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
In the context of global carbon cycle management, accurate knowledge of carbon content in forests is a relevant issue in contemporary forest ecology. We measured the above-ground and soil carbon pools in the darkconif...In the context of global carbon cycle management, accurate knowledge of carbon content in forests is a relevant issue in contemporary forest ecology. We measured the above-ground and soil carbon pools in the darkconiferous boreal taiga. We compared measured carbon pools to those calculated from the forest inventory records containing volume stock and species composition data. The inventory data heavily underestimated the pools in the study area（Stolby State Nature Reserve, central Krasnoyarsk Territory, Russian Federation）. The carbon pool estimated from the forest inventory data varied from 25（t ha-1）（low-density stands） to 73（t ha-1）（highly stocked stands）. Our estimates ranged from 59（t ha-1）（lowdensity stands） to 147（t ha-1）（highly stocked stands）. Our values included living trees, standing deadwood, living cover, brushwood and litter. We found that the proportion of biomass carbon（living trees）： soil carbon varied from99：1 to 8：2 for fully stocked and low-density forest stands,respectively. This contradicts the common understanding that the biomass in the boreal forests represents only16–20 % of the total carbon pool, with the balance being the soil carbon pool.展开更多
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 Russian Foundation for Basic Research,research grant 14-05-00831
文摘In the context of global carbon cycle management, accurate knowledge of carbon content in forests is a relevant issue in contemporary forest ecology. We measured the above-ground and soil carbon pools in the darkconiferous boreal taiga. We compared measured carbon pools to those calculated from the forest inventory records containing volume stock and species composition data. The inventory data heavily underestimated the pools in the study area（Stolby State Nature Reserve, central Krasnoyarsk Territory, Russian Federation）. The carbon pool estimated from the forest inventory data varied from 25（t ha-1）（low-density stands） to 73（t ha-1）（highly stocked stands）. Our estimates ranged from 59（t ha-1）（lowdensity stands） to 147（t ha-1）（highly stocked stands）. Our values included living trees, standing deadwood, living cover, brushwood and litter. We found that the proportion of biomass carbon（living trees）： soil carbon varied from99：1 to 8：2 for fully stocked and low-density forest stands,respectively. This contradicts the common understanding that the biomass in the boreal forests represents only16–20 % of the total carbon pool, with the balance being the soil carbon pool.
