We contrast a new continuous approach (CA) to estimation of plot level above-ground biomass (AGB) in forest inventories with the current approach of deriving the AGB estimate exclusively from the tree-level AGB predicted for each tree in a plot; henceforth called DA (discrete approach). In CA the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed area plot is computed as the integral of the AGB surface taken over the plot. Hence with CA, the portions of biomass in plot-trees that extend across a plot perimeter is ignored while the biomass from trees outside the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped 2 ha area to illustrate, that important differences in plot-level AGB estimates can emerge, and that one should expect CA-based estimates of AGB to be less variable than with the DA, which translates to a higher precision of estimates from field plots: in our case study, for a target precision of estimation of 5%, the required sample size was 27% lower for small plots of 100m 2 when using the CA and 10% lower for larger plots of 1700m 2 . We discuss practical issues to implementing CA in field inventories and discuss the expected potential for applications that model biomass from remote sensing data.

We contrast a new continuous approach (CA) for estimating plot-level above-ground biomass (AGB) in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot; henceforth called DA (discrete approach). With the CA, the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area. Hence with the CA, the portion of the biomass of in-plot-trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge. Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA. If realized, this difference translates to a higher precision from field sampling, or a lower required sample size. In our case study with a target precision of 5 % (i.e. relative standard error of the estimated mean AGB), the CA required a 27.1 % lower sample size for small plots of 100m² and a 10.4 % lower sample size for larger plots of 1700 m², where we examined sampling induced errors only and did not yet consider model errors. We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data. The CA is a variation on a plot design for above-ground forest biomass; as such it can be applied in combination with any forest inventory sampling design.

We contrast a new continuous approach (CA) to estimation of plot level above-ground biomass (AGB) in forest inventories with the current approach of deriving the AGB estimate exclusively from the tree-level AGB predicted for each tree in a plot; henceforth called DA (discrete approach). In CA the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed area plot is computed as the integral of the AGB surface taken over the plot. Hence with CA, the portions of biomass in plot-trees that extend across a plot perimeter is ignored while the biomass from trees outside the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped 2 ha area to illustrate, that important differences in plot-level AGB estimates can emerge, and that one should expect CA-based estimates of AGB to be less variable than with the DA, which translates to a higher precision of estimates from field plots: in our case study, for a target precision of estimation of 5%, the required sample size was 27% lower for small plots of 100m2 when using the CA and 10% lower for larger plots of 1700m2. We discuss practical issues to implementing CA in field inventories and discuss the expected potential for applications that model biomass from remote sensing data.