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 bet...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.展开更多
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 susta...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.展开更多
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 t...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.展开更多
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 ...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.展开更多
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 inte...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.展开更多
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 E...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.展开更多
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 carb...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.展开更多
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 target...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.展开更多
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-b...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.展开更多
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 Plate...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.展开更多
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 devel...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.展开更多
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 accu...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.展开更多
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 impo...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.展开更多
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 V...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.展开更多
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...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.展开更多
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 ...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 </span></span><span><span><span style="font-family:"">×<span> 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</span></span></span></span><span><span><span style="font-family:"">,</span></span></span><span><span><span style="font-family:""> 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 w</span></span></span><span><span><span style="font-family:"">as </span></span></span><span><span><span style="font-family:"">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.展开更多
Typical steppe in Inner Mongolia belongs to a part of Central Asia sub-region in Eurasian temperate steppe region. In climate distinct wet and dry season, coherence of water and heat result in single peak type of seas...Typical steppe in Inner Mongolia belongs to a part of Central Asia sub-region in Eurasian temperate steppe region. In climate distinct wet and dry season, coherence of water and heat result in single peak type of seasonal dynamic of steppe biomass. Community biomass has linear regressional equation with community height, its correlation coefficient (R) is 0.959***. Growth rate of biomass in June, July and August is usually at 1.5-3.0 g/m2. d-1. Community standing dead occurs in June and equates green living biomass by mid-September. Community biomass is only standing dead biomass in the mid-October. Biomass, green production and standing dead have linear regressional relation with days of plant growing, their correlation coefficient (R) are 0.9919***, 0.9878*** and 0.9923***, respectively. Yearly dynamic of typical steppe biomass is variable, the maximum value is 2.4 times as much as the minimum. The peak biomass of Stipa grandis steppe was 87g/m2 in dry 1980 and 210g/m2 in rainy 1981, and their展开更多
This study developed allometric models to estimate aboveground biomass and carbon of Prosopis africana and Faidherbia albida. The destructive method was used with a sample of 20 trees per species for the two parkland ...This study developed allometric models to estimate aboveground biomass and carbon of Prosopis africana and Faidherbia albida. The destructive method was used with a sample of 20 trees per species for the two parkland sites. Linear regression with log transformation was used to model aboveground biomass according to dendrometric parameters. Error analysis, including mean absolute percentage of error(MAPE) and root mean square of error(RMSE), was used to select and validate the models for both species. Model 1(biomass according to tree diameter) for P. africana and F. albida were considered more representative. The statistical parameters of these models were R2 = 0.99, MAPE 0.98% and RMSE1.75% for P. africana, and R2 = 0.99, MAPE 1.19%,RMSE 2.37% for F. albida. The average rate of carbon sequestered was significantly different for the two species(P ≤ 0.05). The total amount sequestered per tree averaged0.17 × 10-3 Mg for P. africana and 0.25 × 10-3 Mg for F. albida. These results could be used to develop policies that would lead to the sustainable management of these resources in the dry parklands of Niger.展开更多
Forests are among the most important carbon sinks on earth. However, their complex structure and vast areas preclude accurate estimation of forest carbon stocks.Data sets from forest monitoring using advanced satellit...Forests are among the most important carbon sinks on earth. However, their complex structure and vast areas preclude accurate estimation of forest carbon stocks.Data sets from forest monitoring using advanced satellite imagery are now used in international policy agreements.Data sets enable tracking of emissions of CO_2 into the atmosphere caused by deforestation and other types of land-use changes. The aim of this study is to determine the capability of SPOT-HRG Satellite data to estimate aboveground carbon stock in a district of Darabkola research and training forest, Iran. Preprocessing to eliminate or reduce geometric error and atmospheric error were performed on the images. Using cluster sampling, 165 sample plots were taken. Of 165 plots, 81 were in natural habitats, and 84 were in forest plantations. Following the collection of ground data, biomass and carbon stocks were quantified for the sample plots on a per hectare basis. Nonparametric regression models such as support vector regression were used for modeling purposes with different kernels including linear, sigmoid, polynomial, and radial basis function.The results showed that a third-degree polynomial was the best model for the entire studied areas having an root mean square error, bias and accuracy, respectively, of 38.41,5.31, and 62.2; 42.77, 16.58, and 57.3% for the best polynomial for natural forest; and 44.71, 2.31, and 64.3%for afforestation. Overall, these results indicate that SPOTHRG satellite data and support vector machines are useful for estimating aboveground carbon stock.展开更多
基金supported by grants from the National Natural Science Foundation of China (31971891)the Guangxi Key Research and Development Projects, China (GuikeAB21238004)+1 种基金the Scientific Innovation 2030 Project, China (2022ZD0401703)the Modern AgroIndustry Technology Research System of Maize, China (CARS-02-03)。
文摘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.
基金supported by the National Nature Science Foundations of China(32160269)the International Science and Technology Cooperation Project of Qinghai province of China(2022-HZ-817).
文摘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.
基金This study was supported by the Basic Research Business Fee Project of Universities Directly under the Inner Mongolia Autonomous Region(JY20220108)the Inner Mongolia Autonomous Region Natural Science Foundation Project(2022LHMS03006)+1 种基金the Inner Mongolia University of Technology Doctoral Research Initiation Fund Project(DC2300001284)the Inner Mongolia Autonomous Region Natural Science Foundation Project(2021MS03082).
文摘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.
基金This study was financially supported by the National Natural Science Foundation of China(41501219)the Applied Basic Research Project of Shanxi Province(2016021136)+2 种基金the National College Students'Innovative Entrepreneurial Training Plan Program of China(201910119007)the Research Project of Philosophy and Social Sciences in Colleges and Universities of Shanxi Province(2019W134)the Soft Science Research Project of Shanxi Province(2018041072-1).
文摘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.
基金supported by the National Key Research and Development Program of China(2017YFD0200202)the National Natural Science Foundation of China(U1404315)+1 种基金the China Scholarship Council(201608410278)the Natural Science Foundation of Henan Province(182300410014)。
文摘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.
文摘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.
基金supported by the CAS Strategic Priority Research Program(No.XDA19030402)the National Key Research and Development Program of China(No.2016YFD0300101)+2 种基金the Natural Science Foundation of China(Nos.31571565,31671585)the Key Basic Research Project of the Shandong Natural Science Foundation of China(No.ZR2017ZB0422)Research Funding of Qingdao University(No.41117010153)
文摘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.
基金the Natural Science Foundation of China(Nos.31670552,31971577)China Postdoctoral Science Foundation(No.2019 M651842)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘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.
基金Funding was provided by the Swedish NFI Development Foundationthe Swedish Kempe Foundation (SMK-1847)。
文摘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.
基金supported financially by the Specialized Fund for the Post-Disaster Reconstruction and Heritage Protec-tion in Sichuan Province(5132202019000128)the Everest Scientific Research Program of Chengdu University of Technology(80000-2021ZF11410)+3 种基金the Second Tibetan Plateau Scientific Expedition and Research Program(STEP,2019QZKK0307)the State Key Laborato-ry of Geohazard Prevention and Geoenvironment Protection Independent Research Project(SKLGP2018Z004)the key technologies of Mountain rail transit green construction in ecologically sensitive region based on Mountain rail transit from Dujiangyan to Mt.Siguniang anti-poverty project(2018-zl-08)Study on risk identification and countermeasures of Sichuan-Tibet Railway Major Projects(2019YFG0460)。
文摘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.
基金Under the auspices of the Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDA19030202)National Key Research and Development Program of China (No. 2020YFE0200800)+1 种基金International Cooperation and Exchange of National Natural Science Foundation of China (No. 31761143018)National Natural Science Foundation of China (No.42071344)。
文摘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.
基金supported by the Special Major Science and Technology Project of Anhui Province(S202103b06020066)the 2020 Annual Graduate Innovation Fund of Anhui Agricultural University(2020YSJ-21)。
文摘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.
基金supported by the National Key R and D Program of China(2016YFC0502101)the National Basic Research Program of China(2013CB956704)the Opening Fund of the State Key Laboratory of Environmental Geochemistry(SKLEG2017911)
文摘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.
基金funded by Vietnam Ministry of Science and Technology under Grant numberDTDL.XH.10/15Vietnam National Foundation for Science&Technology Development(106-NN.06-2016.10)International Foundation for Science(J-1-D-4602-3)。
文摘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.
基金supported by the National Key Basic Research and Development Program of China (2016YFC0500703)the National Natural Science Foundation of China (31572452, 41573063, 31870438)
文摘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.
文摘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 </span></span><span><span><span style="font-family:"">×<span> 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</span></span></span></span><span><span><span style="font-family:"">,</span></span></span><span><span><span style="font-family:""> 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 w</span></span></span><span><span><span style="font-family:"">as </span></span></span><span><span><span style="font-family:"">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.
文摘Typical steppe in Inner Mongolia belongs to a part of Central Asia sub-region in Eurasian temperate steppe region. In climate distinct wet and dry season, coherence of water and heat result in single peak type of seasonal dynamic of steppe biomass. Community biomass has linear regressional equation with community height, its correlation coefficient (R) is 0.959***. Growth rate of biomass in June, July and August is usually at 1.5-3.0 g/m2. d-1. Community standing dead occurs in June and equates green living biomass by mid-September. Community biomass is only standing dead biomass in the mid-October. Biomass, green production and standing dead have linear regressional relation with days of plant growing, their correlation coefficient (R) are 0.9919***, 0.9878*** and 0.9923***, respectively. Yearly dynamic of typical steppe biomass is variable, the maximum value is 2.4 times as much as the minimum. The peak biomass of Stipa grandis steppe was 87g/m2 in dry 1980 and 210g/m2 in rainy 1981, and their
基金supported by the project stocks and potential of carbon sequestration under agroforestry parklands in Niger funded by African Forest Forum(AFF)and International Foundation for Science(IFS),Grant No.D/563-1
文摘This study developed allometric models to estimate aboveground biomass and carbon of Prosopis africana and Faidherbia albida. The destructive method was used with a sample of 20 trees per species for the two parkland sites. Linear regression with log transformation was used to model aboveground biomass according to dendrometric parameters. Error analysis, including mean absolute percentage of error(MAPE) and root mean square of error(RMSE), was used to select and validate the models for both species. Model 1(biomass according to tree diameter) for P. africana and F. albida were considered more representative. The statistical parameters of these models were R2 = 0.99, MAPE 0.98% and RMSE1.75% for P. africana, and R2 = 0.99, MAPE 1.19%,RMSE 2.37% for F. albida. The average rate of carbon sequestered was significantly different for the two species(P ≤ 0.05). The total amount sequestered per tree averaged0.17 × 10-3 Mg for P. africana and 0.25 × 10-3 Mg for F. albida. These results could be used to develop policies that would lead to the sustainable management of these resources in the dry parklands of Niger.
基金Project funding:Sari University of Agricultural Sciences and Natural Resources
文摘Forests are among the most important carbon sinks on earth. However, their complex structure and vast areas preclude accurate estimation of forest carbon stocks.Data sets from forest monitoring using advanced satellite imagery are now used in international policy agreements.Data sets enable tracking of emissions of CO_2 into the atmosphere caused by deforestation and other types of land-use changes. The aim of this study is to determine the capability of SPOT-HRG Satellite data to estimate aboveground carbon stock in a district of Darabkola research and training forest, Iran. Preprocessing to eliminate or reduce geometric error and atmospheric error were performed on the images. Using cluster sampling, 165 sample plots were taken. Of 165 plots, 81 were in natural habitats, and 84 were in forest plantations. Following the collection of ground data, biomass and carbon stocks were quantified for the sample plots on a per hectare basis. Nonparametric regression models such as support vector regression were used for modeling purposes with different kernels including linear, sigmoid, polynomial, and radial basis function.The results showed that a third-degree polynomial was the best model for the entire studied areas having an root mean square error, bias and accuracy, respectively, of 38.41,5.31, and 62.2; 42.77, 16.58, and 57.3% for the best polynomial for natural forest; and 44.71, 2.31, and 64.3%for afforestation. Overall, these results indicate that SPOTHRG satellite data and support vector machines are useful for estimating aboveground carbon stock.
