Application Of ASRL Model To Predict The Forest Biomass Of The Great Khingan Mountain In Inert Mongolia

As an important part of terrestrial ecosystem,forest participates in the global carbon cycle and water cycle,and has a profound impact on regional and even global climate change.Forest biomass is a key parameter for estimating forest carbon reserve and carbon budget,and also a direct indicator for evaluating forest carbon sequestration capacity.Therefore,in recent years,the research on forest biomass inversion has become more and more popular.Especially the environmental issue has received more and more attention,and the protection of ecological environment has become a global consensus.In this paper,the aboveground biomass of forest of Great Khingan mountain in Inner Mongolia is retrieved by applying the metabolic scaling theory and resource limitations,combined with the measured plots data and climatic data.The main content is divided into two parts:regional maximum canopy height prediction and regional biomass prediction.Maximum tree height is an important indicator of forest vegetation in understanding properties of plant communities.In this paper,we estimate regional maximum tree heights across the forest of the Great Khingan Mountain in Inner Mongolia with Allometric Scaling and Resource Limitations(ASRL)model.The model integrates metabolic scaling theory and water-energy balance equation(Penman-Monteith equation)to predict maximum tree height constrained by local resource availability.Monthly climate data,including precipitation,wind speed,vapor pressure,air temperature and solar radiation are inputs of this model.And ground measurements such as tree heights,diameters at breast height(DBH)and crown heights have been used to compute the parameters of the model.In addition,GLAS data was used to verify the results of model prediction.We found that the prediction of regional maximum tree heights are highly correlated with the GLAS tree heights(R~2=0.64,RMSE=2.87m,MPSE=12.45%).All trees are between 10 to 40 meters in height,and trees in north are taller than those in south in the region for research.Furthermore,we analyzed the sensitivity of the input variables and found the model predictions are most sensitive to air temperature and vapor pressure.Applying the quantitive theory of forest structure,plots biomass and DBH data is used to found the statistical relation between the maximum trunk ratio and biomass density.Powell algorithm is used to optimize the parameters of such model.The predicted maximum tree heights are transformed into maximum trunk ratios according the allometric relation between tree height and DBH.Finally,the aboveground biomass density is estimated with the maximum trunk ratios.Compared with the measured biomass data,our result has a high accuracy(R~2=0.5476,RMSE=17.79t/ha,MPSE=23.57%).