by cigarette smoke.As COPD is essentially proinammatory,however,the mechanism underlying this Th2-type anti-inammatory profile remains elusive.In this work,a network model is applied to study BS-induced inammatory dynamics.The network model involves several positive feedback loops,activations of which are responsible for different mechanisms by which clinical phenotypes of COPD are produced.Our modeling study in this work has identified a subset of BS-COPD patients with a mixed M1-and Th2-type inammatory profile.The model's prediction is in good agreement with clinical experiments and our in silico knockout simulations have demonstrated several important network components that play an important role in the disease.Our modeling study provides novel insight into BS-COPD progression,offering a rationale for targeted therapy and personalized medicine for treatment of the disease in future.