Spatial dynamics of aboveground carbon stock in urban green space:a case study of Xi'an,China

Greenhouse gas emission of carbon dioxide（CO2） is one of the major factors causing global climate change.Urban green space plays a key role in regulating the global carbon cycle and reducing atmospheric CO2.Quantifying the carbon stock,distribution and change of urban green space is vital to understanding the role of urban green space in the urban environment.Remote sensing is a valuable and effective tool for monitoring and estimating aboveground carbon（AGC） stock in large areas.In the present study,different remotely-sensed vegetation indices（VIs） were used to develop a regression equation between VI and AGC stock of urban green space,and the best fit model was then used to estimate the AGC stock of urban green space within the beltways of Xi＇an city for the years 2004 and 2010.A map of changes in the spatial distribution patterns of AGC stock was plotted and the possible causes of these changes were analyzed.Results showed that Normalized Difference Vegetation Index（NDVI） correlated moderately well with AGC stock in urban green space.The Difference Vegetation Index（DVI）,Ratio Vegetation Index（RVI）,Soil Adjusted Vegetation Index（SAVI）,Modified Soil Adjusted Vegetation Index（MSAVI） and Renormalized Difference Vegetative Index（RDVI） were lower correlation coefficients than NDVI.The AGC stock in the urban green space of Xi＇an in 2004 and 2010 was 73,843 and 126,621 t,respectively,with an average annual growth of 8,796 t and an average annual growth rate of 11.9%.The carbon densities in 2004 and 2010 were 1.62 and 2.77 t/hm2,respectively.Precipitation was not an important factor to influence the changes of AGC stock in the urban green space of Xi＇an.Policy orientation,major ecological greening projects such as ＂transplanting big trees into the city＂ and the World Horticultural Exposition were found to have an important impact on changes in the spatiotemporal patterns of AGC stock.

Estimation of aboveground tree carbon stock using SPOT-HRG data (a case study: Darabkola forests)

Forests are among the most important carbon sinks on earth. However, their complex structure and vast areas preclude accurate estimation of forest carbon stocks.Data sets from forest monitoring using advanced satellite imagery are now used in international policy agreements.Data sets enable tracking of emissions of COinto the atmosphere caused by deforestation and other types of land-use changes. The aim of this study is to determine the capability of SPOT-HRG Satellite data to estimate aboveground carbon stock in a district of Darabkola research and training forest, Iran. Preprocessing to eliminate or reduce geometric error and atmospheric error were performed on the images. Using cluster sampling, 165 sample plots were taken. Of 165 plots, 81 were in natural habitats, and 84 were in forest plantations. Following the collection of ground data, biomass and carbon stocks were quantified for the sample plots on a per hectare basis. Nonparametric regression models such as support vector regression were used for modeling purposes with different kernels including linear, sigmoid, polynomial, and radial basis function.The results showed that a third-degree polynomial was the best model for the entire studied areas having an root mean square error, bias and accuracy, respectively, of 38.41,5.31, and 62.2; 42.77, 16.58, and 57.3% for the best polynomial for natural forest; and 44.71, 2.31, and 64.3%for afforestation. Overall, these results indicate that SPOTHRG satellite data and support vector machines are useful for estimating aboveground carbon stock.

Carbon storage and net primary productivity in Canadian boreal mixedwood stands

Canadian boreal mixedwood forests are extensive,with large potential for carbon sequestration and storage;thus,knowledge of their carbon stocks at different stand ages is needed to adapt forest management practices to help meet climate-change mitigation goals.Carbon stocks were quantified at three Ontario boreal mixedwood sites.A harvested stand,a juvenile stand replanted with spruce seedlings and a mature stand had total carbon stocks(±SE)of 133±13 at age 2,130±13 at age 25,and 207±15 Mg C ha^-1 at age 81 years.At the clear-cut site,stocks were reduced by about 40%or 90 Mg C ha^-1 at harvest.Vegetation held 27,34 and 62%of stocks,while detritus held 34,29 and 13%of stocks at age 2,25 and 81,respectively.Mineral soil carbon stocks averaged 51 Mg C ha^-1,and held 38,37 and 25%of stocks.Aboveground net primary productivity(±SE)in the harvested and juvenile stand was 2.1±0.2 and 3.7±0.3 Mg C ha^-1 per annum(p.a.),compared to 2.6±2.5 Mg C ha^-1 p.a.in the mature stand.The mature canopies studied had typical boreal mixedwood composition and mean carbon densities of 208 Mg C ha^-1,which is above average for managed Canadian boreal forest ecosystems.A comparison of published results from Canadian boreal forest ecosystems showed that carbon stocks in mixedwood stands are typically higher than coniferous stands at all ages,which was also true for stocks in vegetation and detritus.Also,aboveground net primary productivity was typically found to be higher in mixedwood than in coniferous boreal forest stands over a range of ages.Measurements from this study,together with those published from the other boreal forest stands demonstrate the potential for enhanced carbon sequestration through modified forest management practices to take advantage of Canadian boreal mixedwood stand characteristics.

北方森林乔木层碳储量的估计及空间分析

利用遥感的方式对森林乔木层碳储量(Aboveground Biomass Carbon Stocks,ABGCS)以及乔木层碳储量的光饱和值进行精准估测,以期替代传统大面积调查的繁琐工序,为碳储量的估测提供参考和依据,提高森林可持续经营管理的效率。以2017年黑龙江省伊春市嘉荫县森林乔木层碳储量(ABGCS)作为研究对象,利用Landsat8 OLI遥感影像以及森林资源二类调查数据,构建参数模型多元逐步回归模型(Stepwise Multiple-Regression,SMR),非参数模型BP神经网络模型(BP neural network,BP-NN)、随机森林模型(Random Forest,RF)、支持向量回归模型(Support Vector Machine,SVR)对嘉荫县地区ABGCS进行估测和反演其空间分布情况。研究结果表明,非参数模型的估测精度明显高于参数模型,其中3种非参数模型(BP-NN、RF、SVR)相较于参数模型(SMR),拟合精度分别提高了25.0%、12.2%、7.3%;综合比较4种模型十折交叉验证的评价指标,分析得出模型性能优劣为BP-NN>RF>SVR>SMR,其中BP-NN模型拟合出最大的决定系数(R2为0.785)和最小的均方根误差(RMSE为3.572 t/hm2)、均方误差(MSE为12.757 t/hm2)、平均绝对误差(MAE为2.687 t/hm2);从碳储量残差分段检验结果来看,4种模型均存在碳储量不同程度上高值低估和低值高估的情况,BP-NN模型在各碳储量分段的平均残差(ME)和相对平均残差(MRE)值均为最小,其泛化能力较强;利用立方项模型确定ABGCS的光饱和值为63.056 t/hm2,与BP-NN所预测的ABGCS光饱和值接近(64.232 t/hm2)。因此,BP-NN模型在估测嘉荫县ABGCS具有较为理想的效果,为森林碳储量动态监测及研究提供重要依据。

兼顾碳贮量和木材生产目标的森林经营规划研究

以吉林省汪清林业局金沟岭林场为对象,基于小班调查数据,以木材生产和碳增量净现值最大为规划目标,考虑采伐量不大于生长量、均衡采伐、生长模型等约束,建立了多目标规划模型。通过LINGO软件求解,得到了50a规划期的最优经营方案,并进行了灵敏度分析。结果表明:各个森林类型在不同分期间伐强度在1%～15%之间,择伐强度在1%～35%之间。规划期木材总采伐量为182.33万m3,规划期末地上碳贮增量为47.92万t;规划期总收益为95 434.40万元,其中木材净现值91 895.10万元,碳贮增量净现值3 539.30万元。多目标经营方案可以同时满足对木材生产和碳贮增量的需求,是一个折衷的方案。与木材生产经营方案相比,多目标经营方案和碳贮增量经营方案在规划期内的木材净现值分别减少2.67%、45.43%,但地上碳贮增量净现值分别增加29.88%、50.42%。因此增加碳贮增量要以减少木材采伐量为代价。分析了低、中、高3种碳价格对多目标经营方案的影响,表明整个规划期内木材采伐量随着碳价格的增加而减少,而碳贮增量随着碳价格的增加而增加。

半干旱沙地生境变化对植物地上生物量及其碳、氮储量的影响

研究了科尔沁沙地沙丘固定过程中的流动沙丘、半固定沙丘和固定沙丘以及草地4种生境类型的植物地上生物量及其碳、氮含量与储量特征。结果表明,沙地生境变化对植物叶干重、枝干重、总生物量及其碳、氮含量与储量均有显著影响(P<0.01);固定沙丘的叶干重、枝干重、总生物量及其碳氮储量均大于流动沙丘与半固定沙丘,草地的叶干重、枝干重、总生物量、枝和总的碳储量、枝的氮储量均小于固定沙丘而大于流动沙丘;半固定沙丘叶、枝及总碳含量与枝的碳储量大于流动沙丘,而叶、枝及总氮含量小于流动沙丘。一年生植物分别占流动沙丘、固定沙丘和草地总生物量的63.99%,79.28%,70.86%;灌木占半固定沙丘总生物量的73.15%;C3植物生物量分别占流动、半固定、固定沙丘和草地总生物量的39.99%,90.87%,96.01%和82.67%。固定沙丘上的一年生植物和C3植物的生物量、碳氮储量在4个生境中最高;半固定、固定沙丘和草地中C3植物碳含量及其储量、氮储量均高于C4植物。沙丘固定过程中生物量及其碳氮储量逐渐增加,固定沙丘植被具有较大的碳氮固存潜力,一年生植物、C3植物对其碳氮的固存具有重要的贡献作用;沙丘固定过程中C3植物对C4植物生物量的下降具补偿作用。

东北针叶林与阔叶林乔灌草组成特征及碳汇功能对比研究

【目的】针叶林和阔叶林构成了北半球高纬度地区森林的主体,科学区分二者林分特征和碳汇功能差异预期助力基于林型差异的精准林分管理和多样性保护。【方法】本文以东北大、小兴安岭地区森林为研究对象,共调查野外1275块乔木样地(针叶林698块,阔叶林577块),详细记录植物科、属、种、乔灌草植株大小与密度、地理位置(经纬度、海拔、坡向、坡位、坡度),计算优势种相对多度、多样性特征、地上碳储量,并以树种耐逆境特征与耐分解特征为基础,评价生物量碳汇功能的稳定性。【结果】(1)针叶林共有植物479种,隶属于79科228属,阔叶林共有植物546种,隶属于81科,255属。针、阔叶林乔、灌、草优势种变化明显,以乔木种类差异最大:针叶林以落叶松、红松、樟子松更多,而阔叶林以白桦、山杨和蒙古栎等多度更高。针叶林灌木以越橘和绣线菊为主,较阔叶林高1.6~2.3倍。整体来看乔木多达90%指标针阔叶林差异显著,而灌木为65%,草本为35%。(2)针叶林树更高更粗,较阔叶林高1.5 m、胸径粗2.4 cm,但密度较阔叶林低15%;灌木高度与盖度林分间差异不明显,针叶林草本植物高度较矮、但密度更高。(3)针叶林的碳储量、耐分解稳定性显著高于阔叶林,分别高25.54%和43.24%,但环境稳定较阔叶林低8%(P<0.05)。相比阔叶林,禾本科在针叶林重要性从7.5%增加10.2%。相比而言,同期林业普查碳储量数据可能低估林分生物量碳密度35%左右。(4)与历史数据相比,针、阔叶林乔木高度均降低数米,林分密度高700~1000株/hm^(2),重要林下资源植物减少明显,保守估计森林层厚度年降低量10 cm以上,生态服务功能损失值得警惕。(5)与邻国俄罗斯相比,我国森林珍贵的常绿松类、云杉和冷杉树种占比很低,而落叶松和杨桦占比过大,生物量碳储量与俄罗斯最低质量森林相当。【结论】本研究基于大量野外样方调查对东北森林带核心区域针、阔叶林乔灌草植物组成、个体大小与碳汇功能的差异进行了细致刻画,并与历史数据和邻国俄罗斯数据进行了比较。相关结果为东北地区的森林管理提供了基础数据,建议深入开展林分树种组成变化的长期生态学意义与生态风险管控研究,加强基于林分差异的精准管理,并继续加强保护力度,提升生态服务功能,助力国家双碳目标实现。

利用皆伐法估算黔中喀斯特森林地上生物量

精确估算森林生物量对理解全球碳循环至关重要。已有的喀斯特森林地上生物量估算研究存在很高的不确定性,缺乏校准数据检验研究结果的精度。利用皆伐法,首次精确估算了我国西南贵州省中部喀斯特森林的地上生物量,并检验了已有生物量回归方程和平均标准木法对该喀斯特森林地上生物量的估算效果。该喀斯特森林的地上生物量为122.81 Mg/hm^2,胸径(D)≥1 cm的木本植物、D<1 cm的木本植物和草本植物的地上生物量分别为120.00、2.56、0.24 Mg/hm^2。D在10—30 cm范围内的植株(83.89 Mg/hm^2)是地上生物量的主要贡献者。4个优势树种(云南鼠刺Itea yunnanensis、川钓樟Lindera pulcherrima、猴樟Cinnamomum bodinieri和化香树Platycarya strobilacea的地上生物量为103.03 Mg/hm^2,占森林总地上生物量的83.89%。干(61.04 Mg/hm^2)和枝(40.56 Mg/hm^2)的生物量远高于皮(11.61 Mg/hm^2)和叶(6.80 Mg/hm^2)。在物种水平上,已有生物量回归方程(误差-56.10%—84.61%)和平均标准木法(误差-36.43%—-5.14%)对该喀斯特森林地上生物量的估算效果均较差。最后,建立了5个新的生物量回归方程。本研究可为我国西南喀斯特地区精确估算森林碳储量提供基础校验数据和方法指导。