永平县森林生物量的碳储量初步估算

以云南省永平县的森林资源数据为基础,采用公式:碳储量(C)=林木蓄积湿重×比值×(1-含水率)×含碳率,分别计算林木、林下灌木、林下草本植物、林下凋落物生物量的碳储量,并以2005年为基准年,采用复利公式Cnt=A(1+B)n对永平县2005~2015年森林生物量的碳储量进行了预估.     

不同林龄尾巨桉林地土壤有机碳储量变化差异

为桉树人工林的土壤质量评价提供科学依据,研究了不同林龄(1a、2a、3a、5a、7a)尾巨桉林地0~60cm土壤和枯落物的碳含量及碳储量,测算了不同林龄桉树林地叶面积指数,乔木层、灌木层、草本层和枯落物层生物量。结果表明：土壤有机碳含量随土层深度增加而呈降低趋势,不同林龄0~20 cm土层有机碳含量差异显著,不同林龄相同土层之间土壤有机碳储量差异不显著;枯落物碳储量差异显著,大小顺序为：5 a (4.83 t·hm-2)>7 a (3.89 t·hm-2)>3 a (2.66 t·hm-2)>2 a (2.43 t·hm-2)>1 a (1.56 t·hm-2);0~60 cm土层土壤碳储量与叶面积指数呈负相关关系,与林龄、乔木层生物量、灌木层生物量、草本层生物量、枯落物层生物量之间呈正相关性,但相关性都不显著。     

中国主要乔木树种生物量方程

森林生物量是评估区域森林碳储量的重要参数,也是森林固碳能力的重要标志,而生物量方程是估算森林生物量的主要途径。收集了自1996年至2012年在国内外正式出版的66篇文献中的乔木生物量方程,包括44个乔木树种、612个分量方程,涵盖了中国主要的乔木树种。通过对自变量的选择、方程的形式、样本数量的多少、决定系数的大小、样本采集地区等方面对收集到的生物量方程进行分析。结果表明:1)生物量方程多以胸径(D)、树高(H)或两者的组合作为自变量,方程决定系数(R^2)较高,方程均具有较高的拟合精度;2)树木生物量方程主要采用幂函数、对数函数、多项式函数以及指数方程的形式,其中以幂函数W=a(D2H)b形式的方程居多;3)建立方程的样木数量从3～399不等,样本数量在3～10株的方程数量最多;4)样本采集地区主要分布于中国森林植被丰富的东北地区与西南地区。     

林分生物量碳计量模型的比较研究

选取3个天然林群落作为研究对象,利用3种包含不同计量参数的生物量碳计量模型,即生物量因子法、异速生长方程法及材积源生物量法,分别计算林分碳储量并比较分析各模型计量结果的差异。结果表明:生物量因子法与材积源生物量法计算所得林分平均碳密度相近,分别为155.56和152.82 Mg·hm-2,异速生长方程法的结果偏低,为118.44 Mg·hm-2,生物量因子法计算的不同群落的林分碳储量比异速生长方程法的高22.11%~43.02%;各群落的立木结构及物种组成存在显著差异,均方根误差分析显示生物量因子法对群落碳密度的差异反应最为敏感,计量精度最高;各方法计量结果均显示中、大径级立木是林分碳储量的主要贡献者,中径级立木与大径级立木中计量精度较高的模型分别是异速生长方程法与生物量因子法。综合考虑计量精度及参数获取的便利性,3种计量模型各有优势,在实际应用中可以根据具体情况选择较为适合的模型,一般情况下可使用材积源生物量法,能便利地获得与采用包含木材密度参数的生物量因子法最接近的计量结果。     

火力楠人工林森林生物量和林分碳储量研究

火力楠是原生于我国南部、生长迅速、用途广泛的优良乔木树种。试验以生长10年的火力楠人工林为材料采集乔木各器官及乔灌草凋各层样本,采用相对生长法以胸径为变量与树木各器官结合,建立标准木主干、枝条、叶和根四种器官生物量的异速生长模型,通过换算干重比的方法估算生物量并加以比较;乘以对应的含碳系数换算成碳储量,再加和估算林分总碳储量。火力楠人工林乔木层生物量为21.60 t·hm^-2;总森林生物量为25.96t·hm^-2;林分总碳储量为12.60 t·hm^-2。试验地火力楠人工林的郁闭度较大,林下植被较少,乔木层碳储量占比很高,反映出火力楠是较好的碳汇树种。     

森林碳储量遥感估测模型构建研究——以闽江流域杉木林为例

以福建省闽江流域为研究对象,利用年均降水、温度数据,结合改进NPP模型构建基于气象因子的森林生产力估测模型,并通过森林生产力与二类调查结果对应的小班年龄曲线建立森林生物量模型,以此获得遥感估测模型所需的样地生物量,在此基础上利用遥感影像和杉木平均含碳率可以成功构建森林生物量非线性遥感估测模型和碳储量遥感估测模型。本研究不仅能建立森林碳储量模型,而且避免了繁杂的森林生物量野外实测,节省了大量的人力物力,且不需砍伐森林树木。实验计算结果:闽江流域杉木林碳储量2003~2012年从10 337 774 t增加到19 624 374 t。     

福州市主要森林类型林下灌木层生物量和碳密度研究

为了解林下灌木层生物量和碳密度变化特征,基于森林火灾风险普查样地调查与森林资源一张图数据,对福州市乔木林主要森林类型林下灌木层生物量和碳密度进行研究。结果表明:(1)福州市乔木林林下灌木层总生物量169.37×10^(4)t,总碳储量为79.61×10^(4)t,平均碳密度达1.49 t/hm^(2);(2)不同森林类型林下灌木层碳密度1.11~1.94 t/hm^(2),其大小依次为人工阔叶林>天然阔叶林>人工马尾松林>天然针叶林>人工针叶林>人工杉木林;(3)不同森林类型林下灌木层单位面积生物量、碳密度随着乔木层郁闭度的增加总体呈明显减少趋势,生物量大小为低郁闭度>中郁闭度>高郁闭度;(4)不同森林类型林下灌木层单位面积生物量、碳密度随着乔木层林龄的增加总体呈先减少后增加趋势,生物量大小为幼龄林>成、过熟林>中龄林>近熟林。     

基于径向基函数神经网络模型的桃江县森林碳储量估测

量化森林碳储量对森林经营者的正确决策至关重要。本文以湖南省桃江县为研究区,根据2013年森林资源一类调查数据和Landsat 8遥感影像,建立多元逐步回归、偏最小二乘回归和径向基函数神经网络模型,开展碳储量的估测方法比较。结果表明:三种方法中,径向基函数神经网络模型估测森林碳储量效果最好,决定系数达到0.645,相对均方根误差为15.582 t·hm~(-2);其次为偏最小二乘回归模型,决定系数和相对均方根误差分别为0.511和17.135 t·hm~(-2);多元逐步回归模型精度最低,决定系数和相对均方根误差分别为0.431和18.105 t·hm~(-2)。径向基函数神经网络模型反演的研究区森林碳储量分布图表明,海拔高的地方碳储量较大,城区碳储量较小,与实际植被分布情况一致。     

海南岛北部3种经济林树种的生物量、碳储量及其分配特征

树木地上和地下生物量是森林生态系统中重要的碳汇,了解林木地下部分和地上部分的生物量分配情况对于估算森林碳汇具有重要作用。以海南岛北部菠萝蜜、荔枝和龙眼树为研究对象,每个树种各选取30株不同径阶的样木进行整株挖掘,对其生物量、碳储量及其分配特征进行了研究。结果表明:不同经济林树种在各组分生物量最优模型的选择上,基本以W=a D~bH~c模型为最优;3种经济林树种各组分生物量的大小均表现为树干根系树枝树叶;菠萝蜜、荔枝和龙眼树树枝的含碳比率相差不大,分别为0.39、0.41、0.40,但其树干、根系、树枝和全树的含碳比率却均存在差异,各组分碳储量的大小均表现为树干根系树枝树叶。文中分析认为,基于树高和胸径的相对生长模型,可以实现对经济林树种各组分生物量的准确拟合和碳储量的有效估算。     

北京市主要森林类型蓄积量生物量碳储量模型研建

林分水平的蓄积量、生物量和碳储量模型,是开展森林资源规划设计调查的计量基础。基于北京市2016年森林资源连续清查的1 425个乔木林样地数据,分别利用非线性独立回归估计、误差变量联立方程组和含哑变量的误差变量联立方程组方法,建立了油松林、侧柏林、栎树林、桦木林、榆树林、刺槐林、杨树林、其他硬阔林、其他软阔林、乔木经济林等10种主要森林类型的林分蓄积量、生物量和碳储量模型。结果显示:10种主要森林类型的蓄积量、生物量和碳储量模型的确定系数(R~2)都在0.93以上,总体相对误差(TRE)和平均系统误差(ASE)都在±3%以内且多数趋近于0,平均预估误差(MPE)都在5%以内,平均百分标准误差(MPSE)都在15%以内。结果表明:不同森林类型的蓄积量主要取决于林分断面积和平均高,生物量主要取决于蓄积量和林分平均高;含哑变量的非线性误差变量联立方程组方法,是建立林分水平三储量(森林蓄积量、生物量和碳储量)模型系统的可行方法;所建北京市10种主要森林类型的蓄积量、生物量和碳储量模型,其预估精度达到相关技术规定要求,可以在实践中推广试用;为进一步提高模型的准确度,可采用基于二元模型计算的蓄积量和生物量样地数据对所建模型进行修正。     

Species-specific allometric equations for improving aboveground biomass estimates of dry deciduous woodland ecosystems

Allometric equations are important for quantifying biomass and carbon storage in terrestrial forest ecosystems.However,equations for dry deciduous woodland ecosystems,an important carbon sink in the lowland areas of Ethiopia have not as yet been developed.This study attempts to develop and evaluate species-specific allometric equations for predicting aboveground biomass(AGB)of dominant woody species based on data from destructive sampling for Combretum collinum,Combretum molle,Combretum harotomannianum,Terminalia laxiflora and mixed-species.Diameter at breast height ranged from 5 to 30 cm.Two empirical equations were developed using DBH(Eq.1)and height(Eq.2).Equation 2 gave better AGB estimations than Eq.1.The inclusion of both DBH and H were the best estimate biometric variables for AGB.Further,the equations were evaluated and compared with common generic allometric equations.The result showed that our allometric equations are appropriate for estimating AGB.The development and application of empirical species-specific allometric equations is crucial to improve biomass and carbon stock estimation for dry woodland ecosystems.     

Allometric relationship for estimating above-ground biomass of Aegialitis rotundifolia Roxb. of Sundarbans mangrove forest, in Bangladesh

Tree biomass plays a key role in sustainable management by providing different aspects of ecosystem. Estimation of above ground biomass by non-destructive means requires the development of allometric equations. Most researchers used DBH (diameter at breast height) and TH (total height) to develop allometric equation for a tree. Very few species-specific allometric equations are currently available for shrubs to estimate of biomass from measured plant attributes. Therefore, we used some of readily measurable variables to develop allometric equations such as girth at collar-height (GCH) and height of girth measuring point (GMH) with total height (TH) for A. rotundifolia, a mangrove species of Sundarbans of Bangladesh, as it is too dwarf to take DBH and too irregular in base to take Girth at a fixed height. Linear, non-linear and logarithmic regression techniques were tried to determine the best regression model to estimate the above-ground biomass of stem, branch and leaf. A total of 186 regression equations were generated from the combination of independent variables. Best fit regression equations were determined by examining co-efficient of determination (R2), co-efficient of variation (CV), mean-square of the error (MSerror), residual mean error (Rsme), and F-value. Multiple linear regression models showed more efficient over other types of regression equation. The performance of regression equations was increased by inclusion of GMH as an independent variable along with total height and GCH.     

Applicability of semi-destructive method to derive allometric model for estimating aboveground biomass and carbon stock in the Hill zone of Bangladesh

Biomass estimation using allometric models is a nondestructive and popular method.Selection of an allometric model can influence the accuracy of biomass estimation.Bangladesh Forest Department initiated a nationwide forest inventory to assess biomass and carbon stocks in trees and forests.The relationship between carbon storage and sequestration in a forest has implications for climate change mitigation in terms of the carbon sink in Bangladesh.As part of the national forest inventory,we aimed to derive multi-species biomass models for the hill zone of Bangladesh and to determine the carbon concentration in tree components(leaves,branches,bark and stem).In total,175 trees of 14 species were sampled and a semi-destructive method was used to develop a biomass model,which included development of smaller branch(base dia 7 cm) biomass allometry and volume estimation of bigger branches and stems.The best model of leaf,branches,and bark showed lower values for adjusted R2(0.3152-0.8043) and model efficiency(0.436-0.643),hence these models were not recommended to estimate biomass.The best fit model of stem and total aboveground biomass(TAGB) showed higher model efficiency 0.948 and 0.837,respectively,and this model was recommended for estimation of tree biomass for the hill zone of Bangladesh.The best fit allometric biomass model for stem was Ln(Stem)=-10.7248+1.6094*Ln(D)+1.323*Ln(H)+1.1469*Ln(W);the best fit model for TAGB was Ln(TAGB)=-6.6937+0.809*Ln(D^2*H*W),where DBH=Diameter at Breast Height,H=Total Height,W=Wood density.The two most frequently used pan-tropical biomass models showed lower model efficiency(0.667 to 0.697) compared to our derived TAGB model.The best fit TAGB model proved applicable for accurate estimation of TAGB for the hill zone of Bangladesh.Carbon concentration varied significantly(p 0.05) by species and tree components.Higher concentration(48-49%) of carbon was recorded in the tree stem.     

Allometric equations for accurate estimation of above-ground biomass in logged-over tropical rainforests in Sarawak, Malaysia

Although allometric equations can be used to accurately estimate biomass and/or carbon stock in forest ecosystems, few have been developed for logged-over tropical rainforests in Southeast Asia. We developed allometric relationships between tree size variables (stem diameter at breast height (dbh) and tree height) and leaf, branch, stem and total above-ground biomass in two logged-over tropical rainforests with different soil conditions in Sarawak, Malaysia. The study sites were originally classified as mainly lowland dipterocarp forest and have been selectively logged in the past 20 years. In total, 30 individuals from 27 species were harvested to measure above-ground parts. The correlation coefficients for the allometric relationships obtained for total above-ground biomass as a function of dbh had high values (0.99), although the relationships for leaf biomass had a relatively low coefficient (0.83). We also found relatively high coefficients for allometric relationships between tree height and plant-part biomass, ranging from 0.82 to 0.97. Moreover, there were no differences for allometric equations of total above-ground biomass between study sites. A comparison of equations of above-ground biomass in various previously reported tropical rainforests and pan-tropic general equations imply that our allometric equations differ largely from the equations for tropical primary forests, early successional secondary forest, and even for the general models. Therefore, choosing the biomass estimation models for above-ground biomass in the logged-over forests of Southeast Asia requires careful consideration of their suitability.     

Trends in allometric models and aboveground biomass of family Rhizophoraceae mangroves in the Coral Triangle ecoregion,Southeast Sulawesi,Indonesia

This study aims to establish allometric models and estimate aboveground biomass (AGB) of mangroves Rhizophoraceae in the Southeast Sulawesi, Indonesia. Allometric models of the AGB of mangroves Rhizophora apiculata, R. mucronata, and Ceriops tagal were established using independent variables consisting stem diameter at 30 cm from the ground (D₃₀), diameter at breast height (DBH), D₃₀²H and DBH²H. The AGB of mangroves was estimated by applying allometric model and tree census. The results showed that the best fitting allometric models of AGB for R. apiculata is based on variable DBH, while DBH²H is the best variable for R. mucronata trees. Conversely, the D₃₀ is the best variable for estimating AGB of C. tagal trees. Thus, there is some variation of independent variables on allometric models for the estimation of AGB for Rhizophoraceae mangroves. The AGB (ton ha^?1) of R. apiculata, R. mucronata, and C. tagal was estimated respective 651.60, 232.11 and 154.56 in the protected area, and respective 137.59, 189.35 and 39.06 ton ha^?1 in the unprotected area. Higher AGB of mangroves growing in the protected area indicated the suitable condition and undisturbed by human activities. The conservation of mangroves is necessary for the sustainability of mangroves and coastal ecosystems in the Coral Triangle ecoregion.     

Carbon Stock in Community Managed Hill Sal (Shorea robusta) Forests of Central Nepal

Community forests of developing countries are eligible to participate in the Reducing Emissions from Deforestation and Forest Degradation (REDD+) scheme. For this, estimation of carbon stock and the sequestration is essential. The carbon stock in the living biomass of nine community managed Shorea robusta forests of the mid hill regions of central Nepal (managed for 4–29 yr) were estimated. The carbon stock of trees and shrubs was estimated using an allometric equation while the biomass of herbaceous vegetation was estimated by the harvest method. The carbon stock in the living biomass of the studied forests ranged from 70–183 Mg ha^?1(mean: 120 Mg ha^?1) and it increased with increasing soil organic carbon. However, the carbon stock did not vary significantly with species richness and litter cover. The biomass and carbon stock in the forests managed for >20 yr were significantly higher than in the forests managed for < 20 yr. The carbon stock increased with the management duration (p < .05) with sequestration rate of 2.6 Mg C ha^?1 yr^?1. The local management has had positive effects on the carbon stock of the forests and thus the community forests have been acting as a sink of the atmospheric CO₂. Therefore, the community managed forests of Nepal are eligible to participate in the REDD+ scheme.     

Allometric models for estimation of aboveground biomass of Gmelina arborea Roxb. in pulpwood plantations of Bangladesh

Gmelina arborea Roxb. is a fast-growing, deciduous tree species native to Bangladesh. It has been widely planted since 1992 for commercial production of pulpwood in hilly areas of Bangladesh. The purpose of this study was to derive well-fitting allometric models for estimation of aboveground biomass, and carbon and nutrient (nitrogen, phosphorus and potassium) stocks in G. arborea to ensure sustainable production and management of this species. Eight linear models with 64 regression equations were tested for estimation of aboveground biomass, carbon and nutrient stocks in different parts (leaves, branches, bark and stem) of the plant. The best-fitting allometric models were selected in accordance with various relevant statistical criteria.     

Carbon stock of Banja forest in Banja district,Amhara region,Ethiopia: An implication for climate change mitigation

This study estimates the carbon stock of Banja Forest which is natural and dry Afromontane forest type in Banja District, northwest of Ethiopia. A systematic sampling method was used to identify each sampling point through the Global Positioning System. A total of 63 plots measuring 20 × 20 m were employed to collect tree species and soil data. Losses on ignition and the Walkley–Black method were used to estimate biomass and soil carbon stock, respectively. The carbon stock of trees was estimated using an allometric equation. The results revealed that the total carbon stock of the forest was 639.87 t/ha whereas trees store 406.47 t/ha, litter, herbs, and grasses (LHGs) 2.58 t/ha and soil 230.82 t/ha (up to 30 cm depth). The carbon pools’ carbon stock variation with altitude and slope gradients were not significant (p > 0.05) which was similar to other previous studies. The Banja Forest is a reservoir of high carbon and thus acts as a great sink of the atmospheric carbon. It can be concluded that the Forest plays a role in climate change mitigation. Hence, it should be integrated with reduced emission from deforestation and degradation (REDD⁺) and the clean development mechanism (CDM) of the Kyoto Protocol to catch monetary benefits.     

An allometric equation for estimating stem biomass of Acacia auriculiformis in the north-eastern region of Bangladesh

Tree biomass plays an important role in sustainable management and in estimating forest carbon stocks. The objective of this study was to select the best model for measuring stem biomass of Acacia auriculiformis in the study area. Data from five hillocks and 120 individual trees from each hillock were used in this study. Twelve different forms of linear, power and exponential equations were compared in this study to select the best model. Two models (VI and XI) were selected based on R ², adjusted R ², the Akaike information criterion, F-statistics and the five assumptions of linear regression. Model VI was discarded based on the Durbin-Watson value of autocorrelation of the residuals, then the ARIMA (2, 0, 1) model was used to remove the autocorrelation from the model and the final bias-corrected model XI was derived. The model was validated with a test data set having the same range of DBH and stem height of the training data set on the basis of linear regression, Morisita's similarity index, and t-test for mean difference between predicted and expected biomass. A comparison between the best logarithmic and non-linear allometric model shows that the non-linear model produces systematic biases and overestimates stem biomass for larger trees. The overall results showed that the bias-corrected logarithmic model XI can be used efficiently for estimating stem biomass of A. auriculiformis in the northeastern region of Bangladesh.     

Aboveground biomass estimation of small diameter woody species of tropical dry forest

Estimation of accurate biomass of different forest components is important to estimate their contribution to total carbon stock. There is lack of allometric equations for biomass estimation of woody species at sapling stage in tropical dry forest (TDF), and therefore, the carbon stored in this forest component is ignored. We harvested 46 woody species at sapling stage in a TDF and developed regression models for the biomass estimation of foliage, branch, bole and the total aboveground part. For foliage and branch biomass, the models with only stem diameter as estimator showed greater R ². For bole and aboveground biomass, the models including wood specific gravity or wood density exhibited higher R ² than those without wood density. Also, the model consisting of wood density, stem diameter and height had the lowest standard error of estimate for bole and aboveground biomass. Moreover, the R ² values are very similar among models for each component. The measurement error of height and the use of a standard value of wood density together may introduce more than 2 % error into the models. Therefore, we suggest using diameter-only model, which may be more practical and equally accurate when applied to stands outside our study area.