No effect of invasive tree species on aboveground biomass increments of oaks and pines in temperate forests

Prunus serotina and Robinia pseudoacacia are the most widespread invasive trees in Central Europe.In addition,according to climate models,decreased growth of many economically and ecologically important native trees will likely be observed in the future.We aimed to assess the impact of these two neophytes,which differ in the biomass range and nitrogen-fixing abilities observed in Central European conditions,on the relative aboveground biomass increments of native oaks Qucrcus robur and Q.petraea and Scots pine Pinus sylvestris.We aimed to increase our understanding of the relationship between facilitation and competition between woody alien species and overstory native trees.We established 72 circular plots(0.05 ha)in two different forest habitat types and stands varying in age in western Poland.We chose plots with different abundances of the studied neophytes to determine how effects scaled along the quantitative invasion gradient.Furthermore,we collected growth cores of the studied native species,and we calculated aboveground biomass increments at the tree and stand levels.Then,we used generalized linear mixed-effects models to assess the impact of invasive species abundances on relative aboveground biomass increments of native tree species.We did not find a biologically or statistically significant impact of invasive R.pseudoacacia or P.serotina on the relative aboveground,biomass increments of native oaks and pines along the quantitative gradient of invader biomass or on the proportion of total stand biomass accounted for by invaders.The neophytes did not act as native tree growth stimulators but also did not compete with them for resources,which would escalate the negative impact of climate change on pines and oaks.The neophytes should not significantly modify the carbon sequestration capacity of the native species.Our work combines elements of the per capita effect of invasion with research on mixed forest management.

Aboveground biomass stocks of species-rich natural forests in southern China are influenced by stand structural attributes,species richness and precipitation

Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biomass,their influence on biomass stocks in species-rich forests in southern China,a biodiversity hotspot,has rarely been investigated.In this study,we characterized the effects of environmental factors,forest structure,and species diversity on aboveground biomass stocks of 30 plots(1 ha each) in natural forests located within seven nature reserves distributed across subtropical and marginal tropical zones in Guangxi,China.Our results indicate that forest aboveground biomass stocks in this region are lower than those in mature tropical and subtropical forests in other regions.Furthermore,we found that aboveground biomass was positively correlated with stand age,mean annual precipitation,elevation,structural attributes and species richness,although not with species evenness.When we compared stands with the same basal area,we found that aboveground biomass stock was higher in communities with a higher coefficient of variation of diameter at breast height.These findings highlight the importance of maintaining forest structural diversity and species richness to promote aboveground biomass accumulation and reveal the potential impacts of precipitation changes resulting from climate warming on the ecosystem services of subtropical and northern tropical forests in China.Notably,many natural forests in southern China are not fully stocked.Therefore,their continued growth will increase their carbon storage over time.

Genetic dissection of crown root traits and their relationships with aboveground agronomic traits in maize

The crown root system is the most important root component in maize at both the vegetative and reproductive stages. However, the genetic basis of maize crown root traits(CRT) is still unclear, and the relationship between CRT and aboveground agronomic traits in maize is poorly understood. In this study, an association panel including 531 elite maize inbred lines was planted to phenotype the CRT and aboveground agronomic traits in different field environments. We found that root traits were significantly and positively correlated with most aboveground agronomic traits, including flowering time, plant architecture and grain yield. Using a genome-wide association study(GWAS)coupled with resequencing, a total of 115 associated loci and 22 high-confidence candidate genes were identified for CRT. Approximately one-third of the genetic variation in crown root was co-located with 46 QTLs derived from flowering and plant architecture. Furthermore, 103 (89.6%) of 115 crown root loci were located within known domestication-and/or improvement-selective sweeps, suggesting that crown roots might experience indirect selection in maize during domestication and improvement. Furthermore, the expression of Zm00001d036901, a high-confidence candidate gene, may contribute to the phenotypic variation in maize crown roots, and Zm00001d036901 was selected during the domestication and improvement of maize. This study promotes our understanding of the genetic basis of root architecture and provides resources for genomics-enabled improvements in maize root architecture.

基于电子鼻技术的臭灵丹草指纹图谱建立及谱效关系研究

目的:基于超快速气相电子鼻技术,建立15批臭灵丹草饮片的气味指纹图谱,结合谱效相关性分析,筛选与抗氧化活性相关的成分。方法:使用超快速气相电子鼻建立15批臭灵丹草饮片的气味指纹图谱,进行相似度评价。结合AroChemBase数据库进行定性分析。以DPPH自由基、ABTS自由基清除能力为指标评价臭灵丹草饮片挥发性成分的抗氧化活性,结合多元统计分析评价谱效关系,筛选与抗氧化活性关联度较大的成分。结果:建立了15批臭灵丹草饮片的气味指纹图谱,结合AroChemBase数据库标定了14个共有峰,分别为异戊烷、乙叉降冰生烯、α-蒎烯、α-水芹烯、1,8-桉叶素、5-甲基-4-壬烯、柠檬烯、二溴氯丙烷、芳樟醇、甲基丁香酚、十一酸甲酯、β-石竹烯、醋酸十一酯、覆盆子酮。臭灵丹草饮片挥发油清除DPPH、ABTS自由基的IC50分别为6.56~21.26 mg/mL、3.89~18.34 mg/mL,灰色关联度分析及偏最小二乘回归分析结果表明柠檬烯、甲基丁香酚、覆盆子酮等与其抗氧化相关性较大。结论:臭灵丹草饮片超快速气相电子鼻气味指纹图谱结合谱效关系研究初步筛选了与抗氧化能力相关性较大的3个成分,可为臭灵丹草饮片质量控制提供依据。

土壤-超积累植物体系中稀土元素的空间富集-分异特征与富集机理

离子吸附型稀土矿是我国南方特色的优质稀土资源,但由于落后的采矿方式和无节制的开采,大量稀土元素(稀土元素+Y,简称稀土元素)被带入环境中,导致稀土矿区周边出现许多环境污染和植被退化问题。乌毛蕨是一种对稀土元素具有较强耐受性和超强富集能力的蕨类超积累植物,能被用于稀土污染土壤或尾矿的生态修复。本研究以离子吸附型矿区表生土壤上生长的稀土超积累植物乌毛蕨及其根际土为研究对象,通过化学消解和ICP-MS方法测定根际土、根表、根部、叶柄、叶片中稀土元素的含量,分析土壤–植物体系中稀土元素的空间分布、富集与分异特征;采用顺序提取法测定土壤中不同化学形态的稀土元素含量,同时利用微区X射线荧光光谱(μ-XRF)与扫描电镜能谱分析技术(SEM-EDS),阐明乌毛蕨对稀土元素的吸收与富集机理。化学测试结果表明,乌毛蕨对稀土元素有较强的富集和地上转运能力,富集系数(BF)和转移系数(TF)分别为2.61和2.85;植物器官富集能力顺序为:叶片(1750μg/g)>根部(512μg/g)>叶柄(56.5μg/g);植株整体稀土元素配分模式具有与根际土相似的富轻土元素和Ce负异常特征,不同的是,植株整体Eu无异常(根际土δEu=0.51);根际土壤中富集离子交换态的轻稀土离子(34%~64%,(La/Yb)N=2.36),与根系表面呈轻微富轻稀土元素((La/Yb)N=1.29)的现象暗示根际大部分轻稀土元素能以离子形式被根系吸收,重稀土元素与有机配体络合而被吸附在根系表面;稀土元素在向上迁移过程中,(La/Yb)N值下降(叶柄=27.63,叶片=17.17),揭示木质部伤流液的重稀土元素可能比轻稀土元素更容易向上迁移;μ-XRF与SEM-EDS结果显示,乌毛蕨地上部的超富集器官(叶片)将稀土元素主要储存在叶缘的表皮层细胞,且能将大部分非生理需要的稀土元素有效区室化。上述认识揭示了在离子吸附型稀土矿区表生环境中,稀土元素从土壤向植物体内迁移并在地上部富集与分异的过程、规律和主要原因,为今后利用稀土超积累植物实现植物采矿或稀土尾矿生态修复提供科学认知基础。

Species-specific and generalized allometric biomass models for eight Fagaceae species in the understory of evergreen broadleaved forests in subtropical China

Quantifying the biomass of saplings in the regeneration component is critical for understanding biogeochemical processes of forest ecosystems.However,accurate allometric equations have yet to be developed in sufficient detail.To develop species-specific and generalized allometric equations,154 saplings of eight Fagaceae tree species in subtropical China’s evergreen broadleaved forests were collected.Three dendrometric variables,root collar diameter(d),height(h),and crown area(ca)were applied in the model by the weighted nonlinear seemingly unrelated regression method.Using only d as an input variable,the species-specific and generalized allometric equations estimated the aboveground biomass reasonably,with R _(adj)^(2) values generally>0.85.Adding h and/or ca improved the fitting of some biomass components to a certain extent.Generalized equations showed a relatively large coefficient of variation but comparable bias to species-specific equations.Only in the absence of species-specific equations at a given location are generalized equations for mixed species recommended.The developed regression equations can be used to accurately calculate the aboveground biomass of understory Fagaceae regeneration trees in China’s subtropical evergreen broadleaved forests.

Persistence of fertilization effects on soil organic carbon in degraded alpine wetlands in the Yellow River source region

In the restoration of degraded wetlands,fertilization can improve the vegetation-soil-microorganisms complex,thereby affecting the organic carbon content.However,it is currently unclear whether these effects are sustainable.This study employed Biolog-Eco surveys to investigate the changes in vegetation characteristics,soil physicochemical properties,and soil microbial functional diversity in degraded alpine wetlands of the source region of the Yellow River at 3 and 15 months after the application of nitrogen,phosphorus,and organic mixed fertilizer.The following results were obtained:The addition of nitrogen fertilizer and organic compost significantly affects the soil organic carbon content in degraded wetlands.Three months after fertilization,nitrogen addition increases soil organic carbon in both lightly and severely degraded wetlands,whereas after 15 months,organic compost enhanced the soil organic carbon level in severely degraded wetlands.Structural equation modeling indicates that fertilization decreases the soil pH and directly or indirectly influences the soil organic carbon levels through variations in the soil water content and the aboveground biomass of vegetation.Three months after fertilization,nitrogen fertilizer showed a direct positive effect on soil organic carbon.However,organic mixed fertilizer indirectly reduced soil organic carbon by increasing biomass and decreasing soil moisture.After 15 months,none of the fertilizers significantly affected the soil organic carbon level.In summary,it can be inferred that the addition of nitrogen fertilizer lacks sustainability in positively influencing the organic carbon content.

Improving the Accuracy of Vegetation Index Retrieval for Biomass by Combining Ground-UAV Hyperspectral Data-A New Method for Inner Mongolia Typical Grasslands

Grassland biomass is an important parameter of grassland ecosystems.The complexity of the grassland canopy vegetation spectrum makes the long-term assessment of grassland growth a challenge.Few studies have explored the original spectral information of typical grasslands in Inner Mongolia and examined the influence of spectral information on aboveground biomass(AGB)estimation.In order to improve the accuracy of vegetation index inversion of grassland AGB,this study combined ground and Unmanned Aerial Vehicle(UAV)remote sensing technology and screened sensitive bands through ground hyperspectral data transformation and correlation analysis.The narrow band vegetation indices were calculated,and ground and airborne hyperspectral inversion models were established.Finally,the accuracy of the model was verified.The results showed that:(1)The vegetation indices constructed based on the ASD FieldSpec 4 and the UAV were significantly correlated with the dry and fresh weight of AGB.(2)The comparison between measured R^(2) with the prediction R^(2) indicated that the accuracy of the model was the best when using the Soil-Adjusted Vegetation Index(SAVI)as the independent variable in the analysis of AGB(fresh weight/dry weight)and four narrow-band vegetation indices.The SAVI vegetation index showed better applicability for biomass monitoring in typical grassland areas of Inner Mongolia.(3)The obtained ground and airborne hyperspectral data with the optimal vegetation index suggested that the dry weight of AGB has the best fitting effect with airborne hyperspectral data,where y=17.962e^(4.672x),the fitting R^(2) was 0.542,the prediction R^(2)was 0.424,and RMSE and REE were 57.03 and 0.65,respectively.Therefore,established vegetation indices by screening sensitive bands through hyperspectral feature analysis can significantly improve the inversion accuracy of typical grassland biomass in Inner Mongolia.Compared with ground monitoring,airborne hyperspectral monitoring better reflects the inversion of actual surface biomass.It provides a reliable modeling framework for grassland AGB monitoring and scientific and technological support for grazing management.

基于UPLC-QTOF-HRMS的夏天无地上部分生物碱类成分研究

采用UPLC-QTOF-HRMS法探究夏天无地上部分的生物类成分,方法如下:柱温:40℃,进样量:2μL,流速:0.4 mL/min,流动相A:0.1%甲酸,B:乙腈(含0.1%甲酸)梯度洗脱,采用正离子模式,通过相关文献和数据库分析并鉴定生物碱类成分。UPLC-QTOF-HRMS法检测结果中共鉴定出26种生物碱。上述检测结果可对夏天无地上部分开发利用提供理论依据。

Effects of long-term warming on the aboveground biomass and species diversity in an alpine meadow on the Qinghai-Tibetan Plateau of China

Ecosystems in high-altitude regions are more sensitive and respond more rapidly than other ecosystems to global climate warming.The Qinghai-Tibet Plateau(QTP)of China is an ecologically fragile zone that is sensitive to global climate warming.It is of great importance to study the changes in aboveground biomass and species diversity of alpine meadows on the QTP under predicted future climate warming.In this study,we selected an alpine meadow on the QTP as the study object and used infrared radiators as the warming device for a simulation experiment over eight years(2011-2018).We then analyzed the dynamic changes in aboveground biomass and species diversity of the alpine meadow at different time scales,including an early stage of warming(2011-2013)and a late stage of warming(2016-2018),in order to explore the response of alpine meadows to short-term(three years)and long-term warming(eight years).The results showed that the short-term warming increased air temperature by 0.31℃and decreased relative humidity by 2.54%,resulting in the air being warmer and drier.The long-term warming increased air temperature and relative humidity by 0.19℃and 1.47%,respectively,and the air tended to be warmer and wetter.The short-term warming increased soil temperature by 2.44℃and decreased soil moisture by 12.47%,whereas the long-term warming increased soil temperature by 1.76℃and decreased soil moisture by 9.90%.This caused the shallow soil layer to become warmer and drier under both short-term and long-term warming.Furthermore,the degree of soil drought was alleviated with increased warming duration.Under the long-term warming,the importance value and aboveground biomass of plants in different families changed.The importance values of grasses and sedges decreased by 47.56%and 3.67%,respectively,while the importance value of weeds increased by 1.37%.Aboveground biomass of grasses decreased by 36.55%,while those of sedges and weeds increased by 8.09%and 15.24%,respectively.The increase in temperature had a non-significant effect on species diversity.The species diversity indices increased at the early stage of warming and decreased at the late stage of warming,but none of them reached significant levels(P>0.05).Species diversity had no significant correlation with soil temperature and soil moisture under both short-term and long-term warming.Soil temperature and aboveground biomass were positively correlated in the control plots(P=0.014),but negatively correlated under the long-term warming(P=0.013).Therefore,eight years of warming aggravated drought in the shallow soil layer,which is beneficial for the growth of weeds but not for the growth of grasses.Warming changed the structure of alpine meadow communities and had a certain impact on the community species diversity.Our studies have great significance for the protection and effective utilization of alpine vegetation,as well as for the prevention of grassland degradation or desertification in high-altitude regions.

Modeling compatible single-tree aboveground biomass equations for masson pine(Pinus massoniana) in southern China

Because of global climate change, it is necessary to add forest biomass estimation to national forest resource monitoring. The biomass equations developed for forest biomass estimation should be compatible with volume equations. Based on the tree volume and aboveground biomass data of Masson pine （Pinus massoniana Lamb.） in southern China, we constructed one-, two- and three-variable aboveground biomass equations and biomass conversion functions compatible with tree volume equations by using error-in-variable simultaneous equations. The prediction precision of aboveground biomass estimates from one variable equa- tion exceeded 95%. The regressions of aboveground biomass equations were improved slightly when tree height and crown width were used together with diameter on breast height, although the contributions to regressions were statistically insignificant. For the biomass conversion function on one variable, the conversion factor decreased with increasing diameter, but for the conversion function on two variables, the conversion factor increased with increasing diameter but decreased with in- creasing tree height.

Distribution and Estimation of Aboveground Biomass of Alpine Shrubs along an Altitudinal Gradient in a Small Watershed of the Qilian Mountains, China

Shrublands serve as an important component of terrestrial ecosystems, and play an important role in structure and functions of alpine ecosystem.Accurate estimation of biomass is critical to examination of the productivity of alpine ecosystems, due to shrubification under climate change in past decades.In this study, 14 experimental plots and 42 quadrates of the shrubs Potentilla fruticosa and Caragana jubata were selected along altitudes gradients from 3220 to 3650 m a.s.l.(above sea level) on semi-sunny and semi-shady slope in Hulu watershed of Qilian Mountains, China.The foliage, woody component and total aboveground biomass per quadrate were examined using a selective destructive method, then the biomass were estimated via allometric equations based on measured parameters for two shrub species.The results showed that C.jubata accounted for 1–3 times more biomass(480.98 g/m2) than P.fruticosa(191.21 g/m2).The aboveground biomass of both the shrubs varied significantly with altitudinal gradient(P<0.05).Woody component accounted for the larger proportion than foliage component in the total aboveground biomass.The biomass on semi-sunnyslopes(200.27 g/m2 and 509.07 g/m2) was greater than on semi-shady slopes(182.14 g/m2 and 452.89g/m2) at the same altitude band for P.fruticosa and C.jubata.In contrast, the foliage biomass on semi-shady slopes(30.50 g/m2) was greater than on semi-sunny slopes(27.51 g/m2) for two shrubs.Biomass deceased with increasing altitude for P.fruticosa, whereas C.jubata showed a hump-shaped pattern with altitude.Allometric equations were obtained from the easily descriptive parameters of height(H), basal diameter(D) and crown area(C) for biomass of C.jubata and P.fruticosa.Although the equations type and variables comprising of the best model varied among the species, all equations related to biomass were significant(P < 0.005), with determination coefficients(R2) ranging from 0.81 to 0.96.The allometric equations satisfied the requirements of the model, and can be used to estimate the regional scale biomass of P.fruticosa and C.jubata in alpine ecosystems of the Qilian Mountains.

Estimating aboveground biomass in Mu Us Sandy Land using Landsat spectral derived vegetation indices over the past 30 years

Remote sensing is a valuable and effective tool for monitoring and estimating aboveground biomass （AGB） in large areas.The current international research on biomass estimation by remote sensing technique mainly focused on forests,grasslands and crops,with relatively few applications for desert ecosystems.In this paper,Landsat Thematic Mapper （TM）/Enhanced Thematic Mapper Plus （ETM＋） images from 1988 to 2007 and the data of 283 AGB samples in August 2007 were used to estimate the AGB for Mu Us Sandy Land over the past 30 years.Moreover,temporal and spatial distribution characteristics of AGB and influencing factors of climate and underlying surface were also studied.Results show that：（1） Differences of correlations exist in the fitted equations between AGB and different vegetation indices in desert areas.The modified soil adjusted vegetation index （MSAVI） and soil adjusted vegetation index （SAVI） show relatively higher correlations with AGB,while the correlation between normalized difference vegetation index （NDVI） and AGB is relatively lower.Error testing shows that the AGB-MSAVI model established can be used to accurately estimate AGB of Mu Us Sandy Land in August.（2） AGB in Mu Us Sandy Land shows the fluctuant characteristics over the past 30 years,which decreased from the 1980s to the 1990s,and increased from the 1990s to 2007.AGB in 2007 had the highest value,with a total AGB of 3.352×106 t.Moreover,in the 1990s,AGB had the lowest value with a total AGB of 2.328×106 t.（3） AGB with relatively higher values was mainly located in the middle and southern parts of Mu Us Sandy Land in the 1980s.AGB was low in the whole area in the1990s,and relatively higher AGB values were mainly located in the southern parts of Uxin.In 2007,AGB in the whole area was relatively higher than those of the last twenty years,and higher AGB values were mainly located in the northern,western and middle parts of Mu Us Sandy Land.

The importance of aboveground and belowground interspecific interactions in determining crop growth and advantages of peanut/maize intercropping

Intercropping of maize(Zea mays L.) and peanut(Arachis hypogaea L.) often results in greater yields than the respective sole crops. However, there is limited knowledge of aboveground and belowground interspecific interactions between maize and peanut in field. A two-year field experiment was conducted to investigate the effects of interspecific interactions on plant growth and grain yield for a peanut/maize intercropping system under different nitrogen(N) and phosphorus(P) levels. The method of root separation was employed to differentiate belowground from aboveground interspecific interactions. We observed that the global interspecific interaction effect on the shoot biomass of the intercropping system decreased with the coexistence period, and belowground interaction contributed more than aboveground interaction to advantages of the intercropping in terms of shoot biomass and grain yield. There was a positive effect from aboveground and belowground interspecific interactions on crop plant growth in the intercropping system, except that aboveground interaction had a negative effect on peanut during the late coexistence period. The advantage of intercropping on grain came mainly from increased maize yield(means 95%) due to aboveground interspecific competition for light and belowground interaction(61%–72% vs. 28%–39% in fertilizer treatments). There was a negative effect on grain yield from aboveground interaction for peanut, but belowground interspecific interaction positively affected peanut grain yield.The supply of N, P, or N + P increased grain yield of intercropped maize and the contribution from aboveground interspecific interaction. Our study suggests that the advantages of peanut/maize intercropping for yield mainly comes from aboveground interspecific competition for maize and belowground interspecific facilitation for peanut, and their respective yield can be enhanced by N and P. These findings are important for managing the intercropping system and optimizing the benefits from using this system.

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

Background:Aboveground biomass(AGB)is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.Methods:Here,we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China.We used Landsat time-series observations,Advanced Land Observing Satellite(ALOS)Phased Array L-band Synthetic Aperture Radar(PALSAR)data,and National Forest Inventory(NFI)plot measurements,to generate the forest AGB maps at three time points(1992,2002 and 2010)showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong,China.Results:The proposed model was capable of mapping forest AGB using spectral,textural,topographical variables and the radar backscatter coefficients in an effective and reliable manner.The root mean square error of the plotlevel AGB validation was between 15.62 and 53.78 t∙ha^(−1),the mean absolute error ranged from 6.54 to 32.32 t∙ha^(−1),the bias ranged from−2.14 to 1.07 t∙ha^(−1),and the relative improvement over the random forest algorithm was between 3.8%and 17.7%.The largest coefficient of determination(0.81)and the smallest mean absolute error(6.54 t∙ha^(−1)were observed in the 1992 AGB map.The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010.By adding elevation as a covariable,the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals,because co-kriging resulted in better interpolation results in the valleys and plains of the study area.Conclusions:Validation of the three AGB maps with an independent dataset indicated that the random forest/cokriging performed best for AGB prediction,followed by random forest coupled with ordinary kriging(random forest/ordinary kriging),and the random forest model.The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography.The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.

Contribution of aboveground litter to soil respiration in Populus davidiana Dode plantations at different stand ages

Soil respiration from decomposing aboveground litter is a major component of the terrestrial carbon cycle. However, variations in the contribution of aboveground litter to the total soil respiration for stands of varying ages are poorly understood. To assess soil respiration induced by aboveground litter, treatments of litter and no litter were applied to 5-, l0-, and 20-year-old stands of Populus davidiana Dode in the sandstorm source area of Beijing-Tianjin, equations were applied to China. Optimal nonlinear model the combined effects of soil temperature and soil water content on soil respiration. Results showed that the monthly average contribution of aboveground litter to total soil respiration were 18.46% ± 4.63%, 16.64% ± 9.31%, and 22.37% ± 8.17% for 5-, 10-, and ao-year-old stands, respectively. The relatively high contribution in 5- and 20-year-old stands could be attributed to easily decomposition products and high accumulated litter, resoectivelv. Also. it fluctuated monthly for all stand ages due to substrate availability caused by phenology and environmental factors. Litter removal significantly decreased soil respiration and soil water content for all stand ages （P 〈 0.05） but not soil temperature （P 〉 0.05）. Variations of soil respiration could be explained by soil temperature at 5-cm depth using an exponential equation and by soil water content at lo-cm depth using a quadratic equation, whereas soil respiration was better modeled using the combined parameters of soil temperature and soil water content than with either soil temperature or soil water content alone. Temperature sensitivity （Q10） increased with stand age in both the litter and the no litter treatments. Considering the effects of aboveground litter, this study provides insights for predicting future soil carbon fluxes and for accurately assessing soil carbon budgets.

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.

Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors

Background: The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging(LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference.Results: Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m×18 m map units was found to range between 9 and 447 Mg·ha^-1. The corresponding root mean square errors ranged between 10 and 162 Mg·ha^-1. For the entire study region, the mean aboveground biomass was 55 Mg·ha^-1 and the corresponding relative root mean square error 8%. At this level 75%of the mean square error was due to the uncertainty associated with tree-level models.Conclusions: Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.

A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal

Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.

Estimating aboveground biomass using Pléiades satellite image in a karst watershed of Guizhou Province,Southwestern China

Biomass in karst terrain has rarely been measured because the steep mountainous limestone terrain has limited the ability to sample woody plants.Satellite observation, especially at high spatial resolution, is an important surrogate for the quantification of the biomass of karst forests and shrublands. In this study, an artificial neural network(ANN) model was built using Pléiades satellite imagery and field biomass measurements to estimate the aboveground biomass(AGB) in the Houzhai River Watershed, which is a typical plateau karst basin in Central Guizhou Province, Southwestern China. A back-propagation ANN model was also developed.Seven vegetation indices, two spectral bands of Pléiades imagery, one geomorphological parameter,and land use/land cover were selected as model inputs. AGB was chosen as an output. The AGB estimated by the allometric functions in 78 quadrats was utilized as training data(54 quadrats, 70%),validation data(12 quadrats, 15%), and testing data(12 quadrats, 15%). Data-model comparison showed that the ANN model performed well with an absolute root mean square error of 11.85 t/ha, which was 9.88%of the average AGB. Based on the newly developed ANN model, an AGB map of the Houzhai River Watershed was produced. The average predicted AGB of the secondary evergreen and deciduous broadleaved mixed forest, which is the dominant forest type in the watershed, was 120.57 t/ha. The average AGBs of the large distributed shrubland,tussock, and farmland were 38.27, 9.76, and 11.69 t/ha, respectively. The spatial distribution pattern ofthe AGB estimated by the new ANN model in the karst basin was consistent with that of the field investigation. The model can be used to estimate the regional AGB of karst landscapes that are distributed widely over the Yun-Gui Plateau.