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
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
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
Today the carbon content in the atmosphere is predominantly increasing due to greenhouse gas emission and deforestation. Forest plays a key role in absorbing carbon dioxide from atmosphere by process of sequestration ...Today the carbon content in the atmosphere is predominantly increasing due to greenhouse gas emission and deforestation. Forest plays a key role in absorbing carbon dioxide from atmosphere by process of sequestration through photosynthesis and stores in form of wood biomass which contains nearly 70% - 80% of global carbon. Different forms of biomass in the environment include agricultural products, wood, renewable energy and solid waste. Therefore, it is essential to estimate the biomass content in the environment. In olden days, biomass is estimated by forest inventory techniques which consume lot of time and cost. The spatial distribution of biomass cannot be obtained by traditional inventory forest techniques so the application of remote sensing in biomass assessment is introduced to solve the problem. Overall accuracy of classified map indicates that land features of Surat Thani on map show an accuracy of 91.13% with different land features on ground. Both optical (LANDSAT-8) and synthetic aperture radar (ALOS-2) remote sensing data are used for above ground biomass (AGB) assessment. Biomass that stores in branch and stem of tree is called as above ground biomass. Twenty ground sample plots of 30 m × 30 m utilized for biomass calculation from allometric equations. Optical remote sensing calculates the biomass based on the spectral indices of Soil Adjusted Vegetation Index (SAVI) and Ratio Vegetation Index (RVI) by regression analysis (R<sup>2</sup> = 0.813). Synthetic aperture radar (SAR) is an emerging technique that uses high frequency wavelengths for biomass estimation. HV backscattering of ALOS-2 shows good relation (R<sup>2</sup> = 0.74) with field calculated biomass compared to HH (R<sup>2</sup> = 0.43) utilizes for biomass model generation by linear regression analysis. Combination of both optical spectral indices (SAVI, RVI) and HV (ALOS-2) SAR backscattering increases the plantation biomass accuracy to (R<sup>2</sup> = 0.859) compared to optical (R<sup>2</sup> = 0.788) and SAR (R<sup>2</sup> = 0.742).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
Under conditions of a warmer climate,the advance of the alpine treeline into alpine tundra has implications for carbon dynamics in mountain ecosystems.However,the above- and below-ground live biomass allocations among...Under conditions of a warmer climate,the advance of the alpine treeline into alpine tundra has implications for carbon dynamics in mountain ecosystems.However,the above- and below-ground live biomass allocations among different vegetation types within the treeline ecotones are not well investigated.To determine the altitudinal patterns of above-/below-ground carbon allocation,we measured the root biomass and estimated the above-ground biomass(AGB) in a subalpine forest,treeline forest,alpine shrub,and alpine grassland along two elevational transects towards the alpine tundra in southeast Tibet.The AGB strongly declined with increasing elevation,which was associated with a decrease in the leaf area index and a consequent reduction in carbon gain.The fine root biomass(FRB) increased significantly more in the alpine shrub and grassland than in the treeline forest,whereas the coarse root biomass changed little with increasing altitudes,which led to a stable below-ground biomass(BGB) value across altitudes.Warm and infertile soil conditions might explain the large amount of FRB in alpine shrub and grassland.Consequently,the root toshoot biomass ratio increased sharply with altitude,which suggested a remarkable shift of biomass allocation to root systems near the alpine tundra.Our findings demonstrate contrasting changes in AGB and BGB allocations across treeline ecotones,which should be considered when estimating carbon dynamics with shifting treelines.展开更多
Understanding land use land cover (LULC) change drivers at local scale is vital for development of management strategies to tackle further decline of natural resources. In connection to this, a study was conducted in ...Understanding land use land cover (LULC) change drivers at local scale is vital for development of management strategies to tackle further decline of natural resources. In connection to this, a study was conducted in Dire Dawa administration, Ethiopia to investigate the drivers for change in land use land cover and its impact on above ground biomass and regenerations of woody plants. A total of 160 respondents were selected randomly to collect data on drivers of LULC change. A multistage stratified cluster sampling was used for above ground biomass assessment. Nine sample plots of 10 m × 10 m size in each cluster and a total of 36 sample plots in all clusters were randomly established. In all sample plots, woody plants having >5 cm diameter were measured for their diameter at breast height (DBH), and biomass estimated using allometric equation. The study revealed that, cutting of woody plants for fuel wood and making charcoal, population growth, expansion of cultivated land, drought, settlement areas and livestock ranching are the major six important drivers of LULC change. The study also revealed that, the mean above ground biomass of woody plants in Dire Dawa Administration was 4.94 ton/ha, with maximum and minimum above ground biomass of 6.27 ton/ha and 3.90 ton/ha, respectively. The number of regenerants of tree species was low and only 36% of the plots had tree regenerants. Thus, proper woodland management strategies implementation, land use planning, afforestation and reforestation activities are recommended to minimize unprecedented LULC change in the study area.展开更多
Above Ground Biomass is one of the six pools identified in the inventory of forest resources and estimation of greenhouse gas emissions and sinks from the forestry sector. The pool varies by management practices in di...Above Ground Biomass is one of the six pools identified in the inventory of forest resources and estimation of greenhouse gas emissions and sinks from the forestry sector. The pool varies by management practices in different agro-ecological or agro-climatic zones in forests. The quantification of above ground biomass (AGB) hence carbon sequestration in forests has been very difficult due to the immense costs required. This research was done to estimate AGB using ALOS PALSAR L band data (HH, HV polarisation) acquired in 2009 in relation with ground measurements data in Kericho and Aberdares ranges in Kenya. Tree data information was obtained from ground measurement of DBH and tree heights in 100 circular plots of 15 m radius, by use of random sampling technique. ALOS PALSAR image is advantageous for its active microwave sensor using L-band frequency to achieve cloud free imageries, and the ability of long wavelength cross-polarization to estimate AGB accurately for tropical forests. The variations result between Natural and plantation forest for measured and estimated biomass in Kericho HV band regression value was 0.880 and HH band was 0.520. In Aberdare ranges HV regression value of 0.708 and HH band regression value of 0.511 for measured and estimated biomass respectively. The variations can be explained by the influence of different management regimes induced human disturbances, forest stand age, density, species composition, and trees diameter distribution. However, further research is required to investigate how strong these factors affect relationship between AGB and Alos Palsar backscatters.展开更多
Earth observation(EO)data,such as high-resolution satellite imagery or LiDAR,has become one primary source for forests Aboveground Biomass(AGB)mapping and estimation.However,managing and analyzing the large amount of ...Earth observation(EO)data,such as high-resolution satellite imagery or LiDAR,has become one primary source for forests Aboveground Biomass(AGB)mapping and estimation.However,managing and analyzing the large amount of globally or locally available EO data remains a great challenge.The Google Earth Engine(GEE),which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data,has appeared as an inestimable tool to address this challenge.In this paper,we present a scalable cyberinfrastructure for on-the-fly AGB estimation,statistics,and visualization over a large spatial extent.This cyberinfrastructure integrates state-of-the-art cloud computing applications,including GEE,Fusion Tables,and the Google Cloud Platform(GCP),to establish a scalable,highly extendable,and highperformance analysis environment.Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows.In addition,a web portal was developed to integrate the cyberinfrastructure with some visualization tools(e.g.Google Maps,Highcharts)to provide a Graphical User Interfaces(GUI)and online visualization for both general public and geospatial researchers.展开更多
This study aimed to develop a biomass equation for estimating the total above-ground biomass for Colophospermum mopane (mopane) based on the pooled data from three study sites. The mopane woodlands in Botswana represe...This study aimed to develop a biomass equation for estimating the total above-ground biomass for Colophospermum mopane (mopane) based on the pooled data from three study sites. The mopane woodlands in Botswana represent 14.6% of Botswana’s total area. The woodlands directly or indirectly support the livelihood of the majority of the rural population by providing wood and non-wood products. However, there is limited information on the pattern, trends and distribution of woody biomass production and their primary, environmental, and climatic determinants in different parts of Botswana. All the data were collected by destructive sampling from three study sites in Botswana. Stratified random sampling was based on the stem diameter at breast height (1.3 m from the ground or Diameter at Breast Height (DBH)). A total of 30 sample trees at each study site were measured, felled and weighed. The data from the three sites were pooled together, and the study employed regression analysis to examine the nature of relationships between total above-ground biomass (dependent variable) and five independent variables: 1) total tree height;2) crown diameter;3) stem diameters at 0.15 m;1.3 m (DBH) and 3 m from the ground respectively. There were significant relationships between all the independent variables and the dependent variable. However, DBH emerged as the strongest predictor of total tree above-ground biomass for mopane. The equation lnBiomass=-1.163+2.190lnDBH was adopted for use in the indirect estimation of total tree above-ground biomass for mopane in Botswana.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
The Above Ground Biomass(AGB) estimates of vegetation comprise both the bole biomass determined through a volumetric equation and litter biomass collected from the ground.For mature trees,the AGB estimated in phenolog...The Above Ground Biomass(AGB) estimates of vegetation comprise both the bole biomass determined through a volumetric equation and litter biomass collected from the ground.For mature trees,the AGB estimated in phenologically different time periods is directly affected by the litter biomass since the Diameter at Breast Height(DBH) and height(H) of such trees that are used in the estimation of bole biomass would remain unchanged over a reasonable time period.In the present study,we have determined the AGB of Sal trees(Shorea robusta) in two contrasting seasons:the peak green period in October being devoid of lit-ter on the ground and the leaf shedding period in February with abundant amount of litter present on the ground.Estimation of AGB for the month of February included the litter biomass.In contrast,the AGB for October represented only the bole biomass.AGB was estimated for ten different plots selected in the study area.The AGB estimated from ten sampling plots for each time period was re-gressed with the individual tree parameters such as the average DBH and height of trees measured from the corresponding plots.The regression analysis exhibited a significantly stronger relationship between the AGB and DBH for the month of October as compared to February.Furthermore,the correlation between the remotely sensed derived data and AGB was also found to be significantly higher for the month of October than February.This observation indicates that inclusion of the litter biomass in AGB will tend to decrease the re-gression relationship between AGB and DBH and also between the remotely sensed data and AGB.Therefore,these conclusions invite careful consideration while estimating AGB from satellite data in phenologically different time periods.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
Grassland plays an important role in the global carbon cycle and climate regulation. However, there are still large uncertainties in grassland carbon pool and also its role in global carbon cycle due to the lack of me...Grassland plays an important role in the global carbon cycle and climate regulation. However, there are still large uncertainties in grassland carbon pool and also its role in global carbon cycle due to the lack of measured grassland biomass at regional scale or global scale with a unified survey method, particular for below-ground biomass. The present study, based on a total of 44 grassland sampling plots with 220 quadrats across Ningxia, investigated the characteristics of above-ground biomass (AGB), below-ground biomass (BGB), litter biomass (LB), total biomass (TB) and root:shoot ratios (R:S) for six predominantly grassland types, and their relationships with climatic factors. AGB, BGB, LB and TB varied markedly across different grassland types, the median value ranging from 28.2-692.6 g m-2 for AGB, 130.4-2 036.6 g m-: for BGB, 9.2-82.3 g m2 for LB, and 168.0-2 681.3 g m-: for TB. R:S showed less variation with median values from 3.2 to 5.3 (excluding marshy meadow). The different grassland types showed similar patterns of biomass allocation, with more than 70% BGB for all types. There is evidence of strong positive effects associated with mean annual precipitation (MAP) and negative effects associated with mean annual temperature (MAT) on AGB, BGB, and LB, although both factors have the opposite effect on R:S.展开更多
草地生物量是草地生态系统的重要参数,草原冠层植被光谱的复杂性使得长期评估草场生长状况成为一种挑战。目前少有研究对内蒙典型草原原始光谱信息进行深度探索,探讨地物光谱信息对地上生物量估算的影响。本研究于2017年7月至2018年8月...草地生物量是草地生态系统的重要参数,草原冠层植被光谱的复杂性使得长期评估草场生长状况成为一种挑战。目前少有研究对内蒙典型草原原始光谱信息进行深度探索,探讨地物光谱信息对地上生物量估算的影响。本研究于2017年7月至2018年8月使用ASD Field Spec3野外便携式高光谱仪采集内蒙古锡林郭勒毛登牧场的草地冠层高光谱数据,分析草地的反射光谱曲线来表征植被变化的趋势。同时采用光谱预处理方法结合多种高光谱模型选出最优预测模型。结果表明:(1)从对比不同的广义线性拟合模型(Generalize linear model,GLM)的预测精度来看,最佳的高光谱建模方法为,选取SD_(r)/SD_(b)为变量的最佳模型为y=-3.7953x 2+60.065x-78.455(x为SD_(r)/ SD_(b),y是估算的地上生物量鲜重),拟合R^(2)=0.662,预测R^(2)=0.302。(2)高光谱变量与地上生物量干重之间分析中,选择SD_(r)-SD_(y)/ SD_(r)+SD_(y)作为变量的最佳模型为y=7.744 e 3.4349x(x为SD_(r)-SD_(b)/SD_(r)+SD_(b),y是估算的地上生物量干重),拟合R^(2)=0.559;预测R^(2)=0.304。该研究结果对草地生物量高光谱预测建模具有科学价值。展开更多
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.
文摘Today the carbon content in the atmosphere is predominantly increasing due to greenhouse gas emission and deforestation. Forest plays a key role in absorbing carbon dioxide from atmosphere by process of sequestration through photosynthesis and stores in form of wood biomass which contains nearly 70% - 80% of global carbon. Different forms of biomass in the environment include agricultural products, wood, renewable energy and solid waste. Therefore, it is essential to estimate the biomass content in the environment. In olden days, biomass is estimated by forest inventory techniques which consume lot of time and cost. The spatial distribution of biomass cannot be obtained by traditional inventory forest techniques so the application of remote sensing in biomass assessment is introduced to solve the problem. Overall accuracy of classified map indicates that land features of Surat Thani on map show an accuracy of 91.13% with different land features on ground. Both optical (LANDSAT-8) and synthetic aperture radar (ALOS-2) remote sensing data are used for above ground biomass (AGB) assessment. Biomass that stores in branch and stem of tree is called as above ground biomass. Twenty ground sample plots of 30 m × 30 m utilized for biomass calculation from allometric equations. Optical remote sensing calculates the biomass based on the spectral indices of Soil Adjusted Vegetation Index (SAVI) and Ratio Vegetation Index (RVI) by regression analysis (R<sup>2</sup> = 0.813). Synthetic aperture radar (SAR) is an emerging technique that uses high frequency wavelengths for biomass estimation. HV backscattering of ALOS-2 shows good relation (R<sup>2</sup> = 0.74) with field calculated biomass compared to HH (R<sup>2</sup> = 0.43) utilizes for biomass model generation by linear regression analysis. Combination of both optical spectral indices (SAVI, RVI) and HV (ALOS-2) SAR backscattering increases the plantation biomass accuracy to (R<sup>2</sup> = 0.859) compared to optical (R<sup>2</sup> = 0.788) and SAR (R<sup>2</sup> = 0.742).
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supported by the National Natural Science Foundation of China (41561011)the Natural Science Foundation of Jiangxi Province, China (20151BAB213029)
文摘Under conditions of a warmer climate,the advance of the alpine treeline into alpine tundra has implications for carbon dynamics in mountain ecosystems.However,the above- and below-ground live biomass allocations among different vegetation types within the treeline ecotones are not well investigated.To determine the altitudinal patterns of above-/below-ground carbon allocation,we measured the root biomass and estimated the above-ground biomass(AGB) in a subalpine forest,treeline forest,alpine shrub,and alpine grassland along two elevational transects towards the alpine tundra in southeast Tibet.The AGB strongly declined with increasing elevation,which was associated with a decrease in the leaf area index and a consequent reduction in carbon gain.The fine root biomass(FRB) increased significantly more in the alpine shrub and grassland than in the treeline forest,whereas the coarse root biomass changed little with increasing altitudes,which led to a stable below-ground biomass(BGB) value across altitudes.Warm and infertile soil conditions might explain the large amount of FRB in alpine shrub and grassland.Consequently,the root toshoot biomass ratio increased sharply with altitude,which suggested a remarkable shift of biomass allocation to root systems near the alpine tundra.Our findings demonstrate contrasting changes in AGB and BGB allocations across treeline ecotones,which should be considered when estimating carbon dynamics with shifting treelines.
文摘Understanding land use land cover (LULC) change drivers at local scale is vital for development of management strategies to tackle further decline of natural resources. In connection to this, a study was conducted in Dire Dawa administration, Ethiopia to investigate the drivers for change in land use land cover and its impact on above ground biomass and regenerations of woody plants. A total of 160 respondents were selected randomly to collect data on drivers of LULC change. A multistage stratified cluster sampling was used for above ground biomass assessment. Nine sample plots of 10 m × 10 m size in each cluster and a total of 36 sample plots in all clusters were randomly established. In all sample plots, woody plants having >5 cm diameter were measured for their diameter at breast height (DBH), and biomass estimated using allometric equation. The study revealed that, cutting of woody plants for fuel wood and making charcoal, population growth, expansion of cultivated land, drought, settlement areas and livestock ranching are the major six important drivers of LULC change. The study also revealed that, the mean above ground biomass of woody plants in Dire Dawa Administration was 4.94 ton/ha, with maximum and minimum above ground biomass of 6.27 ton/ha and 3.90 ton/ha, respectively. The number of regenerants of tree species was low and only 36% of the plots had tree regenerants. Thus, proper woodland management strategies implementation, land use planning, afforestation and reforestation activities are recommended to minimize unprecedented LULC change in the study area.
文摘Above Ground Biomass is one of the six pools identified in the inventory of forest resources and estimation of greenhouse gas emissions and sinks from the forestry sector. The pool varies by management practices in different agro-ecological or agro-climatic zones in forests. The quantification of above ground biomass (AGB) hence carbon sequestration in forests has been very difficult due to the immense costs required. This research was done to estimate AGB using ALOS PALSAR L band data (HH, HV polarisation) acquired in 2009 in relation with ground measurements data in Kericho and Aberdares ranges in Kenya. Tree data information was obtained from ground measurement of DBH and tree heights in 100 circular plots of 15 m radius, by use of random sampling technique. ALOS PALSAR image is advantageous for its active microwave sensor using L-band frequency to achieve cloud free imageries, and the ability of long wavelength cross-polarization to estimate AGB accurately for tropical forests. The variations result between Natural and plantation forest for measured and estimated biomass in Kericho HV band regression value was 0.880 and HH band was 0.520. In Aberdare ranges HV regression value of 0.708 and HH band regression value of 0.511 for measured and estimated biomass respectively. The variations can be explained by the influence of different management regimes induced human disturbances, forest stand age, density, species composition, and trees diameter distribution. However, further research is required to investigate how strong these factors affect relationship between AGB and Alos Palsar backscatters.
文摘Earth observation(EO)data,such as high-resolution satellite imagery or LiDAR,has become one primary source for forests Aboveground Biomass(AGB)mapping and estimation.However,managing and analyzing the large amount of globally or locally available EO data remains a great challenge.The Google Earth Engine(GEE),which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data,has appeared as an inestimable tool to address this challenge.In this paper,we present a scalable cyberinfrastructure for on-the-fly AGB estimation,statistics,and visualization over a large spatial extent.This cyberinfrastructure integrates state-of-the-art cloud computing applications,including GEE,Fusion Tables,and the Google Cloud Platform(GCP),to establish a scalable,highly extendable,and highperformance analysis environment.Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows.In addition,a web portal was developed to integrate the cyberinfrastructure with some visualization tools(e.g.Google Maps,Highcharts)to provide a Graphical User Interfaces(GUI)and online visualization for both general public and geospatial researchers.
文摘This study aimed to develop a biomass equation for estimating the total above-ground biomass for Colophospermum mopane (mopane) based on the pooled data from three study sites. The mopane woodlands in Botswana represent 14.6% of Botswana’s total area. The woodlands directly or indirectly support the livelihood of the majority of the rural population by providing wood and non-wood products. However, there is limited information on the pattern, trends and distribution of woody biomass production and their primary, environmental, and climatic determinants in different parts of Botswana. All the data were collected by destructive sampling from three study sites in Botswana. Stratified random sampling was based on the stem diameter at breast height (1.3 m from the ground or Diameter at Breast Height (DBH)). A total of 30 sample trees at each study site were measured, felled and weighed. The data from the three sites were pooled together, and the study employed regression analysis to examine the nature of relationships between total above-ground biomass (dependent variable) and five independent variables: 1) total tree height;2) crown diameter;3) stem diameters at 0.15 m;1.3 m (DBH) and 3 m from the ground respectively. There were significant relationships between all the independent variables and the dependent variable. However, DBH emerged as the strongest predictor of total tree above-ground biomass for mopane. The equation lnBiomass=-1.163+2.190lnDBH was adopted for use in the indirect estimation of total tree above-ground biomass for mopane in Botswana.
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.
文摘The Above Ground Biomass(AGB) estimates of vegetation comprise both the bole biomass determined through a volumetric equation and litter biomass collected from the ground.For mature trees,the AGB estimated in phenologically different time periods is directly affected by the litter biomass since the Diameter at Breast Height(DBH) and height(H) of such trees that are used in the estimation of bole biomass would remain unchanged over a reasonable time period.In the present study,we have determined the AGB of Sal trees(Shorea robusta) in two contrasting seasons:the peak green period in October being devoid of lit-ter on the ground and the leaf shedding period in February with abundant amount of litter present on the ground.Estimation of AGB for the month of February included the litter biomass.In contrast,the AGB for October represented only the bole biomass.AGB was estimated for ten different plots selected in the study area.The AGB estimated from ten sampling plots for each time period was re-gressed with the individual tree parameters such as the average DBH and height of trees measured from the corresponding plots.The regression analysis exhibited a significantly stronger relationship between the AGB and DBH for the month of October as compared to February.Furthermore,the correlation between the remotely sensed derived data and AGB was also found to be significantly higher for the month of October than February.This observation indicates that inclusion of the litter biomass in AGB will tend to decrease the re-gression relationship between AGB and DBH and also between the remotely sensed data and AGB.Therefore,these conclusions invite careful consideration while estimating AGB from satellite data in phenologically different time periods.
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supported by the Strategic-Leader Sci-Tech Projects of Chinese Academy of Sciences(XDA05050403)the Important Direction Project of Innovation of Chinese Academy of Sciences(CAS)(KSCX1-YW-12)
文摘Grassland plays an important role in the global carbon cycle and climate regulation. However, there are still large uncertainties in grassland carbon pool and also its role in global carbon cycle due to the lack of measured grassland biomass at regional scale or global scale with a unified survey method, particular for below-ground biomass. The present study, based on a total of 44 grassland sampling plots with 220 quadrats across Ningxia, investigated the characteristics of above-ground biomass (AGB), below-ground biomass (BGB), litter biomass (LB), total biomass (TB) and root:shoot ratios (R:S) for six predominantly grassland types, and their relationships with climatic factors. AGB, BGB, LB and TB varied markedly across different grassland types, the median value ranging from 28.2-692.6 g m-2 for AGB, 130.4-2 036.6 g m-: for BGB, 9.2-82.3 g m2 for LB, and 168.0-2 681.3 g m-: for TB. R:S showed less variation with median values from 3.2 to 5.3 (excluding marshy meadow). The different grassland types showed similar patterns of biomass allocation, with more than 70% BGB for all types. There is evidence of strong positive effects associated with mean annual precipitation (MAP) and negative effects associated with mean annual temperature (MAT) on AGB, BGB, and LB, although both factors have the opposite effect on R:S.
文摘草地生物量是草地生态系统的重要参数,草原冠层植被光谱的复杂性使得长期评估草场生长状况成为一种挑战。目前少有研究对内蒙典型草原原始光谱信息进行深度探索,探讨地物光谱信息对地上生物量估算的影响。本研究于2017年7月至2018年8月使用ASD Field Spec3野外便携式高光谱仪采集内蒙古锡林郭勒毛登牧场的草地冠层高光谱数据,分析草地的反射光谱曲线来表征植被变化的趋势。同时采用光谱预处理方法结合多种高光谱模型选出最优预测模型。结果表明:(1)从对比不同的广义线性拟合模型(Generalize linear model,GLM)的预测精度来看,最佳的高光谱建模方法为,选取SD_(r)/SD_(b)为变量的最佳模型为y=-3.7953x 2+60.065x-78.455(x为SD_(r)/ SD_(b),y是估算的地上生物量鲜重),拟合R^(2)=0.662,预测R^(2)=0.302。(2)高光谱变量与地上生物量干重之间分析中,选择SD_(r)-SD_(y)/ SD_(r)+SD_(y)作为变量的最佳模型为y=7.744 e 3.4349x(x为SD_(r)-SD_(b)/SD_(r)+SD_(b),y是估算的地上生物量干重),拟合R^(2)=0.559;预测R^(2)=0.304。该研究结果对草地生物量高光谱预测建模具有科学价值。