Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data

Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present above- ground biomass (AGB) in Alberta, Canada, by taking advan- tage of a spatially explicit data set derived from a combi- nation of forest inventory data from 1968 plots and space- borne light detection and ranging (lidar) canopy height data. Ten climatic variables, together with elevation, were used for model development and assessment. Four approaches, in- cluding spatial interpolation, non-spatial and spatial regres- sion models, and decision-tree-based modeling with random forests algorithm (a machine-learning technique), were com- pared to find the "best" estimates. We found that the random forests approach provided the best accuracy for biomass es- timates. Non-spatial and spatial regression models gave esti- mates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total AGB stock in Alberta forests was estimated to be 2.26◊ 10 9 Mg (megagram), with an average AGB den- sity of 56.30± 35.94 Mg ha 1 . At the species level, three ma- jor tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.39◊ 10 9 Mg biomass, accounting for nearly 62 % of total estimated AGB. Spatial distribu- tion of biomass varied with natural regions, land cover types, and species. Furthermore, the relative importance of predic- tor variables on determining biomass distribution varied with species. This study showed that the combination of ground- based inventory data, spaceborne lidar data, land cover clas- sification, and climatic and environmental variables was an efficient way to estimate the quantity, distribution and varia- tion of forest biomass carbon stocks across large regions.