上海城市森林叶生物量遥感监测

区域尺度城市森林叶生物量的估测对了解植物长势、碳同化过程和森林生态系统具有显著作用。本研究基于2011年6月—2012年6月样地实测叶生物量数据以及同期遥感信息,采用回归分析与空间分析相结合的方法,估测了上海城市森林叶生物量的空间分布,探讨了区域尺度森林叶生物量的遥感估测方法。结果表明:(1)上海城市森林叶生物量密度总体呈现出中心城区(静安区、黄浦区等)高,郊区县(松江区、金山区等)低的空间分布特征,其生物量密度分别介于4~10和1~6 t·hm-2。(2)研究区森林叶的平均生物量密度和生物量总量分别为2.55 t·hm-2和300.81×103t,郊区县与中心城区森林叶生物量分别占总量的94.16%和5.84%。在所有区县中,以林地面积最大的崇明县和浦东新区具有最高的森林叶生物量值,两者总量达到研究区总量的34.82%;以林地面积最小的静安区为最低,仅占总量的0.1%。(3)通过残差计算并引入空间分析的森林叶生物量遥感估算方法,其标准误差RMSE、平均绝对误差MAE、平均相对误差MRE较回归模型分别降低了58.46%、48.76%和48.71%,较空间插值的结果分别降低了47.74%、38%和49.24%。结合空间分析和回归分析的城市森林叶生物量研究方法为快速、便捷、客观、高效的区域生物量遥感监测提供了可能。     

浙江省森林生态系统碳储量及其分布特征

利用2011-2012年野外标准地实测资料, 结合第八次全国森林资源清查资料, 研究了浙江省森林生态系统碳储量及其分布特征。结果表明: 浙江省森林生态系统碳储量为602.73 Tg, 其中乔木层、灌草层、凋落物层和土壤层碳储量分别为122.88 Tg、16.73 Tg、11.36 Tg和451.76 Tg, 分别占生态系统碳储量的20.39%、2.78%、1.88%和74.95%; 在各森林类型中, 阔叶混交林碳储量为138.03 Tg, 所占比例最大(22.90%); 在森林各龄组中, 幼、中龄林约占浙江省森林生态系统碳储量的70.66%, 是碳储量的主要贡献者。浙江省森林生态系统平均碳密度为120.80 t·hm^-2, 乔木层、灌草层、凋落物层和土壤层碳密度分别为24.65 t·hm^-2、3.36 t·hm^-2、2.28 t·hm^-2和90.51 t·hm^-2。浙江省森林生态系统土壤层碳储量和生态系统碳储量呈极显著相关关系, 说明土壤层碳储量对浙江省森林生态系统碳储量贡献较大。浙江省天然林乔木层碳密度整体表现为过熟林>成熟林>近熟林>中龄林>幼龄林, 而人工林乔木层碳密度表现为过熟林>近熟林>成熟林>中龄林>幼龄林。浙江省幼、中龄林林分面积占比重较大, 占全省森林面积的76.76%, 若对现有森林进行更好的经营和管理, 可以增加浙江省森林的碳固存能力。     

吉林省森林植被固碳现状与速率

通过对吉林省森林植被的普遍调查、典型调查以及植被样品含碳率测定, 结合吉林省2009年和2014年森林清查数据, 估算了区域森林植被的碳储量、碳密度及固碳速率。研究结果表明: 林下植被的生物量在不同林分和同类林分中存在较大的差异, 整体不足乔木层生物量的3%, 灌木植物的生物量略高于草本植物和幼树。不同林分类型的乔木含碳率介于45.80%-52.97%之间, 整体表现为针叶林高于阔叶林; 灌木和草本植物分别为39.79%-47.25%和40%左右。吉林省森林植被碳转换系数以0.47或0.48更为准确, 若以0.50或0.45作为植被的碳转换系数计算碳储量, 会造成±5.26%的偏差。吉林省森林植被不仅维持着较高的碳库水平, 而且极具碳汇能力; 2009年和2014年碳储量分别为471.29 Tg C和505.76 Tg C, 累计碳增量34.47 Tg C, 平均每年碳增量6.89 Tg C·a^-1; 碳密度由64.58 t·hm^-2增至66.68 t·hm^-2, 平均增加2.10 t·hm^-2, 固碳速率0.92 t·hm^-2·a^-1。森林植被碳储量的增长主体是蒙古栎(Quercus mongolica)林和阔叶混交林, 合计碳增量占总体的90.34%。受植被发育引起的生物量增长、林分龄组晋级以及森林经营所引起的面积变化影响, 各龄组植被碳增量为幼龄林>过熟林>近熟林>中龄林, 成熟林表现为负增长; 固碳速率为过熟林>幼龄林>近熟林>中龄林>成熟林。森林植被碳储量和碳密度的市/区分布整体表现为自东向西明显的降低变化; 碳增量以东北和中东部地区较高, 西部地区较低; 固碳速率整体以南部的通化地区和白山地区相对较高, 中部的吉林地区和东部的延边地区次之, 西部的白城地区、松原地区等地呈负增长。     

小兴安岭7种典型林型林分生物量碳密度与固碳能力

森林生物碳储量作为森林生态系统碳库的重要组成部分, 在全球碳循环中发挥着重要作用。以小兴安岭7种典型林型为研究对象, 通过外业样地调查与室内实验分析相结合的方法, 从林分尺度对林分生物量与碳密度进行计量, 分析了林分生物碳储量的空间分配格局, 并对林分年固碳能力与碳汇潜力进行了探讨。结果表明: 小兴安岭不同林型从幼龄林到成熟林的乔木层碳密度增长速率为: 蒙古栎(Quercus mongolica)林>兴安落叶松(Larix gmelinii)林>云冷杉(Picea-Abies)林>樟子松(Pinus sylvestris var. mongolica)林>山杨(Populus davidiana)林>红松(Pinus koraiensis)林>白桦(Betula platyphylla)林。7种典型林型不同龄组(幼龄林、中龄林、近熟林和成熟林)林分生物量碳密度分别为: 红松林31.4、74.7、118.4和130.2 t·hm^-2; 兴安落叶松林28.9、44.3、74.2和113.3 t·hm^-2; 樟子松林22.8、52.0、71.1和92.6 t·hm^-2; 云冷杉林23.1、44.1、77.6和130.3 t·hm^-2; 白桦林18.8、35.3、66.6和88.5 t·hm^-2; 蒙古栎林25.0、20.0、47.5和68.9 t·hm^-2; 山杨林19.8、28.7、43.7和76.6 t·hm^-2。红松林、兴安落叶松林、樟子松林和蒙古栎林在幼龄林时林分年固碳量较高, 其他林型在成熟林时林分年固碳量较高。7种典型林型不同龄组的林分生物量碳密度均随林龄增长而增加, 但不同林型的碳汇功能存在差异, 同一林型不同林龄的生物量碳密度增幅差异也较大。林分年固碳量在0.4-2.8 t·hm^-2之间, 碳汇能力较强、碳汇潜力较大。尤其是小兴安岭目前林分质量较差, 幼龄林和中龄林所占的比重较大, 具有较大的碳汇潜力。研究结果可为森林经营管理及碳汇功能评价提供参考。     

内蒙古森林生态系统碳储量及其空间分布

内蒙古森林面积居全国第一位, 林木蓄积量居第五位, 准确地估算该区域森林碳储量对于评估中国森林碳储量以及制定森林资源管理措施均具有重要意义。该研究基于内蒙古森林资源野外样方调查和室内分析, 评估了内蒙古森林生态系统的固碳现状, 估算了内蒙古森林生态系统不同林型和不同碳库(乔木、灌木、草本、凋落物和土壤碳库)的碳密度大小, 揭示了其空间分布特征。在此基础上估算了内蒙古森林碳储量大小及空间格局。结果表明: 1)内蒙古森林植被层碳储量为787.8 Tg C, 乔木层、凋落物层、草本层和灌木层分别占植被层总碳储量的93.5%、3.0%、2.7%和0.8%。内蒙古森林植被层平均碳密度为40.4 t·hm^-2, 其中, 乔木层、凋落物层、草本层和灌木层的碳密度分别为35.6 t·hm^-2、2.9 t·hm^-2、1.2 t·hm^-2和0.6 t·hm^-2。2)内蒙古森林土壤层(0-100 cm)碳储量为2449.6 Tg C, 其中0-30 cm的土壤碳储量最高, 占总碳储量的79.8%。0-10 cm、10-20 cm和20-30 cm的土壤碳储量分别占0-30 cm土壤碳储量的38.8%、34.1%和27.1%。内蒙古森林土壤平均碳密度为144.4 t·hm^-2。黑桦(Betula davurica)林土壤碳密度最高, 云杉(Picea asperata)林最小。土壤碳密度随土壤深度的增加而降低。3)内蒙古森林生态系统碳储量为3237.4 Tg C, 植被层和土壤层碳储量分别占森林生态系统碳储量的24.3%和75.7%。落叶松(Larix gmelinii)林总碳储量最高, 其次为白桦(Betula platyphylla)林、夏栎(Quercus robur)林、黑桦林、榆树(Ulmus pumila)疏林和山杨(Populus davidiana)林。内蒙古森林生态系统平均碳密度为184.5 t·hm^-2。土壤碳密度与植被碳密度呈显著正相关关系。4)内蒙古森林生态系统碳储量和碳密度的空间分布总体上为东部地区高、西部地区低的趋势。在降水量充沛的东部地区和降水偏少的中西部地区, 有针对性地开展森林保护区建设和人工造林, 可显著提升区域的碳汇能力。     

西南干旱河谷植物群落组分生物量的纬度格局及其影响因素

研究植物群落不同组分生物量的纬度格局及其与生物、非生物因子的定量关系有助于揭示植物对环境的适应性, 能够解释生态系统结构和功能的空间差异性及其成因。该研究在西南干旱河谷跨越9个纬度(23.23°-32.26° N), 布置101个群落 样方(4 m × 6 m)。采用收割法测定群落及组分生物量, 分析生物量在纬度梯度上的变化规律及其影响因子。结果显示, 干旱河谷群落平均生物量为(17.05 ± 1.09) t·hm^-2, 其中, 灌木平均生物量为(11.51 ± 1.03) t·hm^-2, 占60.2%; 草本平均生物量为(2.11 ± 0.21) t·hm^-2, 占15.6%; 凋落物平均生物量为(3.41 ± 0.34) t·hm^-2, 占24.1%。群落生物量和灌木生物量随纬度升高而显著增加, 草本生物量随纬度升高无明显变化, 凋落物生物量随纬度的升高而显著降低。随着纬度增加, 灌木生物量的比例明显增大, 草本生物量的比例无明显变化, 凋落物生物量的比例明显降低。灌木优势度及丰富度的变化是驱动干旱河谷植被生物量在纬度梯度上变化的主要内在因子, 而外在因子中, 气候因子对群落及组分生物量的影响显著高于土壤因子｡     

天山森林生态系统碳储量格局及其影响因素

科学地估算亚洲中部天山雪岭杉(Picea schrenkiana)生态系统碳密度与碳储量是评价新疆森林碳汇潜力、评估森林在减缓大气CO₂浓度上升、应对气候变化等方面功能的关键, 对干旱区森林生态系统的保育和可持续发展具有重要意义。该文基于在天山雪岭杉林区布设的70个野外样地调查数据, 结合新疆森林资源连续清查数据, 全面估算了天山雪岭杉生态系统的碳密度和碳储量, 分析了其分布格局与影响因素。结果表明: 天山雪岭杉不同龄组叶、枝、干和根的含碳率变化不显著, 其乔木层平均含碳率为49%, 而林下植被(凋落物、草本等)平均含碳率仅为42%。雪岭杉森林生态系统单位面积生物量为187.98 Mg·hm^-2, 其中乔木层生物量占生态系统总生物量的98.93%。乔木层各组分生物量大小为: 干>根>枝>叶, 而各龄组生物量排序为: 成熟林>中龄林>近熟林>过熟林>幼龄林。雪岭杉生态系统碳密度为544.57 Mg·hm^-2, 碳储量为290.84 Tg C, 其中植被碳密度为92.57 Mg·hm^-2, 植被碳储量为53.14 Tg C, 土壤碳密度为452.00 Mg·hm^-2, 土壤碳储量为237.70 Tg C。天山雪岭杉生态系统碳密度分异与不同林区林带垂直宽度变化具有很高的相关性, 其生态系统碳密度西高东低的分布格局和它所处的环境因子西优东劣的变异是相一致的, 即不同的环境因素组合是造成天山雪岭杉生态系统碳密度差异的主要原因。     

黑龙江省大兴安岭森林生物量空间格局及其影响因素

基于样地实测数据和EVI指数,定量分析了黑龙江省大兴安岭森林生物量空间格局,并利用ArcGIS软件的空间分析与统计工具,分析了气候区、海拔、坡度、坡向和植被类型对森林生物量空间格局的影响.结果表明: 黑龙江省大兴安岭森林生物量为350 Tg,空间上呈聚集分布,生物量有巨大的增长空间.森林生物量密度大小顺序为:寒温带湿润区(64.02 t·hm^-2)＞中温带湿润区(60.26 t·hm^-2);各植被类型生物量密度大小顺序为:针阔混交林(65.13 t·hm^-2)＞云冷杉林(63.92 t·hm^-2)＞偃松 落叶松林(63.79 t·hm^-2)＞樟子松林(61.97 t·hm^-2)＞兴安落叶松林(61.40 t·hm^-2)＞落叶阔叶混交林(58.96 t·hm^-2).随海拔和坡度的增大,森林生物量密度先减小后增加,并且阴坡大于阳坡.大兴安岭森林生物量空间格局随气候区、植被类型和地形因子的梯度变化表现出差异性,在区域尺度上估算生物量密度时,需要充分考虑这种空间差异性.     

1985—2030年江西泰和县森林植被碳储量的时空动态

根据第6次森林清查小班数据,运用生物量转换因子法和平均生物量法估算了2003年江西省泰和县森林植被的生物量和碳储量,采用空间替代时间的方法,利用Logistic方程拟合了泰和主要森林类型年龄与碳密度的曲线关系,并结合小班轮伐信息,估算了全县1985—2003年的植被生物量和碳储量,分析了期间的时空动态特征,并以2003年为基准年,假定到2020、2030年泰和县森林植被面积保持稳定、且不考虑轮伐期,推算了此情景下2020、2030年泰和县植被碳储量.结果表明：2003年,泰和县森林林分总面积15.74×10⁴ hm²,总生物量6.71 Tg,植被碳储量4.14 Tg C,平均碳密度26.31 t C·hm^{- 2}. 1985、1994、2003、2020、2030年泰和县森林植被碳储量分别为1.06、2.83、4.14、5.65和6.35Tg C,森林植被碳密度的空间分布由东西部向中部递减.人工造林使泰和县林分面积大幅增加,全县森林植被的固碳能力明显增强.     

宁夏回族自治区森林生态系统固碳现状

根据宁夏回族自治区森林资源清查资料以及野外调查和室内分析的结果,研究了宁夏地区森林生态系统固碳现状,估算了该区森林生态系统的碳密度、碳储量,并分析了其空间分布特征.结果表明: 宁夏森林各植被层生物量大小顺序为: 乔木层(46.64 Mg·hm^-2)>凋落物层(7.34 Mg·hm^-2)>细根层(6.67 Mg·hm^-2)>灌草层(0.73 Mg·hm^-2).云杉类(115.43 Mg·hm^-2)和油松(94.55 Mg·hm^-2)的单位面积植被生物量高于其他树种.不同林龄乔木层碳密度中,过熟林最高,但由于幼龄林面积所占比例最大,其乔木层碳储量(1.90 Tg C)最大.宁夏地区森林生态系统平均碳密度为265.74 Mg C·hm^-2,碳储量为43.54 Tg C,其中,植被层平均碳密度为27.24 Mg C·hm^-2、碳储量为4.46 Tg C,土壤层碳储量是植被层的8.76倍.宁夏地区的森林碳储量整体呈南高北低分布,总量较低.这与其森林面积小和林龄结构低龄化有很大关系.随着林龄结构的改善和林业生态工程的进一步实施,宁夏森林生态系统将发挥巨大的固碳潜力.     

Research on characteristics of biomass distribution in urban forests of Shanghai metropolis based on remote sensing and spatial analysis

Aims
Monitoring and quantifying the biomass and its distribution in urban trees and forests are crucial to understanding the role of vegetation in an urban environment. In this paper, an estimation method for biomass of urban forests was developed for the Shanghai metropolis, China, based on spatial analysis and a wide variety of data from field inventory and remote sensing.
Methods
An optimal regression model between forest biomass and auxiliary variables was established by stepwise regression analysis. The residual value of regression model was computed for each of the sites sampled and interpolated by Inverse-distance weighting (IDW) to predict residual errors of other sites not subjected to sampling. Forest biomass in the study area was estimated by combining the regression model based on remote sensing image data and residual errors of spatial distribution map. According to the distribution of plantations and management practices, a total of 93 sample plots were established between June 2011 and June 2012 in the Shanghai metropolis. To determine a suitable model, several spectral vegetation indices relating to forest biomass and structure such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and new images synthesized through band combinations such as the sum of TM2, TM3 and TM4 (denoted Band 234), and the sum of TM3, TM4 and TM5 (denoted Band 345) were used as alternative auxiliary parameters .
Important findings
The biomass density in urban forests of the Shanghai metropolis varied from 15 to 120 t·hm^-2. The higher densities of forest biomass concentrated mostly in the urban areas, e.g. in districts of Jing’an and Huangpu, mostly ranging from 35 to 70 t·hm^-2. Suburban localities such as the districts of Jiading and Qingpu had lower biomass densities at around 15 to 50 t·hm^-2. The biomass density of Cinnamomum camphora trees across the Shanghai metropolis varied between 20 and 110 t·hm^-2. The spatial biomass distribution of urban forests displayed a tendency of higher densities in northeastern areas and lower densities in southwestern areas. The total biomass was 3.57 million tons (Tg) for urban forests and 1.33 Tg for C. camphora trees. The overall forest biomass was also found to be distributed mostly in the suburban areas with a fraction of 93.9%, whereas the urban areas shared a fraction of only 6.1%. In terms of the areas, the suburban and urban forests accounted for 95.44% and 4.56%, respectively, of the total areas in the Shanghai metropolis. Among all the administrative districts, the Chongming county and the new district of Pudong had the highest and the second highest biomass, accounting for 20.1% and 19.18% of the total forest biomass, respectively. In contrast, the Jing’an district accounted for only 0.11% of the total forest biomass. The root-mean-square error (RMSE), mean absolute error (MAE) and mean relative error (MRE) of the model for estimating urban forest biomass in this study were 8.39, 6.86 and 24.22%, respectively, decreasing by 57.69%, 55.43% and 64.00% compared to the original simple regression model and by 62.21%, 58.50%, 65.40% compared to the spatial analysis method. Our results indicated that a more efficient way to estimate urban forest biomass in the Shanghai metropolis might be achieved by combining spatial analysis with regression analysis. In fact, the estimated results based on the proposed model are also more comparable to the up-scaled forest inventory data at a city scale than the results obtained using regression analysis or spatial analysis alone.     

Carbon storage of the forests and its spatial pattern in Nei Mongol,China

Aims
Forest carbon storage in Nei Mongol plays a significant role in national terrestrial carbon budget due to its large area in China. Our objectives were to estimate the carbon storage in the forest ecosystems in Nei Mongol and to quantify its spatial pattern.
Methods
Field survey and sampling were conducted at 137 sites that distributed evenly across the forest types in the study region. At each site, the ecosystem carbon density was estimated thorough sampling and measuring different pools of soil (0-100 cm) and vegetation, including biomass of tree, grass, shrub, and litter. Regional carbon storage was calculated with the estimated carbon density for each forest type.
Important findings
Carbon storage of vegetation layer in forests in Nei Mongol was 787.8 Tg C, with the biomass of tree, litter, herbaceous and shrub accounting for 93.5%, 3.0%, 2.7% and 0.8%, respectively. Carbon density of vegetation layer was 40.4 t·hm^-2, with 35.6 t·hm^-2 in trees, 2.9 t·hm^-2 in litter, 1.2 t·hm^-2 in herbaceous and 0.6 t·hm^-2 in shrubs. In comparison, carbon storage of soil layer in forests in Nei Mongol was 2449.6 Tg C, with 79.8% distributed in the first 30 cm. Carbon density of soil layer was 144.4 t·hm^-2. Carbon storage of forest ecosystem in Nei Mongol was 3237.4 Tg C, with vegetation and soil accounting for 24.3% and 75.7%, respectively. Carbon density of forest ecosystems in Nei Mongol was 184.5 t·hm^-2. Carbon density of soil layer was positively correlated with that of vegetation layer. Spatially, both carbon storage and carbon density were higher in the eastern area, where the climate is more humid. Forest reserves and artificial afforestations can significantly improve the capacity of regional carbon sink.     

Present status and rate of carbon sequestration of forest vegetation in Jilin Province,Northeast China

Aims
Forests represent the most important component of the terrestrial biological carbon pool and play an important role in the global carbon cycle. The regional scale estimation of carbon budgets of forest ecosystems, however, have high uncertainties because of the different data sources, estimation methods and so on. Our objective was to accurately estimate the carbon storage, density and sequestration rate in forest vegetation in Jilin Province of China, in order to understand the role of the carbon sink and to better manage forest ecosystems.
Methods
Vegetation survey data were used to determine forest distribution, size of area and vegetation types regionally. In our study, 561 plots were investigated to build volume-biomass models; 288 plots of shrubs and herbs were harvested to calculate the biomass of understory vegetation, and samples of trees, shrubs and herbs were collected to analyze carbon content. Carbon storage, density and sequestration rate were estimated by two forest inventory data (2009 and 2014), combined with volume-biomass models, the average biomass of understory vegetation and carbon content of vegetation. Finally, the distribution patterns of carbon pools were presented using ArcGIS soft ware.
Important findings
Understory vegetation biomass overall was less than 3% of the tree layer biomass, varying greatly among different forest types and even among the similar types. The carbon content of trees was between 45.80%-52.97%, and that of the coniferous forests was higher than that of the broadleaf forests. The carbon content of shrub and herb layers was about 39.79%-47.25% and 40%, respectively. Therefore, the vegetation carbon conversion coefficient was 0.47 or 0.48 in Jilin Province, and the conventional use of 0.50 or 0.45 would cause deviation of ±5.26%. The vegetation carbon pool of Jilin Province was at the upper range of regional carbon pool and had higher capacity of carbon sequestration. The value in 2009 and 2014 was 471.29 Tg C and 505.76 Tg C, respectively, and the total increase was 34.47 Tg C with average annual growth of 6.89 Tg C·a^-1. The corresponding carbon sequestration rate was 0.92 t·hm^-2·a^-1. The carbon density rose from 64.58 t·hm^-2 in 2009 to 66.68 t·hm^-2 in 2014, with an average increase of 2.10 t·hm^-2. In addition, the carbon storage of the Quercus mongolica forests and broadleaved mixed forests, accounted for 90.34% of that of all forests. The carbon increment followed the order of young > over-mature > near mature > middle-aged > mature forests. The carbon sequestration rate of followed the order of over-mature > young > near mature > middle-aged > mature forests. Both the carbon increment and the carbon sequestration rate of mature forests were negative. Furthermore, spatially the carbon storage and density were higher in the east than in the west of Jilin province, while the carbon increment was higher in northeast and middle east than in the west. The carbon sequestration rate was higher in Tonghua and Baishan in the south, followed by Jinlin in the middle and Yanbian in the east, while Baicheng and Songyuan, etc. in west showed negative values.     

Carbon storage,spatial distribution and the influence factors in Tianshan forests

Aims
Accurate estimation of carbon density and storage is among the key challenges in evaluating ecosystem carbon sink potentials for reducing atmospheric CO₂ concentration. It is also important for developing future conservation strategies and sustainable practices. Our objectives were to estimate the ecosystem carbon density and storage of Picea schrenkiana forests in Tianshan region of Xinjiang, and to analyze the spatial distribution and influencing factors.
Methods
Based on field measurements, the forest resource inventories, and laboratory analyses, we studied the carbon storage, its spatial distribution, and the potential influencing factors in Picea schrenkiana forest of Tianshan. Field surveys of 70 sites, with 800 m² (28.3 m × 28.3 m) for plot size, was conducted in 2011 for quantifying arbor biomass (leaf, branch, trunk and root), grass and litterfall biomass, soil bulk density, and other laboratory analyses of vegetation carbon content, soil organic carbon content, etc.
Important findings
The carbon content of the leaf, branch, trunk and root of Picea schrenkiana is varied from 46.56% to 52.22%. The vegetation carbon content of arbor and the herbatious/litterfall layer was 49% and 42%, respectively. The forest biomass of Picea schrenkiana was 187.98 Mg·hm^-2, with 98.93% found in the arbor layer. The biomass in all layers was in the order of trunk (109.81 Mg·hm^-2) > root (39.79 Mg·hm^-2) > branch (23.62 Mg·hm^-2) > leaf (12.76 Mg·hm^-2). From the age-group point of view, the highest and the lowest biomass was found at the mature forest (228.74 Mg·hm^-2) and young forest (146.77 Mg·hm^-2), respectively. The carbon density and storage were 544.57 Mg·hm^-2 and 290.84 Tg C, with vegetation portion of 92.57 Mg·hm^-2 and 53.14 Tg C, and soil portion of 452.00 Mg·hm^-2 and 237.70 Tg C, respectively. The spatial distribution of carbon density and storage appeared higher in the western areas than those in the eastern regions. In the western Tianshan Mountains (e.g., Ili district), carbon density was the highest, whereas the central Tianshan Mountains (e.g., Manas County, Fukang City, Qitai County) also had high carbon density. In the eastern Tianshan Mountains (e.g., Hami City), it was low. This distribution seemed consistent with the changes in environmental conditions. The primary causes of carbon density difference might be a combined effects of multiple environmental factors such as terrain, precipitation, temperature, and soil.     

Carbon storage and its distribution of forest ecosystems in Zhejiang Province,China

Aims
The concentration of CO₂ and other greenhouse gases in the atmosphere has considerably increased over last century and is set to rise further. Forest ecosystems play a key role in reducing CO₂ concentration in the atmosphere and mitigating global climate change. Our objective is to understand carbon storage and its distribution in forest ecosystems in Zhejiang Province, China.
Methods
By using the 8th forest resource inventory data and 2011-2012 field investigation data, we estimated carbon storage, density and its distribution in forest ecosystems of Zhejiang Province.
Important findings
The carbon storage of forest ecosystems in Zhejiang Province was 602.73 Tg, of which 122.88 Tg in tree layer, 16.73 Tg in shrub-herb layer, 11.36 Tg in litter layer and 451.76 Tg in soil layer accounting for 20.39%, 2.78%, 1.88% and 74.95% of the total carbon storage, respectively. The carbon storage of mixed broadleaved forests was 138.03 Tg which ranked the largest (22.90%) among all forest types. The young and middle aged forests which accounted for 70.66% of the total carbon storage were the main body of carbon storage in Zhejiang Province. The carbon density of forest ecosystems in Zhejiang Province was 120.80 t·hm^-2 and that in tree layer, shrub-herb layer, litter layer and soil layer were 24.65 t·hm^-2, 3.36 t·hm^-2, 2.28 t·hm^-2 and 90.51 t·hm^-2, respectively. The significant relationship between soil organic carbon storage and forest ecosystem carbon storage indicated that soil carbon played an important role in shaping forest ecosystem carbon density. Carbon density of tree layer increased with age in natural forests, but decreased in the order over-mature > near-mature > mature > middle-aged > young forest in plantations. The proportions of young and middle aged forests were larger than any other age classes. Thereby, the carbon storage of forest ecosystems in Zhejiang Province could be increased through a proper forest management.