The CAR(Constant Allometric Ratio) and VAR(Variable Allometric Ratio) models wer e two basic biomass models most widely used in research and applications. Re\|sa mpling and sign test were employed in this paper to com...The CAR(Constant Allometric Ratio) and VAR(Variable Allometric Ratio) models wer e two basic biomass models most widely used in research and applications. Re\|sa mpling and sign test were employed in this paper to compare these two models for their parameters' stabilities and their predictions. Research showed that the C AR model would give more stable parameter and more accurate estimation than the VAR model.展开更多
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 sto...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.展开更多
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 equa...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）.展开更多
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 internati...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.展开更多
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 ...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.展开更多
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 metho...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.展开更多
We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empiri...We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation （70.94%） were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution （29.06%）,with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.展开更多
Addressing climate change has become a common issue around the world in the 21st century and equally an important mission in Chinese forestry.Understanding the development of monitoring and assessment of forest biomas...Addressing climate change has become a common issue around the world in the 21st century and equally an important mission in Chinese forestry.Understanding the development of monitoring and assessment of forest biomass and carbon storage in China is important for promoting the evaluation of forest carbon sequestration capacity of China.The author conducts a systematic analysis of domestic publications addressing＂monitoring and assessment of forest biomass and carbon storage＂in order to understand the development trends,describes the brief history through three stages,and gives the situation of new development.Towards the end of the 20th century,a large number of papers on biomass and productivity of the major forest types in China had been published,covering the exploration and efforts of more than 20 years,while investigations into assessment of forest carbon storage had barely begun.Based on the data of the 7th and 8th National Forest Inventories,forest biomass and carbon storage of the entire country were assessed using individual tree biomass models and carbon conversion factors of major tree species,both previously published and newly developed.Accompanying the implementation of the 8th National Forest Inventory,a program of individual tree biomass modeling for major tree species in China was carried out simultaneously.By means of thematic research on classification of modeling populations,as well as procedures for collecting samples and methodology for biomass modeling,two technical regulations on sample collection and model construction were published as ministerial standards for application.Requests for approval of individual tree biomass models and carbon accounting parameters of major tree species have been issued for approval as ministerial standards.With the improvement of biomass models and carbon accounting parameters,thematic assessment of forest biomass and carbon storage will be gradually changed into a general monitoring of forest biomass and carbon storage,in order to realize their dynamic monitoring in national forest inventories.Strengthening the analysis and assessment of spatial distribution patterns of forest biomass and carbon storage through application of remote sensing techniques and geostatistical approaches will also be one of the major directions of development in the near future.展开更多
Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated ...Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.展开更多
Laser-induced breakdown spectroscopy(LIBS) is a new technology suitable for classification of various materials. This paper proposes a hybrid classification scheme for coal, municipal sludge and biomass by using LIBS ...Laser-induced breakdown spectroscopy(LIBS) is a new technology suitable for classification of various materials. This paper proposes a hybrid classification scheme for coal, municipal sludge and biomass by using LIBS combined with K-means and support vector machine(SVM)algorithm. In the study, 10 samples were classified in 3 groups without supervision by K-means clustering, then a further supervised classification of 6 kinds of biomass samples by SVM was carried out. The results show that the comprehensive accuracy of the hybrid classification model is over 98%. In comparison with the single SVM classification model, the hybrid classification model can save 58.92% of operation time while guaranteeing the accuracy. The results demonstrate that the hybrid classification model is able to make an efficient, fast and accurate classification of coal, municipal sludge and biomass, furthermore, it is precise for the detection of various kinds of biomass fuel.展开更多
This study quantifies biomass, aboveground and belowground net productivity, along with additional environmental factors over a 2-3 year period in Barnawapara Sanctuary of Chhattisgarh, India through satellite remotes...This study quantifies biomass, aboveground and belowground net productivity, along with additional environmental factors over a 2-3 year period in Barnawapara Sanctuary of Chhattisgarh, India through satellite remotesensing and GIS techniques. Ten sampling quadrates20×20, 5×5 and 1×1 m were randomly laid for overstorey (OS), understorey (US) and ground vegetation(GS), respectively. Girth of trees was measured at breast height and collar diameters of shrubs and herbs at 0.1 m height. Biomass was estimated using allometric regression equations and herb biomass by harvesting. Net primary productivity (NPP) was determined by Ssumming biomass increment and litter crop values. Aspect and slope influenced the vegetation types, biomass and NPP in different forests. Standing biomass and NPP varied from 18.6 to101.5 Mg ha^(-1) and 5.3 to 12.7 Mg ha^(-1) a^(-1), respectively,in different forest types. The highest biomass was found in dense mixed forest, while net production recoded in Teak forests. Both were lowest in degraded mixed forests of different forest types. OS, US and GS contributed 90.4, 8.7and 0.7%, respectively, for the total mean standing biomass in different forests. This study developed spectral models for the estimation of biomass and NPP using Normalized Difference Vegetation Index and other vegetation indices.The study demonstrated the potential of geospatial tools for estimation of biomass and net productivity of dry tropical forest ecosystem.展开更多
A stochastic numerical approach was developed to model the actual standing biomass in the sand dunes of the northwestern Negev （Israel） and probable boundary conditions that may be responsible for the vegetation pat...A stochastic numerical approach was developed to model the actual standing biomass in the sand dunes of the northwestern Negev （Israel） and probable boundary conditions that may be responsible for the vegetation patterns investigated in detail. Our results for several variables characteristic for the prevailing climate, geomorphology, hydrology and biologicy at four measurement stations along a transect from northwest to southeast allowed for the development of a stochastic model for biomass distribution over the entire sand dune field （mesoscale） and at Nizzana experimental station （microscale）. With this equation it was possible to compute and interpolate a biomass index value for each grid point on the mesoscale and micro scale. The spatial distribution of biomass is negatively linked to distance from the sea, to rainfall and relief energy.展开更多
Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these f...Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional bio- mass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor （BCEF）, biomass expansion factor （BEF） and root-to-shoot ratio （RSR）. Our objectives were to select probability density functions （PDFs） that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. Fromthese publications we chose 52 study sites spread out across South Korea. The statistical model for the meta- analysis was a multilevel model with publication （data source） as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quanti- tative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publica- tions were 1.016 （0.800-1.299）, 1.414 （1.304-1.560） and 0.260 （0.200-0.335）, respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation.展开更多
Savannas constitute a mixture of trees and shrub patches with a more continuous herbaceous understory.The contribution of this biome to the soil organic carbon(SOC)and above-ground biomass(AGB)carbon(C)stock globally ...Savannas constitute a mixture of trees and shrub patches with a more continuous herbaceous understory.The contribution of this biome to the soil organic carbon(SOC)and above-ground biomass(AGB)carbon(C)stock globally is significant.However,they are frequently subjected to land use changes,promoting increases in CO_(2) emissions.In Uruguay,subtropical wooded savannas cover around 100,000 ha,of which approximately 28%is circumscribed to sodic soils(i.e.,subtropical halophytic wooded savannas).Nevertheless,there is little background about the contribution of each ecosystem component to the C stock as well as site-specific allometric equations.The study was conducted in 5 ha of subtropical halophytic wooded savannas of the national protected area Esteros y Algarrobales del Rio Uruguay.This work aimed to estimate the contribution of the main ecosystem components(e.g.,soil,trees,shrubs,and herbaceous plants)to the C stock.Site-specific allometric equations for the most frequent tree species and shrub genus were fitted based on basal diameter(BD)and total height(H).The fitted equations accounted for between 77%and 98%of the aerial biomass variance of Netuma affinis and Vachellia caven.For shrubs(Baccharis sp.),the adjusted equation accounted for 86%of total aerial biomass.C stock for the entire system was 116.71±11.07 Mg·ha^(-1),of which 90.7%was allocated in the soil,8.3%in the trees,0.8%in the herbaceous plants,and 0.2%in the shrubs.These results highlight the importance of subtropical halophytic wooded savannas as C sinks and their relevance in the mitigation of global warming under a climate change scenario.展开更多
文摘The CAR(Constant Allometric Ratio) and VAR(Variable Allometric Ratio) models wer e two basic biomass models most widely used in research and applications. Re\|sa mpling and sign test were employed in this paper to compare these two models for their parameters' stabilities and their predictions. Research showed that the C AR model would give more stable parameter and more accurate estimation than the VAR model.
基金conducted as part of the project ‘‘Pilot site:quantification and modelling of forest carbon stocks in Benin’’ funded by the Global Climate Change Alliance and the European Union(No.00009 CILSS/SE/UAM-AFC/2013)
文摘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.
基金funded by National Natural Science Foundation of China(Grant Nos.31270697,31370634,31570628)supported by State Forestry Administration of China(Grant No.2030208)
文摘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）.
基金provided by the United States Agency for International Development under grant number 3FS-G-11-00002 to the Center for International Forestry Research,entitled the Nyimba Forest Projectprovided by The University of British Columbia
文摘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.
基金supported by the project stocks and potential of carbon sequestration under agroforestry parklands in Niger funded by African Forest Forum(AFF)and International Foundation for Science(IFS),Grant No.D/563-1
文摘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.
基金Supported by the National Natural Science Foundation of China (20476007)
文摘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.
基金supported by the Major Research Development Program of China(2016YFC0502704)National Science Foundation of China(31670645,31470578 and 31200363)+4 种基金National Forestry Public Welfare Foundation of China(201304205)Fujian Provincial Department of S&T Project(2013YZ0001-1,2015Y0083,2016Y0083,2016T3037 and 2016T3032)Key Laboratory of Urban Environment and Health of CAS(KLUEH-C-201701)Youth Innovation Promotion Association CAS(2014267)Key Program of the Chinese Academy of Sciences(KFZDSW-324)
文摘We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation （70.94%） were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution （29.06%）,with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.
基金funded by the State Forestry Administration of China
文摘Addressing climate change has become a common issue around the world in the 21st century and equally an important mission in Chinese forestry.Understanding the development of monitoring and assessment of forest biomass and carbon storage in China is important for promoting the evaluation of forest carbon sequestration capacity of China.The author conducts a systematic analysis of domestic publications addressing＂monitoring and assessment of forest biomass and carbon storage＂in order to understand the development trends,describes the brief history through three stages,and gives the situation of new development.Towards the end of the 20th century,a large number of papers on biomass and productivity of the major forest types in China had been published,covering the exploration and efforts of more than 20 years,while investigations into assessment of forest carbon storage had barely begun.Based on the data of the 7th and 8th National Forest Inventories,forest biomass and carbon storage of the entire country were assessed using individual tree biomass models and carbon conversion factors of major tree species,both previously published and newly developed.Accompanying the implementation of the 8th National Forest Inventory,a program of individual tree biomass modeling for major tree species in China was carried out simultaneously.By means of thematic research on classification of modeling populations,as well as procedures for collecting samples and methodology for biomass modeling,two technical regulations on sample collection and model construction were published as ministerial standards for application.Requests for approval of individual tree biomass models and carbon accounting parameters of major tree species have been issued for approval as ministerial standards.With the improvement of biomass models and carbon accounting parameters,thematic assessment of forest biomass and carbon storage will be gradually changed into a general monitoring of forest biomass and carbon storage,in order to realize their dynamic monitoring in national forest inventories.Strengthening the analysis and assessment of spatial distribution patterns of forest biomass and carbon storage through application of remote sensing techniques and geostatistical approaches will also be one of the major directions of development in the near future.
基金financially supported by the Scientific Research Funds for Forestry Public Welfare of China(Granted No.201004026)the Program for Changjiang Scholars and Innovative Research Team in University(IRT1054)
文摘Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.
基金supported by National Natural Science Foundation of China (No. 51 676 073)the Guangdong Province Train High-Level Personnel Special Support Program (No. 2014TQ01N334)+1 种基金the Science and Technology Project of Guangdong Province (No. 2015A020215005)the Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization (No. 2013A061401005)
文摘Laser-induced breakdown spectroscopy(LIBS) is a new technology suitable for classification of various materials. This paper proposes a hybrid classification scheme for coal, municipal sludge and biomass by using LIBS combined with K-means and support vector machine(SVM)algorithm. In the study, 10 samples were classified in 3 groups without supervision by K-means clustering, then a further supervised classification of 6 kinds of biomass samples by SVM was carried out. The results show that the comprehensive accuracy of the hybrid classification model is over 98%. In comparison with the single SVM classification model, the hybrid classification model can save 58.92% of operation time while guaranteeing the accuracy. The results demonstrate that the hybrid classification model is able to make an efficient, fast and accurate classification of coal, municipal sludge and biomass, furthermore, it is precise for the detection of various kinds of biomass fuel.
文摘This study quantifies biomass, aboveground and belowground net productivity, along with additional environmental factors over a 2-3 year period in Barnawapara Sanctuary of Chhattisgarh, India through satellite remotesensing and GIS techniques. Ten sampling quadrates20×20, 5×5 and 1×1 m were randomly laid for overstorey (OS), understorey (US) and ground vegetation(GS), respectively. Girth of trees was measured at breast height and collar diameters of shrubs and herbs at 0.1 m height. Biomass was estimated using allometric regression equations and herb biomass by harvesting. Net primary productivity (NPP) was determined by Ssumming biomass increment and litter crop values. Aspect and slope influenced the vegetation types, biomass and NPP in different forests. Standing biomass and NPP varied from 18.6 to101.5 Mg ha^(-1) and 5.3 to 12.7 Mg ha^(-1) a^(-1), respectively,in different forest types. The highest biomass was found in dense mixed forest, while net production recoded in Teak forests. Both were lowest in degraded mixed forests of different forest types. OS, US and GS contributed 90.4, 8.7and 0.7%, respectively, for the total mean standing biomass in different forests. This study developed spectral models for the estimation of biomass and NPP using Normalized Difference Vegetation Index and other vegetation indices.The study demonstrated the potential of geospatial tools for estimation of biomass and net productivity of dry tropical forest ecosystem.
文摘A stochastic numerical approach was developed to model the actual standing biomass in the sand dunes of the northwestern Negev （Israel） and probable boundary conditions that may be responsible for the vegetation patterns investigated in detail. Our results for several variables characteristic for the prevailing climate, geomorphology, hydrology and biologicy at four measurement stations along a transect from northwest to southeast allowed for the development of a stochastic model for biomass distribution over the entire sand dune field （mesoscale） and at Nizzana experimental station （microscale）. With this equation it was possible to compute and interpolate a biomass index value for each grid point on the mesoscale and micro scale. The spatial distribution of biomass is negatively linked to distance from the sea, to rainfall and relief energy.
文摘Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional bio- mass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor （BCEF）, biomass expansion factor （BEF） and root-to-shoot ratio （RSR）. Our objectives were to select probability density functions （PDFs） that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. Fromthese publications we chose 52 study sites spread out across South Korea. The statistical model for the meta- analysis was a multilevel model with publication （data source） as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quanti- tative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publica- tions were 1.016 （0.800-1.299）, 1.414 （1.304-1.560） and 0.260 （0.200-0.335）, respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation.
基金funded by the Comision Sectorial de Investigacion Cientifica(CSIC)[ID-501]the Agencia Nacional de Investigacion e Innovacion(ANII)[POS_EXT_2023_1_174913]。
文摘Savannas constitute a mixture of trees and shrub patches with a more continuous herbaceous understory.The contribution of this biome to the soil organic carbon(SOC)and above-ground biomass(AGB)carbon(C)stock globally is significant.However,they are frequently subjected to land use changes,promoting increases in CO_(2) emissions.In Uruguay,subtropical wooded savannas cover around 100,000 ha,of which approximately 28%is circumscribed to sodic soils(i.e.,subtropical halophytic wooded savannas).Nevertheless,there is little background about the contribution of each ecosystem component to the C stock as well as site-specific allometric equations.The study was conducted in 5 ha of subtropical halophytic wooded savannas of the national protected area Esteros y Algarrobales del Rio Uruguay.This work aimed to estimate the contribution of the main ecosystem components(e.g.,soil,trees,shrubs,and herbaceous plants)to the C stock.Site-specific allometric equations for the most frequent tree species and shrub genus were fitted based on basal diameter(BD)and total height(H).The fitted equations accounted for between 77%and 98%of the aerial biomass variance of Netuma affinis and Vachellia caven.For shrubs(Baccharis sp.),the adjusted equation accounted for 86%of total aerial biomass.C stock for the entire system was 116.71±11.07 Mg·ha^(-1),of which 90.7%was allocated in the soil,8.3%in the trees,0.8%in the herbaceous plants,and 0.2%in the shrubs.These results highlight the importance of subtropical halophytic wooded savannas as C sinks and their relevance in the mitigation of global warming under a climate change scenario.
