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.

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.

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.

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.

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.

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.

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.

Remote Sensing-based Spatiotemporal Distribution of Grassland Aboveground Biomass and Its Response to Climate Change in the Hindu Kush Himalayan Region

The grassland in the Hindu Kush Himalayan(HKH) region is one of the large st and most biodiverse mountain grassland types in the world,and its ecosystem service functions have profound impacts on the sustainable development of the HKH region.Monitoring the spatiotemporal distribution of grassland aboveground biomass(AGB) accurately and quantifying its response to climate change are indispensable sources of information for sustainably managing grassland ecosystems in the HKH region.In this study,a pure vegetation index model(PVIM) was applied to estimate the long-term dynamics of grassland AGB in the HKH region during 2000-2018.We further quantified the response of grassland AGB to climate change(temperature and precipitation) by partial correlation and variance partitioning analyses and then compared their differences with elevation.Our results demonstrated that the grassland AGB predicted by the PVIM had a good linear relationship with the ground sampling data.The grassland AGB distribution pattern showed a decreasing trend from east to west across the HKH region except in the southern Himalayas.From 2000 to 2018,the mean AGB of the HKH region increased at a rate of 1.57 g/(m~2·yr) and ranged from 252.9(2000) to 307.8 g/m~2(2018).AGB had a positive correlation with precipitation in more than 80% of the grassland,and temperature was positively correlated with AGB in approximately half of the region.The change in grassland AGB was more responsive to the cumulative effect of annual precipitation,while it was more sensitive to the change in temperature in the growing season;in addition,the influence of climate varied at different elevations.Moreover,compared with that of temperature,the contribution of precipitation to grassland AGB change was greater in approximately 60% of the grassland,but the differences in the contribution for each climate factor were small between the two temporal scales at elevations over 2000 m.An accurate assessment of the temporal and spatial distributions of grassland AGB and the quantification of its response to climate change are of great significance for grassland management and sustainable development in the HKH region.

Incorporating topographic factors in nonlinear mixed-effects models for aboveground biomass of natural Simao pine in Yunnan,China

A total of 128 Simao pine trees （Pinus kesiya var. langbianensis） from three regions of Pu＇er City, Yunnan Province, People＇s Republic of China, were destructively sampled to obtain tree aboveground biomass （AGB）. Tree variables such as diameter at breast height and total height, and topographical factors such as altitude, aspect of slope, and degree of slope were recorded. We considered the region and site quality classes as the ran- dom-effects, and the topographic variables as the fixed- effects. We fitted a total of eight models as follows： least- squares nonlinear models （BM）, least-squares nonlinear models with the topographic factors （BMT）, nonlinear mixed-effects models with region as single random-effects （NLME-RE）, nonlinear mixed-effects models with site as single random-effects （NLME-SE）, nonlinear mixed-ef- fects models with the two-level nested region and site random-effects （TLNLME）, NLME-RE with the fixed-ef- fects of topographic factors （NLMET-RE）, NLME-SE with the fixed-effects of topographic factors （NLMET-SE）, and TLNLME with the fixed-effects of topographic factors （TLNLMET）. The eight models were compared by modelfitting and prediction statistics. The results showed： model fitting was improved by considering random-effects of region or site, or both. The models with the fixed-effects of topographic factors had better model fitting. According to AIC and BIC, the model fitting was ranked as TLNLME 〉 NLMET-RE 〉 NLME-RE.〉 NLMET-SE 〉 TLNLMET 〉 NLME-SE 〉 BMT 〉 BM. The differences among these models for model prediction were small. The model pre- diction was ranked as TLNLME 〉 NLME-RE 〉 NLME- SE 〉 NLMET-RE 〉 NLMET-SE 〉 TLNLMET 〉 BMT 〉 BM. However, all eight models had relatively high prediction precision （〉90 %）. Thus, the best model should be chosen based on the available data when using the model to predict individual tree AGB.

Allometric models for aboveground biomass of six common subtropical shrubs and small trees

The aboveground biomass(AGB)of shrubs and small trees is the main component for the productivity and carbon storage of understory vegetation in subtropical secondary forests.However,few allometric models exist to accurately evaluate understory biomass.To estimate the AGB of five common shrub(diameter at base<5 cm,<5 m high)and one small tree species(<8 m high,trees’s seedling),206 individuals were harvested and species-specific and multi-species allometric models developed based on four predictors,height(H),stem diameter(D),crown area(Ca),and wood density(ρ).As expected,the six species possessed greater biomass in their stems compared with branches,with the lowest biomass in the leaves.Species-specific allometric models that employed stem diameter and the combined variables of D~2H andρDH as predictors accurately estimated the components and total AGB,with R^(2) values from 0.602 and 0.971.A multi-species shrub allometric model revealed that wood density×diameter×height(ρDH)was the best predictor,with R^(2) values ranging from between 0.81 and 0.89 for the components and total AGB,respectively.These results indicated that height(H)and diameter(D)were effective predictors for the models to estimate the AGB of the six species,and the introduction of wood density(ρ)improved their accuracy.The optimal models selected in this study could be applied to estimate the biomass of shrubs and small trees in subtropical regions.

Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve,Eastern Tibet Plateau

Accurate estimates of forest aboveground biomass(AGB)are critical for supporting strategies of ecosystem conservation and climate change mitigation.The Jiuzhaigou National Nature Reserve,located in Eastern Tibet Plateau,has rich forest resources on steep slopes and is very sensitive to climate change but plays an important role in the regulation of regional carbon cycles.However,an estimation of AGB of subalpine forests in the Nature Reserve has not been carried out and whether a global biomass model is available has not been determined.To provide this information,Landsat 8 OLI and Sentinel-2B data were combined to estimate subalpine forest AGB using linear regression,and two machine learning approaches–random forest and extreme gradient boosting,with 54 inventory plots.Regardless of forest type,Observed AGB of the Reserve varied from 61.7 to 475.1 Mg hawith an average of 180.6 Mg ha.Results indicate that integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency regardless of modelling approaches.The results highlight a potential way to improve the prediction of forest AGB in mountainous regions.Modelled AGB indicated a strong spatial variability.However,the modelled biomass varied greatly with global biomass products,indicating that global biomass products should be evaluated in regional AGB estimates and more field observations are required,particularly for areas with complex terrain to improve model accuracy.

The aboveground biomass of desert steppe and its spatiotemporal variation in western Inner Mongolia

A precise understanding of the aboveground biomass of desert steppe and its spatio-temporal variation is important to understand how arid ecosystems respond to climate change and to ensure that scarce grassland resources are used rationally. On the basis of 756 ground survey quadrats sampled in western Inner Mongolia steppe in 2005-2011 and remote sensing data from the Moderate Resolu- tion Imaging Spectroradiometer （MODIS）--the normalized difference vegetation index （NDVI） dataset for the period of 2001-2011--we developed a statistical model to estimate the aboveground biomass of the desert steppe and further explored the rela- tionships between aboveground biomass and climate factors. The conclusions are as follows： （1） the aboveground biomass of the steppe in the research area was 5.27 Tg （1 Tg=1012 g） on average over 11 years; between 2001 and 2011, the aboveground biomass of the western Inner Mongolia steppe exhibited fluctuations, with no significant trend over time; （2） the aboveground biomass of the steppe in the research area exhibits distinct spatial variation and generally decreases gradually from southeast to northwest; and （3） the important factor causing intemnnual variations in aboveground biomass is precipitation during the period from January to July, but we did not find a significant relationship between the aboveground biomass and the corresponding temperature changes. The precipitation in this period is also an important factor influencing the spatial distribution of aboveground biomass （R2=0.39, P〈0.001）, while the temperature might be a minor factor （R2=0.12, P〈0.01 ）. The uncertainties in our estimate are primarily due to uncertainty in converting the fresh grass yield estimates to dry weight, underestimates of the biomass of shrubs, and error in remote sensing dataset.

Aboveground Biomass and Water Storage Allocation in Alpine Willow Shrubs in the Qilian Mountains in China

The aboveground biomass allocation and water relations in alpine shrubs can provide useful information on analyzing their ecological and hydrological functions in alpine regions. The objectives of this study were to compare the aboveground biomass allocation, water storage ratio and distribution between foliage/woody components,and to investigate factors affecting aboveground biomass allocation and water storage ratio in alpine willow shrubs in the Qilian Mountains, China. Three experimental sites were selected along distance gradients from the riverside in the Hulu watershed in the Qilian Mountains. The foliage, woody component biomass, and water allocation of Salix cupularis Rehd.and Salix oritrepha Schneid. shrubs were measured using the selective destructive method. The results indicated that the foliage component had higher relative water and biomass storage than the woody component in the upper part of the crown in individual shrubs. However, the woody component was the major biomass and water storage component in the whole shrub level for S. cupularis and S.oritrepha. Moreover, the foliage/woody component biomass ratio decreased from the top to the basal level of shrubs. The relative water storage allocation was significantly affected by species types, but was not affected by sites and interaction between species and sites. Meanwhile, relative water storage was affectedby sites as well as by interaction between sites and species type.

Ecoregional variations of aboveground biomass and stand structure in evergreen broadleaved forests

Biotic and abiotic factors control aboveground biomass(AGB)and the structure of forest ecosystems.This study analyses the variation of AGB and stand structure of evergreen broadleaved forests among six ecoregions of Vietnam.A data set of 1731-ha plots from 52 locations in undisturbed old-growth forests was developed.The results indicate that basal area and AGB are closely correlated with annual precipitation,but not with annual temperature,evaporation or hours of sunshine.Basal area and AGB are positively correlated with trees>30 cm DBH.Most areas surveyed(52.6%)in these old-growth forests had AGB of 100–200 Mg ha^-1;5.2%had AGB of 400–500 Mg ha^-1,and 0.6%had AGB of>800 Mg ha^-1.Seventy percent of the areas surveyed had stand densities of 300–600 ind.ha^-1,and 64%had basal areas of 20–40 m^2 ha^-1.Precipitation is an important factor influencing the AGB of old-growth,evergreen broadleaved forests in Vietnam.Disturbances causing the loss of large-diameter trees(e.g.,>100 cm DBH)affects AGB but may not seriously affect stand density.

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.

Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes

Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets.However,combining the advantages of active and passive data sources to improve estimation accuracy remains challenging.Here,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information.Over 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed approach.The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values.Compared with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests types.The proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf forest.Furthermore,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.

Can Stand Density and Stem Stratification Be Indicators of Aboveground Biomass in Woody Plant Recruitment in Savannah

Stem density and size stratification of woody species are informative of vegetation conditions and its physiognomy in savannah whereas their variation influence woody population functioning. Current study endeavoured to evaluate the stand density and size variability of woody species related to aboveground biomass in a Sudanian savannah. Total height, stem diameter at breast height (dbh) ≥ 5 cm were measured in 30 plots of 50 m × 20 m laid in respect to vegetation type as bowal, shrubland and woodland. Species diversity, stem density, height and basal area were calculated and compared across sites and variation in stem dbh classes evaluated. Total aboveground biomass was estimated and thereafter linear relationships were established between stand density and aboveground biomass, and basal area. Results revealed three different sites with an overall 58 species identified through vegetation type including liana species (4 stems in bowal) with 18 genera and 42 families. Fabaceae Combretaceae, Anacardiaceae and Rubiaceae were dominant families. Small sized trees represented 72% of total stem density considered in structure with significant higher basal area, while large sized trees as 28% were scarcely distributed. More than 70% variation in biomass was due to stem density and basal area with a dominance of small trees. In conclusion increase size in tree community indicated increase in accumulated aboveground biomass as positive regeneration features. But, change in vegetation structure strongly influence negatively species ability to grow from lower to upper size class and later on, disrupt ecosystem functioning. Plant stem density and stratification could be considered as indicators of aboveground biomass fluctuating in regeneration monitoring.