Forest biomass and volume estimation using airborne LiDAR in a cool-temperate forest of northern Hokkaido, Japan

Abstract

Trees are recognized as a carbon reservoir, and precise and convenient methods for forest biomass estimation are required for adequate carbon management. Airborne light detection and ranging (LiDAR) is considered to be one of the solutions for large-scale forest biomass evaluation. To clarify the relationship between mean canopy height determined by airborne LiDAR and forest timber volume and biomass of cool-temperate forests in northern Hokkaido, Japan, we conducted LiDAR observations covering the total area of the Teshio Experimental Forest (225km2) of Hokkaido University and compared the results with ground surveys and previous studies. Timber volume and aboveground tree carbon content of the studied forest stands ranged from 101.43 to 480.40m3ha–1 and from 30.78 to 180.54MgC ha–1, respectively. The LiDAR mean canopy height explained the variation among stands well (volume: r2=0.80, RMSE=55.04m3ha–1; aboveground tree carbon content: r2=0.78, RMSE=19.10MgC ha–1) when one simple linear regression equation was used for all types (hardwood, coniferous, and mixed) of forest stands. The determination of a regression equation for each forest type did not improve the prediction power for hardwood (volume: r2=0.84, RMSE=62.66m3ha–1; aboveground tree carbon content: r2=0.76, RMSE=27.05MgC ha–1) or coniferous forests (volume: r2=0.75, RMSE=51.07m3ha–1; aboveground tree carbon content: r2=0.58, RMSE=19.00MgC ha–1). Thus, the combined regression equation that includes three forest types appears to be adequate for practical application to large-scale forest biomass estimation.