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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Effects of spring fire and slope on the aboveground biomass, and organic C and N dynamics in a semi-arid grassland of northern China

The aboveground primary production is a major source of carbon(C) and nitrogen(N) pool and plays an important role in regulating the response of ecosystem and nutrient cycling to natural and anthropogenic disturbances. To explore the mechanisms underlying the effect of spring fire and topography on the aboveground biomass(AGB) and the soil C and N pool, we conducted a field experiment between April 2014 and August 2016 in a semi-arid grassland of northern China to examine the effects of slope and spring fire, and their potential interactions on the AGB and organic C and total N contents in different plant functional groups(C_3 grasses, C_4 grasses, forbs, Artemisia frigida plants, total grasses and total plants).The dynamics of AGB and the contents of organic C and N in the plants were examined in the burned and unburned plots on different slope positions(upper and lower). There were differences in the total AGB of all plants between the two slope positions. The AGB of grasses was higher on the lower slope than on the upper slope in July. On the lower slope, spring fire marginally or significantly increased the AGB of C_3 grasses, forbs, total grasses and total plants in June and August, but decreased the AGB of C_4 grasses and A.frigida plants from June to August. On the upper slope, however, spring fire significantly increased the AGB of forbs in June, the AGB of C_3 grasses and total grasses in July, and the AGB of forbs and C_4 grasses in August. Spring fire exhibited no significant effect on the total AGB of all plants on the lower and upper slopes in 2014 and 2015. In 2016, the total AGB in the burned plots showed a decreasing trend after fire burning compared with the unburned plots. The different plant functional groups had different responses to slope positions in terms of organic C and N contents in the plants. The lower and upper slopes differed with respect to the organic C and N contents of C_3 grasses, C_4 grasses, total grasses, forbs, A. frigida plants and total plants in different growing months. Slope position and spring fire significantly interacted to affect the AGB and organic C and N contents of C_4 grasses and A. frigida plants. We observed the AGB and organic C and N contents in the plants in a temporal synchronized pattern. Spring fire affected the functional AGB on different slope positions, likely by altering the organic C and N contents and, therefore,it is an important process for C and N cycling in the semi-arid natural grasslands. The findings of this study would facilitate the simulation of ecosystem C and N cycling in the semi-arid grasslands in northern China.

Decadal (2003–2013) changes in liana diversity,abundance and aboveground biomass in four inland tropical dry evergreen forest sites of peninsular India

In 2013, we re-inventoried all lianas （≥1 cm diameter measured at 1.3 m from the rooting point） in four I-ha permanent plots distributed one each in four sites of inland tropical dry evergreen forest on the Coromandel Coast （Pudukottai district） of peninsular India, established in 2003. Among the four sites, Shanmuganathapuram （SP） and Araiyapatti （AP） were much disturbed and the other two sites （Karisakkadu--KR and Maramadakki--MM） were moderately disturbed. We inventoried a total of 3425 lianas representing 37 species of 33 genera and 22 families. Over a decade （2003-2013） liana species richness increased at two sites （MM and SP） and no changes occurred at the other two sites. Liana abundance increased by 210, 211,164 and 162 individuals at sites AP, KR, MM and SP, respectively, and basal area increased （from 1.09 to 1.76 m2 at AP, 0.67 to 0.86 m2 at KR, 1.68 to 2.06 mz at MM, and from 0.44 to 1.06 m2 at SP）. Over a 10-year period, three species （Abrus precatorius, Canavalia virosa, and Cocculus hirsutus） were lost and five species （Gloriosa superba, Ampelocissus tomentosa, Capparis sepiaria, Aganosma cymosa and Tiliacora acuminata） were newly added. Total aboveground biomass increased by 18.5, 0.74, 3.6 and 9.5 Mg ha-1, respectively, at sites AP, KR, MM and SP.

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.

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.