Neotropical forests encompass a diverse array of forest types,each with distinct structures and species compositions shaped by varying soil and climate conditions.Despite their global importance in carbon(C)cycling,th...Neotropical forests encompass a diverse array of forest types,each with distinct structures and species compositions shaped by varying soil and climate conditions.Despite their global importance in carbon(C)cycling,their diversity is underrepresented in Earth system models due to our limited understanding of the abiotic and biotic factors influencing above-ground carbon(AGC)and soil organic carbon(SOC)stocks.We conducted a forest inventory across five neotropical forest types-cloud forest(CF),transition forest(TF),riparian forest(RF),pineoak forest(POF),and tropical dry forest(TDF)-in Nicaragua's Miraflor-Moropotente Protected Landscape(MMPL)to explore these relationships.Across 40 plots,we identified 4,495 trees belonging to 239 species and 62 families,with Fabaceae being the most species-rich.RF exhibited the highest alpha diversity,followed by CF,TF,and TDF,while POF had the lowest.C stocks varied significantly among forest types,averaging 147.8 Mg C⋅ha^(-1)for AGC and 46.3 Mg C⋅ha^(-1)for SOC.RF and CF stored the most AGC,while TF had the highest SOC.AGC correlated strongly with basal area,species richness,and climatic factors such as precipitation and temperature,while SOC was primarily influenced by soil chemical properties(e.g.,total nitrogen,pH)and precipitation.Notably,no significant relationship was found between SOC and AGC.Our C mapping estimated~3,700 Gg of C in the MMPL,with TDF covering the largest area and contributing most to AGC.However,RF and CF,despite occupying smaller areas,harbored the highest species richness and disproportionately contributed to AGC.These findings highlight the need to treat the different neotropical forest types as distinct ecosystems and prioritize the conservation and expansion of endangered cloud and RF across the Neotropics.展开更多
One of the fundamental questions in community ecology is whether communities are random or formed by deterministic mechanisms. Although many efforts have been made to verify non-randomness in community structure, litt...One of the fundamental questions in community ecology is whether communities are random or formed by deterministic mechanisms. Although many efforts have been made to verify non-randomness in community structure, little is known with regard to co-occurrence patterns in above-ground and below-ground communities. In this paper, we used a null model to test non-randomness in the structure of the above-ground and below-ground mite communities in farmland of the Sanjiang Plain, Northeast China. Then, we used four tests for non-randomness to recognize species pairs that would be demonstrated as significantly aggregated or segregated co-occurrences of the above-ground and below-ground mite communities. The pattern of the above-ground mite commu- nity was significantly non-random in October, suggesting species segregation and hence interspecific competition. Additionally, species co-occurrence patterns did not differ from randomness in the above-ground mite community in August or in below-ground mite com- munities in August and October. Only one significant species pair was detected in the above-ground mite community in August, while no significant species pairs were recognized in the above-ground mite community in October or in the below-ground mite communities in August and October. The results indicate that non-randomness and significant species pairs may not be the general rule in the above-ground and below-ground mite communities in farmland of the Sanjiang Plain at the fine scale.展开更多
Background: Forest productivity has a pivotal role in human well-being. Vegetation quantity, niche complementarity, mass-ratio, and soil resources are alternative/complementary ecological mechanisms driving productivi...Background: Forest productivity has a pivotal role in human well-being. Vegetation quantity, niche complementarity, mass-ratio, and soil resources are alternative/complementary ecological mechanisms driving productivity. One challenge in current forest management depends on identifying and manipulating these mechanisms to enhance productivity. This study assessed the extent to which these mechanisms control aboveground biomass productivity(AGBP) of a Chilean mediterranean-type matorral. AGBP measured as tree aboveground biomass changes over a 7-years period, was estimated for twelve 25 m × 25 m plots across a wide range of matorral compositions and structures. Variables related to canopy structure, species and functional diversity, species and functional dominance, soil texture, soil water and soil nitrogen content were measured as surrogates of the four mechanisms proposed. Linear regression models were used to test the hypotheses. A multimodel inference based on the Akaike’s information criterion was used to select the best models explaining AGBP and for identifying the relative importance of each mechanism.Results: Vegetation quantity(tree density) and mass-ratio(relative biomass of Cryptocarya alba, a conservative species) were the strongest drivers increasing AGBP, while niche complementarity(richness species) and soil resources(sand, %) had a smaller effect either decreasing or increasing AGBP, respectively. This study provides the first assessment of alternative mechanisms driving AGBP in mediterranean forests of Chile. There is strong evidence suggesting that the vegetation quantity and mass-ratio mechanisms are key drivers of AGBP, such as in other tropical and temperate forests. However, in contrast with other studies from mediterranean-type forests, our results show a negative effect of species diversity and a small effect of soil resources on AGBP.Conclusion: AGBP in the Chilean matorral depends mainly on the vegetation quantity and mass-ratio mechanisms.The findings of this study have implications for matorral restoration and management for the production of timber and non-timber products and carbon sequestration.展开更多
The above-ground net primary production(ANPP) and the precipitation-use efficiency(PUE) regulate the carbon and water cycles in grassland ecosystems, but the relationships among the ANPP, PUE and precipitation are sti...The above-ground net primary production(ANPP) and the precipitation-use efficiency(PUE) regulate the carbon and water cycles in grassland ecosystems, but the relationships among the ANPP, PUE and precipitation are still controversial. We selected 717 grassland sites with ANPP and mean annual precipitation(MAP) data from 40 publications to characterize the relationships ANPP–MAP and PUE–MAP across different grassland types. The MAP and ANPP showed large variations across all grassland types, ranging from 69 to 2335 mm and 4.3 to 1706 g m^(-2), respectively. The global maximum PUE ranged from 0.19 to 1.49 g m^(-2) mm^(-1) with a unimodal pattern. Analysis using the sigmoid function explained the ANPP–MAP relationship best at the global scale. The gradient of the ANPP–MAP graph was small for arid and semi-arid sites(MAP <400 mm). This study improves our understanding of the relationship between ANPP and MAP across dry grassland ecosystems. It provides new perspectives on the prediction and modeling of variations in the ANPP for different grassland types along precipitation gradients.展开更多
Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used parti...Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.展开更多
We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominate...We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.展开更多
[Objective] The purpose was to research the distribution characteristics of Tamarix species above-ground biomass of Tarim River＇s middle reaches and to find out best-fit linear-regression model of Tamarix species in ...[Objective] The purpose was to research the distribution characteristics of Tamarix species above-ground biomass of Tarim River＇s middle reaches and to find out best-fit linear-regression model of Tamarix species in this area.[Method] By dint of the most common sampling method PCQ,five samples in the middle reaches of Tarim River were collected.The best-fit linear-regression model of Tamarix species of this area was set up,based on the fieldwork and the model of Evangelista and obtained the distribution rules of Tamarix species of Tarim River＇s middle reaches.[Result] The result indicated that this model fitted for the estimation of aboveground biomass of the study area.According to the distribution rules of aboveground biomass,it was clear that underground water was the major element which decided the distribution of aboveground biomass.[Conclusion] The study provided theoretical basis for the calculation of biomass of Tamarix.展开更多
The quantification of carbon storage in vegetation biomass is a crucial factor in the estimation and mitigation of CO2 emissions.Globally,arid and semi-arid regions are considered an important carbon sink.However,they...The quantification of carbon storage in vegetation biomass is a crucial factor in the estimation and mitigation of CO2 emissions.Globally,arid and semi-arid regions are considered an important carbon sink.However,they have received limited attention and,therefore,it should be a priority to develop tools to quantify biomass at the local and regional scales.Individual plant variables,such as stem diameter and crown area,were reported to be good predictors of individual plant weight.Stand-level variables,such as plant cover and mean height,are also easy-to-measure estimators of above-ground biomass(AGB)in dry regions.In this study,we estimated the AGB in semi-arid woody vegetation in Northeast Patagonia,Argentina.We evaluated whether the AGB at the stand level can be estimated based on plant cover and to what extent the estimation accuracy can be improved by the inclusion of other field-measured structure variables.We also evaluated whether remote sensing technologies can be used to reliably estimate and map the regional mean biomass.For this purpose,we analyzed the relationships between field-measured woody vegetation structure variables and AGB as well as LANDSAT TM-derived variables.We obtained a model-based ratio estimate of regional mean AGB and its standard error.Total plant cover allowed us to obtain a reliable estimation of local AGB,and no better fit was attained by the inclusion of other structure variables.The stand-level plant cover ranged between 18.7%and 95.2%and AGB between about 2.0 and 70.8 Mg/hm^(2).AGB based on total plant cover was well estimated from LANDSAT TM bands 2 and 3,which facilitated a model-based ratio estimate of the regional mean AGB(approximately 12.0 Mg/hm^(2))and its sampling error(about 30.0%).The mean AGB of woody vegetation can greatly contribute to carbon storage in semi-arid lands.Thus,plant cover estimation by remote sensing images could be used to obtain regional estimates and map biomass,as well as to assess and monitor the impact of land-use change on the carbon balance,for arid and semi-arid regions.展开更多
Allometric models are necessary for estimating biomass in terrestrial ecosystems. Generalized allometric relationship exists for many tropical trees, but species- and region-specific models are often lacking. We devel...Allometric models are necessary for estimating biomass in terrestrial ecosystems. Generalized allometric relationship exists for many tropical trees, but species- and region-specific models are often lacking. We developed species-specific allometric models to predict aboveground biomass for 11 native tree species of the Sudanian savanna- woodlands. Diameters at the base and at breast height, with species means ranging respectively from 11 to 28 cm and 9 to 19 cm, and the height of the trees were used as predictor variables. Sampled trees spanned a wide range of sizes including the largest sizes these species can reach. As a response variable, the biomass of the trees was obtained through destructive sampling of 4 754 trees during wood harvesting. We used a stepwise multiple regression analysis with backward elimination procedure to develop models separately predicting, total biomass of the trees, stem biomass, and biomass of branches and twigs. All species- specific regression models relating biomass with measured tree dimen- sions were highly significant （p 〈 0.001）. The biomass of branches and twigs was less predictable compared to stem biomass and total biomass, although their models required fewer predictors and predictor interac- tions. The best-fit equations for total above-ground biomass and stem biomass bad R2 〉 0.70, except for the Acacia species; for branches including twig biomass, R2-values varied from 0.749 for Anogeissus leiocarpa to 0.183 for Acacia macrostachya. The use of these equations in estimating available biomass will avoid destructive sampling, and aid in planning for sustainable use of these species.展开更多
Background：Information on above-ground biomass（AGB） is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and internati...Background：Information on above-ground biomass（AGB） is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels.In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels.There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements.Model-based methods provide an efficient framework to estimate AGB.Methods：Using National Forest Inventory（NFI） data for a^1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery.We assessed different combinations of these datasets using three models,a semiparametric generalized additive model（GAM） and two nonlinear models（sigmoidal and exponential）,employing a genetic algorithm for variable selection that minimized root mean square prediction error（RMSPE）,calculated through cross-validation.We compared model fit statistics to a null model as a baseline estimation method.Using bootstrap resampling methods,we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.Results：Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables.The sigmoidal model and the GAM performed similarly;for both models the RMSPE was -36.8 tonnes per hectare（i.e.,57% of the mean）.However,the sigmoidal model was approximately 30% more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model.After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes（＋/- 477,730）,with a confidence interval 20 times more precise than a simple designbased estimate.Conclusions：Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.展开更多
The purpose of this study was to quantify the changes in tree diversity and above-ground biomass associated with six land-use types in Kodagu district of India's Western Ghats. We collected data on species richnes...The purpose of this study was to quantify the changes in tree diversity and above-ground biomass associated with six land-use types in Kodagu district of India's Western Ghats. We collected data on species richness,composition and above-ground biomass(AGB) of trees,shrubs and herbs from 96 sample plots of 0.1 ha. Totals of83 species from 26 families were recorded across the landuses. Tree species richness, diversity and composition were significantly higher in evergreen forest(EGF) than in other land-uses. Similarly, stem density and basal area were greater in EGF compared to other land-uses. Detrended correspondence analysis(DCA) yielded three distinct groups along the land-use intensities and rainfall gradient on the first and second axes, respectively. The first DCA axis accounted for 45% and second axis for 35% of the total variation in species composition. Together the first two axes accounted for over 2/3 of the variation in species composition across land-use types. Across the land-uses,AGB ranged from 58.6 Mg ha-1 in rubber plantation to327.3 Mg ha-1 in evergreen forest. Our results showed that species diversity and AGB were negatively impacted bythe land-use changes. We found that coffee agroforests resembled natural forest and mixed species plantation in terms of tree diversity and biomass production, suggesting that traditional coffee farms can help to protect tree species, sustain smallholder production and offer opportunities for conservation of biodiversity and climate change mitigation.展开更多
The conversion of forests into agricultural lands is a major cause of deforestation,particularly in the mountain ecosystems of northern Thailand.It results in a rapid loss of biological diversity of both flora and fau...The conversion of forests into agricultural lands is a major cause of deforestation,particularly in the mountain ecosystems of northern Thailand.It results in a rapid loss of biological diversity of both flora and fauna.In addition,the above-ground biomass(AGB),which can be a major source of carbon storage,is also decreased.This study aimed to predict the AGB in Doi Suthep-Pui National Park,Chiang Mai province,based on land-use/land cover(LULC)changes from 2000 to 2030.Landsat-5 TM(2000)and Landsat-8 TM(2015)satellite images were analyzed to predict LULC changes to 2030.Temporary plots(30 m 930 m)were established in each LULC type for AGB analysis;trees with diameters at breast height≥4.5 cm were identified and measured.AGB of all LULC types were analyzed based on specific allometric equations of each type.The results show that area of forest and nonforested areas fluctuated during the study period.Through the first 15 years(2000–2015),5%(2.9 km^2)of forest changed to either agriculture or urban lands,especially mixed deciduous forest and lower montane forest.There was a similar trend in the 2030 prediction,showing the effect of forest fragmentation and the resultant high number of patches.Total AGB tended to decrease over the 30-year period from 12.5 to 10.6 t ha^-1 in the first and second periods,respectively.Deforestation was the main factor influencing the loss of AGB(30.6 t ha^-1)related to LULC changes.Furthermore,habitat loss would be expected to result in decreased biological diversity.Consequently,a management plan should be developed to avoid unsustainable land use changes,which may adversely affect human well-being.展开更多
Roadside trees are effective natural solutions for mitigating climate change. Despite the usefulness of trees to carbon sequestration, there is a dearth of information on the estimation of biomass and carbon stock for...Roadside trees are effective natural solutions for mitigating climate change. Despite the usefulness of trees to carbon sequestration, there is a dearth of information on the estimation of biomass and carbon stock for roadside trees in the study area. This study aimed to estimate the carbon stock and carbon dioxide equivalent of roadside trees. A complete enumeration of trees was carried out in Kétou, Pobè and Sakété within the communes of the Plateau Department, Bénin Republic. Total height and diameter at breast height were measured from trees along the roads while individual wood density value was obtained from wood density database. The allometric method of biomass estimation was adopted for the research. The results showed that the total estimations for above-ground biomass, carbon stock and carbon equivalent from all the enumerated roadside trees were 154.53 mt, 72.63 mt and 266.55 mt, respectively. The results imply that the roadside trees contain a substantial amount of carbon stock that can contribute to climate change mitigation through carbon sequestration.展开更多
The research was aimed to estimate the carbon stocks of above-ground biomass (AGB) in Lesiolouna forest in Republic of Congo. The methodology of Allometric equations was used to measure the carbon stock of Lesio-louna...The research was aimed to estimate the carbon stocks of above-ground biomass (AGB) in Lesiolouna forest in Republic of Congo. The methodology of Allometric equations was used to measure the carbon stock of Lesio-louna tropical rainforest. The research was done with six circular plots each 40 m of diameter, with a distance of 100 m between each plot, depending on the topography of the site of the installation of these plots. The six studied plots are divided in two sites, which are: Iboubikro and Ngambali. Thus, in the six plots, there are three plots in Iboubikro site and three plots in Ngambali site. The results of this study show that the average carbon stock of aboveground biomass (AGB) in six plots was 170.673 t C ha-1. So, the average of carbon stock of aboveground biomass (ABG) in Iboubikro site was 204.693 t C ha-1 and in the Ngambali site was 136.652 t C ha-1. In this forest ecosystem, the high stock of carbon was obtained in Plot 3, which was in Iboubikro site. Plot 3 contains 20 trees and an average DBH of 24.56 cm. However, the lowest carbon stock was obtained in Plot 4, which was in Ngambali site. Also, Plot 4 contains 11 trees and an average DBH of 31.86 cm. The results of this research indicate that, the forests in the study area are an important carbon reservoir, and they can also play a key role in mitigation of climate change.展开更多
Canopy properties (e.g. canopy structure and spectral variables) strongly influence forest above-ground biomass (AGB). However, the importance of these canopy properties in driving AGB in natural forests, especially r...Canopy properties (e.g. canopy structure and spectral variables) strongly influence forest above-ground biomass (AGB). However, the importance of these canopy properties in driving AGB in natural forests, especially relative to other drivers such as plant species diversity and environmental conditions, remains poorly understood. We assessed the relative importance of canopy properties (structure and spectral variables) and plant species diversity (multidimensional diversity metrics and trait composition) in regulating AGB along environmental gradients (topography and soil nutrients) in a temperate forest in Northeast China, using UAV-based LiDAR and hyperspectral data. We found that the explanatory power of environmental conditions, plant species diversity, canopy spectral properties and canopy structure on temperate old-growth forests AGB was 3.8%, 8.0%, 4.1% and 13.3%, respectively. AGB increased with increasing canopy height and structural complexity. Canopy spectral diversity was a better predictor of AGB than traditional diversity metrics in old-growth forests. Canopy spectral composition also played an important role in explaining AGB in the secondary forests. In addition, plant phylogeny, functional diversity and the community-weighted mean of acquisitive traits had significant direct positive effects on AGB. Finally, topography and soil nutrient content indirectly influenced AGB through canopy properties and plant species diversity. Our study highlights the key role of canopy properties in influencing AGB. For future monitoring, regular monitoring with spectral and LiDAR data should be emphasized to provide real-time insights for forest management.展开更多
Background Biomass is the result of long-term production and metabolism in forest ecosystems and is an important indicator of the carbon storage capacity of forests.Although there is increasing empirical evidence supp...Background Biomass is the result of long-term production and metabolism in forest ecosystems and is an important indicator of the carbon storage capacity of forests.Although there is increasing empirical evidence supporting the positive impact of biodiversity on forest productivity and biomass,there is still uncertainty about the relative importance of tree diversity in determining carbon storage compared to other factors such as environmental conditions,functional characteristics and stand structure,especially in complex forest ecosystems.Methods In this study,based on dataset from a 25-ha forest dynamics monitoring plot,we investigated the effects of tree diversity,environmental variables,functional traits and stand structural attributes on above-ground biomass(AGB).Spearman correlation coefficients were used to analyze the correlations between AGB and the variables.The relative importance of these factors in influencing AGB variation was assessed using a random forest model.Structural equation model was used to determine the direct or indirect effects of each factor on AGB.Results The results showed a negative,though not significant,correlation between species richness and AGB.There was a significant positive correlation between leaf dry matter content and leaf tissue density,implying that more leaf photosynthetic products were utilized for dry matter accumulation.The variation in AGB was mainly explained by the maximum diameter at breast height and the coefficients of variation of diameter at breast height,suggesting that large diameter individuals contribute disproportionately to AGB.In addition,AGB was also influenced by topographic factors(i.e.,altitude and slope),while there was no significant correlation with soil variables.Conclusions This study reflects the response of AGB to different influencing factors.Our study emphasizes that stand structure attributes may be more suitable as predictors of forest AGB than species richness.展开更多
The estimation of above-ground biomass(AGB) and carbon storage is very important for arid and semi-arid ecosystems.HJ-1A/B satellite data combined with field measurement data was used for the estimation of shrub AGB a...The estimation of above-ground biomass(AGB) and carbon storage is very important for arid and semi-arid ecosystems.HJ-1A/B satellite data combined with field measurement data was used for the estimation of shrub AGB and carbon storage in the Mu Us desert,China.The correlations of shrub AGB and spectral reflectance of four bands as well as their combined vegetation indexes were respectively analyzed and stepwise regression analysis was employed to establish AGB prediction equation.The prediction equation based on ratio vegetation index(RVI)was proved to be more suitable for shrub AGB estimation in the Mu Us desert than others.Shrub AGB and carbon storage were mapped using the RVI based prediction model in final.The statistics showed the western Mu Us desert has relatively high AGB and carbon storage,and that the gross shrub carton storage in Mu Us desert reaches 16 799 200 t,which has greatly contributed to the carbon fixation in northern China.展开更多
Changes in litter quality(carbon:nitrogen,C:N)and above-ground biomass(AGB)following vegetation restoration significantly impact soil physicochemical properties,yet their effects on soil microbial metabolic limitation...Changes in litter quality(carbon:nitrogen,C:N)and above-ground biomass(AGB)following vegetation restoration significantly impact soil physicochemical properties,yet their effects on soil microbial metabolic limitations remain unclear.We measured litter quality,AGB,soil physicochemical properties,and extracellular enzyme activity(EEA)along a vegetation restoration gradient(7,14,49,70 years,and nearly climax evergreen broadleaved forests)in southern China.We also evaluated soil microbial metabolic limitations by a vector analysis of the EEA.Results revealed the soil microbial metabolisms were co-limited by C and phosphorus(P).The microbial C limitation initially decreased(before 14 years)and then increased,while the microbial P limitation initially increased(before 49 years)and then decreased.Partial least squares path modeling(PLS-PM)showed that the microbial C limitation was mainly attributed to microbial C use efficiency induced by litter quality,suggesting that microorganisms may transfer cellular energy between microbial growth and Cacquiring enzyme production.The microbial P limitation was primarily correlated with AGB-driven change in soil elements and their stoichiometry,highlighting the importance of nutrient stoichiometry and balance in microbial metabolism.The shifts between microbial C and P limitations and the strong connections of plant–soil-microbe processes during vegetation restoration revealed here will provide us with helpful information for optimal management to achieve forest restoration success.展开更多
The wheat above-ground biomass(AGB)is an important index that shows the life activity of vegetation,which is of great significance for wheat growth monitoring and yield prediction.Traditional biomass estimation method...The wheat above-ground biomass(AGB)is an important index that shows the life activity of vegetation,which is of great significance for wheat growth monitoring and yield prediction.Traditional biomass estimation methods specifically include sample surveys and harvesting statistics.Although these methods have high estimation accuracy,they are time-consuming,destructive,and difficult to implement to monitor the biomass at a large scale.The main objective of this study is to optimize the traditional remote sensing methods to estimate the wheat AGBbased on improved convolutional features(CFs).Low-cost unmanned aerial vehicles(UAV)were used as the main data acquisition equipment.This study acquired image data acquired by RGB camera(RGB)and multi-spectral(MS)image data of the wheat population canopy for two wheat varieties and five key growth stages.Then,field measurements were conducted to obtain the actual wheat biomass data for validation.Based on the remote sensing indices(RSIs),structural features(SFs),and CFs,this study proposed a new feature named AUR-50(multi-source combination based on convolutional feature optimization)to estimate the wheat AGB.The results show that AUR-50 could estimate the wheat AGB more accurately than RSIs and SFs,and the average R^(2) exceeded 0.77.In the overwintering period,AUR-50_(MS)(multi-source combination with convolutional feature optimization using multispectral imagery)had the highest estimation accuracy(R^(2) of 0.88).In addition,AUR-50 reduced the effect of the vegetation index saturation on the biomass estimation accuracy by adding CFs,where the highest R^(2) was 0.69 at the flowering stage.The results of this study provide an effective method to evaluate the AGB in wheat with high throughput and a research reference for the phenotypic parameters of other crops.展开更多
A crucial impact of climate change is the disruption of the agricultural sector,posing a threat to food supply for the globally increasing population.In this context,prioritizing food security in each country becomes ...A crucial impact of climate change is the disruption of the agricultural sector,posing a threat to food supply for the globally increasing population.In this context,prioritizing food security in each country becomes an important concern.This study aimed to explore biomass and C-Stock content of Sago forests for handling climate change and resilience.The methodology used comprised various steps including determining the type and the hydraulic conductivity of the soil,assessing biomass and C-Stock by cutting Sago at various growth stages,weighing the wet and dry weight of each fraction,calculating the Top-Root Ratio,and determining the starch yield.The results showed that there were four types of soil namely Hydric,District,and Fluvic Gleisol,as well as Oxic Cambisole.C-Stock was 26.99 tonnes per hectare with a Top-Root Ratio of 636%,implying that above-ground biomass(AGB)was six times more than below-ground biomass(BGB)and the presence of mineral soil.Sago dry starch product ranged from 490.3–571.8 kg per tree and the potential relatively varied due to differences in the structure and composition of forests,as well as habitat and environment.Although logging remained persistent on a very small scale,early signs of disturbances were observed in hydrological conditions and fluctuations in water levels or puddles in the soil profile.This implied that conversion of Sago forests to other uses for the expansion of grain crops on a large scale,would lead to the area experiencing drought.展开更多
基金supported by the UNAN-Managua through the research project No.43201804by The Rufford Foundation through the Small Grants Program(ID-32146-1)by the project 2022-20 granted by The Franklinia Foundation。
文摘Neotropical forests encompass a diverse array of forest types,each with distinct structures and species compositions shaped by varying soil and climate conditions.Despite their global importance in carbon(C)cycling,their diversity is underrepresented in Earth system models due to our limited understanding of the abiotic and biotic factors influencing above-ground carbon(AGC)and soil organic carbon(SOC)stocks.We conducted a forest inventory across five neotropical forest types-cloud forest(CF),transition forest(TF),riparian forest(RF),pineoak forest(POF),and tropical dry forest(TDF)-in Nicaragua's Miraflor-Moropotente Protected Landscape(MMPL)to explore these relationships.Across 40 plots,we identified 4,495 trees belonging to 239 species and 62 families,with Fabaceae being the most species-rich.RF exhibited the highest alpha diversity,followed by CF,TF,and TDF,while POF had the lowest.C stocks varied significantly among forest types,averaging 147.8 Mg C⋅ha^(-1)for AGC and 46.3 Mg C⋅ha^(-1)for SOC.RF and CF stored the most AGC,while TF had the highest SOC.AGC correlated strongly with basal area,species richness,and climatic factors such as precipitation and temperature,while SOC was primarily influenced by soil chemical properties(e.g.,total nitrogen,pH)and precipitation.Notably,no significant relationship was found between SOC and AGC.Our C mapping estimated~3,700 Gg of C in the MMPL,with TDF covering the largest area and contributing most to AGC.However,RF and CF,despite occupying smaller areas,harbored the highest species richness and disproportionately contributed to AGC.These findings highlight the need to treat the different neotropical forest types as distinct ecosystems and prioritize the conservation and expansion of endangered cloud and RF across the Neotropics.
基金Under the auspices of National Natural Science Foundation of China(No.41101049,40601047,41371072,31101617,41171047)China Postdoctoral Science Foundation(No.2012M511361)+2 种基金Excellent Youth Scholars of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences(No.DLSYQ2012004)Fund for Distinguished Young Scholar of Harbin Normal University(No.KGB201204)Scientific Innovation Project for Doctoral Candidate of Harbin Normal University(No.HSDBSCX2012-07)
文摘One of the fundamental questions in community ecology is whether communities are random or formed by deterministic mechanisms. Although many efforts have been made to verify non-randomness in community structure, little is known with regard to co-occurrence patterns in above-ground and below-ground communities. In this paper, we used a null model to test non-randomness in the structure of the above-ground and below-ground mite communities in farmland of the Sanjiang Plain, Northeast China. Then, we used four tests for non-randomness to recognize species pairs that would be demonstrated as significantly aggregated or segregated co-occurrences of the above-ground and below-ground mite communities. The pattern of the above-ground mite commu- nity was significantly non-random in October, suggesting species segregation and hence interspecific competition. Additionally, species co-occurrence patterns did not differ from randomness in the above-ground mite community in August or in below-ground mite com- munities in August and October. Only one significant species pair was detected in the above-ground mite community in August, while no significant species pairs were recognized in the above-ground mite community in October or in the below-ground mite communities in August and October. The results indicate that non-randomness and significant species pairs may not be the general rule in the above-ground and below-ground mite communities in farmland of the Sanjiang Plain at the fine scale.
基金Funding for this research was obtained from CONICy T(Comisión Nacional de Investigación Científica y Tecnológica)for the grant Fondecyt No1150877funding was derived from the CONICy T doctoral grant No 21150802
文摘Background: Forest productivity has a pivotal role in human well-being. Vegetation quantity, niche complementarity, mass-ratio, and soil resources are alternative/complementary ecological mechanisms driving productivity. One challenge in current forest management depends on identifying and manipulating these mechanisms to enhance productivity. This study assessed the extent to which these mechanisms control aboveground biomass productivity(AGBP) of a Chilean mediterranean-type matorral. AGBP measured as tree aboveground biomass changes over a 7-years period, was estimated for twelve 25 m × 25 m plots across a wide range of matorral compositions and structures. Variables related to canopy structure, species and functional diversity, species and functional dominance, soil texture, soil water and soil nitrogen content were measured as surrogates of the four mechanisms proposed. Linear regression models were used to test the hypotheses. A multimodel inference based on the Akaike’s information criterion was used to select the best models explaining AGBP and for identifying the relative importance of each mechanism.Results: Vegetation quantity(tree density) and mass-ratio(relative biomass of Cryptocarya alba, a conservative species) were the strongest drivers increasing AGBP, while niche complementarity(richness species) and soil resources(sand, %) had a smaller effect either decreasing or increasing AGBP, respectively. This study provides the first assessment of alternative mechanisms driving AGBP in mediterranean forests of Chile. There is strong evidence suggesting that the vegetation quantity and mass-ratio mechanisms are key drivers of AGBP, such as in other tropical and temperate forests. However, in contrast with other studies from mediterranean-type forests, our results show a negative effect of species diversity and a small effect of soil resources on AGBP.Conclusion: AGBP in the Chilean matorral depends mainly on the vegetation quantity and mass-ratio mechanisms.The findings of this study have implications for matorral restoration and management for the production of timber and non-timber products and carbon sequestration.
基金jointly funded by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20020401)the Young Foundation of Institute of Mountain Hazard and Environment(SDS-QN-1702)National Natural Science Foundation of China(Grant No.41571205)
文摘The above-ground net primary production(ANPP) and the precipitation-use efficiency(PUE) regulate the carbon and water cycles in grassland ecosystems, but the relationships among the ANPP, PUE and precipitation are still controversial. We selected 717 grassland sites with ANPP and mean annual precipitation(MAP) data from 40 publications to characterize the relationships ANPP–MAP and PUE–MAP across different grassland types. The MAP and ANPP showed large variations across all grassland types, ranging from 69 to 2335 mm and 4.3 to 1706 g m^(-2), respectively. The global maximum PUE ranged from 0.19 to 1.49 g m^(-2) mm^(-1) with a unimodal pattern. Analysis using the sigmoid function explained the ANPP–MAP relationship best at the global scale. The gradient of the ANPP–MAP graph was small for arid and semi-arid sites(MAP <400 mm). This study improves our understanding of the relationship between ANPP and MAP across dry grassland ecosystems. It provides new perspectives on the prediction and modeling of variations in the ANPP for different grassland types along precipitation gradients.
基金supported by the 948 Program of the State Forestry Administration (2009-4-43)the National Natura Science Foundation of China (No.30870420)
文摘Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.
基金made possible by a scholarship from the Australian Government(International Postgraduate Research Scholarship-awarded in 2009)a Southern Cross University Postgraduate Research Scholarship(SCUPRS in 2009)
文摘We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.
基金Supported by Sino-German Cooperation Program(PP[2007]3086)~~
文摘[Objective] The purpose was to research the distribution characteristics of Tamarix species above-ground biomass of Tarim River＇s middle reaches and to find out best-fit linear-regression model of Tamarix species in this area.[Method] By dint of the most common sampling method PCQ,five samples in the middle reaches of Tarim River were collected.The best-fit linear-regression model of Tamarix species of this area was set up,based on the fieldwork and the model of Evangelista and obtained the distribution rules of Tamarix species of Tarim River＇s middle reaches.[Result] The result indicated that this model fitted for the estimation of aboveground biomass of the study area.According to the distribution rules of aboveground biomass,it was clear that underground water was the major element which decided the distribution of aboveground biomass.[Conclusion] The study provided theoretical basis for the calculation of biomass of Tamarix.
基金This research was funded by the National University of Río Negro Research Project(40-C-658)the Research Project National Institute of Agricultural Technology,University Association of Higher Agricultural Education and National Council of Veterinary Deans(Proyect 940175).
文摘The quantification of carbon storage in vegetation biomass is a crucial factor in the estimation and mitigation of CO2 emissions.Globally,arid and semi-arid regions are considered an important carbon sink.However,they have received limited attention and,therefore,it should be a priority to develop tools to quantify biomass at the local and regional scales.Individual plant variables,such as stem diameter and crown area,were reported to be good predictors of individual plant weight.Stand-level variables,such as plant cover and mean height,are also easy-to-measure estimators of above-ground biomass(AGB)in dry regions.In this study,we estimated the AGB in semi-arid woody vegetation in Northeast Patagonia,Argentina.We evaluated whether the AGB at the stand level can be estimated based on plant cover and to what extent the estimation accuracy can be improved by the inclusion of other field-measured structure variables.We also evaluated whether remote sensing technologies can be used to reliably estimate and map the regional mean biomass.For this purpose,we analyzed the relationships between field-measured woody vegetation structure variables and AGB as well as LANDSAT TM-derived variables.We obtained a model-based ratio estimate of regional mean AGB and its standard error.Total plant cover allowed us to obtain a reliable estimation of local AGB,and no better fit was attained by the inclusion of other structure variables.The stand-level plant cover ranged between 18.7%and 95.2%and AGB between about 2.0 and 70.8 Mg/hm^(2).AGB based on total plant cover was well estimated from LANDSAT TM bands 2 and 3,which facilitated a model-based ratio estimate of the regional mean AGB(approximately 12.0 Mg/hm^(2))and its sampling error(about 30.0%).The mean AGB of woody vegetation can greatly contribute to carbon storage in semi-arid lands.Thus,plant cover estimation by remote sensing images could be used to obtain regional estimates and map biomass,as well as to assess and monitor the impact of land-use change on the carbon balance,for arid and semi-arid regions.
基金provided by Swedish International Development Cooperation Agency (Sida)
文摘Allometric models are necessary for estimating biomass in terrestrial ecosystems. Generalized allometric relationship exists for many tropical trees, but species- and region-specific models are often lacking. We developed species-specific allometric models to predict aboveground biomass for 11 native tree species of the Sudanian savanna- woodlands. Diameters at the base and at breast height, with species means ranging respectively from 11 to 28 cm and 9 to 19 cm, and the height of the trees were used as predictor variables. Sampled trees spanned a wide range of sizes including the largest sizes these species can reach. As a response variable, the biomass of the trees was obtained through destructive sampling of 4 754 trees during wood harvesting. We used a stepwise multiple regression analysis with backward elimination procedure to develop models separately predicting, total biomass of the trees, stem biomass, and biomass of branches and twigs. All species- specific regression models relating biomass with measured tree dimen- sions were highly significant （p 〈 0.001）. The biomass of branches and twigs was less predictable compared to stem biomass and total biomass, although their models required fewer predictors and predictor interac- tions. The best-fit equations for total above-ground biomass and stem biomass bad R2 〉 0.70, except for the Acacia species; for branches including twig biomass, R2-values varied from 0.749 for Anogeissus leiocarpa to 0.183 for Acacia macrostachya. The use of these equations in estimating available biomass will avoid destructive sampling, and aid in planning for sustainable use of these species.
基金provided by the United States Agency for International Development under grant number 3FS-G-11-00002 to the Center for International Forestry Research,entitled the Nyimba Forest Projectprovided by The University of British Columbia
文摘Background：Information on above-ground biomass（AGB） is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels.In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels.There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements.Model-based methods provide an efficient framework to estimate AGB.Methods：Using National Forest Inventory（NFI） data for a^1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery.We assessed different combinations of these datasets using three models,a semiparametric generalized additive model（GAM） and two nonlinear models（sigmoidal and exponential）,employing a genetic algorithm for variable selection that minimized root mean square prediction error（RMSPE）,calculated through cross-validation.We compared model fit statistics to a null model as a baseline estimation method.Using bootstrap resampling methods,we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.Results：Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables.The sigmoidal model and the GAM performed similarly;for both models the RMSPE was -36.8 tonnes per hectare（i.e.,57% of the mean）.However,the sigmoidal model was approximately 30% more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model.After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes（＋/- 477,730）,with a confidence interval 20 times more precise than a simple designbased estimate.Conclusions：Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.
基金funded by the Indian Institute of Remote Sensing,Dehradun,India under IIRS-VCP project entitled“National Carbon Pool Assessment”(Project Number:(UAS(B)/DR/GOI/246/2011-12)。
文摘The purpose of this study was to quantify the changes in tree diversity and above-ground biomass associated with six land-use types in Kodagu district of India's Western Ghats. We collected data on species richness,composition and above-ground biomass(AGB) of trees,shrubs and herbs from 96 sample plots of 0.1 ha. Totals of83 species from 26 families were recorded across the landuses. Tree species richness, diversity and composition were significantly higher in evergreen forest(EGF) than in other land-uses. Similarly, stem density and basal area were greater in EGF compared to other land-uses. Detrended correspondence analysis(DCA) yielded three distinct groups along the land-use intensities and rainfall gradient on the first and second axes, respectively. The first DCA axis accounted for 45% and second axis for 35% of the total variation in species composition. Together the first two axes accounted for over 2/3 of the variation in species composition across land-use types. Across the land-uses,AGB ranged from 58.6 Mg ha-1 in rubber plantation to327.3 Mg ha-1 in evergreen forest. Our results showed that species diversity and AGB were negatively impacted bythe land-use changes. We found that coffee agroforests resembled natural forest and mixed species plantation in terms of tree diversity and biomass production, suggesting that traditional coffee farms can help to protect tree species, sustain smallholder production and offer opportunities for conservation of biodiversity and climate change mitigation.
基金supported by the Center for Advanced Studies in Tropical Natural Resources(CASTNaR)Kasetsart University,Bangkok,Thailandthe Kasetsart University Research and Development Institute(KURDI)。
文摘The conversion of forests into agricultural lands is a major cause of deforestation,particularly in the mountain ecosystems of northern Thailand.It results in a rapid loss of biological diversity of both flora and fauna.In addition,the above-ground biomass(AGB),which can be a major source of carbon storage,is also decreased.This study aimed to predict the AGB in Doi Suthep-Pui National Park,Chiang Mai province,based on land-use/land cover(LULC)changes from 2000 to 2030.Landsat-5 TM(2000)and Landsat-8 TM(2015)satellite images were analyzed to predict LULC changes to 2030.Temporary plots(30 m 930 m)were established in each LULC type for AGB analysis;trees with diameters at breast height≥4.5 cm were identified and measured.AGB of all LULC types were analyzed based on specific allometric equations of each type.The results show that area of forest and nonforested areas fluctuated during the study period.Through the first 15 years(2000–2015),5%(2.9 km^2)of forest changed to either agriculture or urban lands,especially mixed deciduous forest and lower montane forest.There was a similar trend in the 2030 prediction,showing the effect of forest fragmentation and the resultant high number of patches.Total AGB tended to decrease over the 30-year period from 12.5 to 10.6 t ha^-1 in the first and second periods,respectively.Deforestation was the main factor influencing the loss of AGB(30.6 t ha^-1)related to LULC changes.Furthermore,habitat loss would be expected to result in decreased biological diversity.Consequently,a management plan should be developed to avoid unsustainable land use changes,which may adversely affect human well-being.
文摘Roadside trees are effective natural solutions for mitigating climate change. Despite the usefulness of trees to carbon sequestration, there is a dearth of information on the estimation of biomass and carbon stock for roadside trees in the study area. This study aimed to estimate the carbon stock and carbon dioxide equivalent of roadside trees. A complete enumeration of trees was carried out in Kétou, Pobè and Sakété within the communes of the Plateau Department, Bénin Republic. Total height and diameter at breast height were measured from trees along the roads while individual wood density value was obtained from wood density database. The allometric method of biomass estimation was adopted for the research. The results showed that the total estimations for above-ground biomass, carbon stock and carbon equivalent from all the enumerated roadside trees were 154.53 mt, 72.63 mt and 266.55 mt, respectively. The results imply that the roadside trees contain a substantial amount of carbon stock that can contribute to climate change mitigation through carbon sequestration.
基金Chinese and Congolese governments by China Scholarship Council(CSC),Beijing Forestry University,Universite Marien Ngouabi,MDDEFE-REDD+/WRI Project and Lesio-louna Project for supporting this research.
文摘The research was aimed to estimate the carbon stocks of above-ground biomass (AGB) in Lesiolouna forest in Republic of Congo. The methodology of Allometric equations was used to measure the carbon stock of Lesio-louna tropical rainforest. The research was done with six circular plots each 40 m of diameter, with a distance of 100 m between each plot, depending on the topography of the site of the installation of these plots. The six studied plots are divided in two sites, which are: Iboubikro and Ngambali. Thus, in the six plots, there are three plots in Iboubikro site and three plots in Ngambali site. The results of this study show that the average carbon stock of aboveground biomass (AGB) in six plots was 170.673 t C ha-1. So, the average of carbon stock of aboveground biomass (ABG) in Iboubikro site was 204.693 t C ha-1 and in the Ngambali site was 136.652 t C ha-1. In this forest ecosystem, the high stock of carbon was obtained in Plot 3, which was in Iboubikro site. Plot 3 contains 20 trees and an average DBH of 24.56 cm. However, the lowest carbon stock was obtained in Plot 4, which was in Ngambali site. Also, Plot 4 contains 11 trees and an average DBH of 31.86 cm. The results of this research indicate that, the forests in the study area are an important carbon reservoir, and they can also play a key role in mitigation of climate change.
基金supported by the National Key Research and Development Program of China (2023YFE0124300, 2022YFF1300501)the CAS (Chinese Academy of Sciences) Project for Young Scientists in Basic Research (YSBR-108)+1 种基金the National Natural Science Foundation of China (32301344)LiaoNing Revitalization Talents Program (XLYC2402003)。
文摘Canopy properties (e.g. canopy structure and spectral variables) strongly influence forest above-ground biomass (AGB). However, the importance of these canopy properties in driving AGB in natural forests, especially relative to other drivers such as plant species diversity and environmental conditions, remains poorly understood. We assessed the relative importance of canopy properties (structure and spectral variables) and plant species diversity (multidimensional diversity metrics and trait composition) in regulating AGB along environmental gradients (topography and soil nutrients) in a temperate forest in Northeast China, using UAV-based LiDAR and hyperspectral data. We found that the explanatory power of environmental conditions, plant species diversity, canopy spectral properties and canopy structure on temperate old-growth forests AGB was 3.8%, 8.0%, 4.1% and 13.3%, respectively. AGB increased with increasing canopy height and structural complexity. Canopy spectral diversity was a better predictor of AGB than traditional diversity metrics in old-growth forests. Canopy spectral composition also played an important role in explaining AGB in the secondary forests. In addition, plant phylogeny, functional diversity and the community-weighted mean of acquisitive traits had significant direct positive effects on AGB. Finally, topography and soil nutrient content indirectly influenced AGB through canopy properties and plant species diversity. Our study highlights the key role of canopy properties in influencing AGB. For future monitoring, regular monitoring with spectral and LiDAR data should be emphasized to provide real-time insights for forest management.
基金financially supported by the National Natural Science Foundation of China(32230067 and 32001171)the Fundamental Research Funds for the Central Universities(5000220151)
文摘Background Biomass is the result of long-term production and metabolism in forest ecosystems and is an important indicator of the carbon storage capacity of forests.Although there is increasing empirical evidence supporting the positive impact of biodiversity on forest productivity and biomass,there is still uncertainty about the relative importance of tree diversity in determining carbon storage compared to other factors such as environmental conditions,functional characteristics and stand structure,especially in complex forest ecosystems.Methods In this study,based on dataset from a 25-ha forest dynamics monitoring plot,we investigated the effects of tree diversity,environmental variables,functional traits and stand structural attributes on above-ground biomass(AGB).Spearman correlation coefficients were used to analyze the correlations between AGB and the variables.The relative importance of these factors in influencing AGB variation was assessed using a random forest model.Structural equation model was used to determine the direct or indirect effects of each factor on AGB.Results The results showed a negative,though not significant,correlation between species richness and AGB.There was a significant positive correlation between leaf dry matter content and leaf tissue density,implying that more leaf photosynthetic products were utilized for dry matter accumulation.The variation in AGB was mainly explained by the maximum diameter at breast height and the coefficients of variation of diameter at breast height,suggesting that large diameter individuals contribute disproportionately to AGB.In addition,AGB was also influenced by topographic factors(i.e.,altitude and slope),while there was no significant correlation with soil variables.Conclusions This study reflects the response of AGB to different influencing factors.Our study emphasizes that stand structure attributes may be more suitable as predictors of forest AGB than species richness.
基金supported by the National Basic Research Program of China("973"Project)(Grant No.2007CB714404)the National Natural Science Foundation of China(Grant No.40871173)the Special Grantfor Prevention and Treatment of Infectious Diseases(2008ZX10004-012)
文摘The estimation of above-ground biomass(AGB) and carbon storage is very important for arid and semi-arid ecosystems.HJ-1A/B satellite data combined with field measurement data was used for the estimation of shrub AGB and carbon storage in the Mu Us desert,China.The correlations of shrub AGB and spectral reflectance of four bands as well as their combined vegetation indexes were respectively analyzed and stepwise regression analysis was employed to establish AGB prediction equation.The prediction equation based on ratio vegetation index(RVI)was proved to be more suitable for shrub AGB estimation in the Mu Us desert than others.Shrub AGB and carbon storage were mapped using the RVI based prediction model in final.The statistics showed the western Mu Us desert has relatively high AGB and carbon storage,and that the gross shrub carton storage in Mu Us desert reaches 16 799 200 t,which has greatly contributed to the carbon fixation in northern China.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(Grant No.41825020)General Program(Grant No.31870461)+3 种基金the“Hundred Talent Program”of South China Botanical Garden at the Chinese Academy of Sciences(Grant No.Y761031001)the“Young Top-notch Talent”in Pearl River talent plan of Guangdong Province(Grant No.2019QN01L763)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515012147)the China Scholarships Council(Grant No.202004910605).
文摘Changes in litter quality(carbon:nitrogen,C:N)and above-ground biomass(AGB)following vegetation restoration significantly impact soil physicochemical properties,yet their effects on soil microbial metabolic limitations remain unclear.We measured litter quality,AGB,soil physicochemical properties,and extracellular enzyme activity(EEA)along a vegetation restoration gradient(7,14,49,70 years,and nearly climax evergreen broadleaved forests)in southern China.We also evaluated soil microbial metabolic limitations by a vector analysis of the EEA.Results revealed the soil microbial metabolisms were co-limited by C and phosphorus(P).The microbial C limitation initially decreased(before 14 years)and then increased,while the microbial P limitation initially increased(before 49 years)and then decreased.Partial least squares path modeling(PLS-PM)showed that the microbial C limitation was mainly attributed to microbial C use efficiency induced by litter quality,suggesting that microorganisms may transfer cellular energy between microbial growth and Cacquiring enzyme production.The microbial P limitation was primarily correlated with AGB-driven change in soil elements and their stoichiometry,highlighting the importance of nutrient stoichiometry and balance in microbial metabolism.The shifts between microbial C and P limitations and the strong connections of plant–soil-microbe processes during vegetation restoration revealed here will provide us with helpful information for optimal management to achieve forest restoration success.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(SJCX23_1973)the National Natural Science Foundation of China(32172110,32071945)+7 种基金the Key Research and Development Program(Modern Agriculture)of Jiangsu Province,China(BE2022342-2,BE2020319)the Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center Open Project,China(ZHKF04)the National Key Research and Development Program of China(2023YFD2300201,2023YFD1202200)the Special Funds for Scientific and Technological Innovation of Jiangsu Province,China(BE2022425)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China(PAPD)the Central Publicinterest Scientific Institution Basal Research Fund,China(JBYW-AII-2023-08)the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences(CAAS-CS-202201)the Special Fund for Independent Innovation of Agriculture Science and Technology in Jiangsu Province,China(CX(22)3112)。
文摘The wheat above-ground biomass(AGB)is an important index that shows the life activity of vegetation,which is of great significance for wheat growth monitoring and yield prediction.Traditional biomass estimation methods specifically include sample surveys and harvesting statistics.Although these methods have high estimation accuracy,they are time-consuming,destructive,and difficult to implement to monitor the biomass at a large scale.The main objective of this study is to optimize the traditional remote sensing methods to estimate the wheat AGBbased on improved convolutional features(CFs).Low-cost unmanned aerial vehicles(UAV)were used as the main data acquisition equipment.This study acquired image data acquired by RGB camera(RGB)and multi-spectral(MS)image data of the wheat population canopy for two wheat varieties and five key growth stages.Then,field measurements were conducted to obtain the actual wheat biomass data for validation.Based on the remote sensing indices(RSIs),structural features(SFs),and CFs,this study proposed a new feature named AUR-50(multi-source combination based on convolutional feature optimization)to estimate the wheat AGB.The results show that AUR-50 could estimate the wheat AGB more accurately than RSIs and SFs,and the average R^(2) exceeded 0.77.In the overwintering period,AUR-50_(MS)(multi-source combination with convolutional feature optimization using multispectral imagery)had the highest estimation accuracy(R^(2) of 0.88).In addition,AUR-50 reduced the effect of the vegetation index saturation on the biomass estimation accuracy by adding CFs,where the highest R^(2) was 0.69 at the flowering stage.The results of this study provide an effective method to evaluate the AGB in wheat with high throughput and a research reference for the phenotypic parameters of other crops.
基金supported by the Dean of Agricultural Faculty,Pattimura University.
文摘A crucial impact of climate change is the disruption of the agricultural sector,posing a threat to food supply for the globally increasing population.In this context,prioritizing food security in each country becomes an important concern.This study aimed to explore biomass and C-Stock content of Sago forests for handling climate change and resilience.The methodology used comprised various steps including determining the type and the hydraulic conductivity of the soil,assessing biomass and C-Stock by cutting Sago at various growth stages,weighing the wet and dry weight of each fraction,calculating the Top-Root Ratio,and determining the starch yield.The results showed that there were four types of soil namely Hydric,District,and Fluvic Gleisol,as well as Oxic Cambisole.C-Stock was 26.99 tonnes per hectare with a Top-Root Ratio of 636%,implying that above-ground biomass(AGB)was six times more than below-ground biomass(BGB)and the presence of mineral soil.Sago dry starch product ranged from 490.3–571.8 kg per tree and the potential relatively varied due to differences in the structure and composition of forests,as well as habitat and environment.Although logging remained persistent on a very small scale,early signs of disturbances were observed in hydrological conditions and fluctuations in water levels or puddles in the soil profile.This implied that conversion of Sago forests to other uses for the expansion of grain crops on a large scale,would lead to the area experiencing drought.
