Using a remote sensing-based, percent tree cover map to enhance forest inventory estimation

•Remotely sensed data were used to increase the precision of inventory estimates.•Simple random sampling, stratified, and model-assisted estimators were compared.•Mean proportion forest was more precisely estimated than mean growing stock volume.•Model-assisted estimation facilitated consistent estimation at different scales.

For most national forest inventories, the variables of primary interest to users are forest area and growing stock volume. The precision of estimates of parameters related to these variables can be increased using remotely sensed auxiliary variables, often in combination with stratified estimators. However, acquisition and processing of large amounts of remotely sensed data can be costly and laborious, and stratified estimation requires construction of strata and satisfaction of within-stratum sample size constraints. An alternative to both challenges is to use an existing remote sensing-based, spatial product with the model-assisted estimators. The latter estimators use continuous auxiliary information directly rather than their aggregation into strata and are not subject to such severe sample size constraints. The objective of the study was to compare estimates of mean proportion forest area and mean growing stock volume per unit area obtained using both stratified and model assisted estimators with a remote sensing-based percent tree canopy cover map as auxiliary information. For a study area in Minnesota, USA, the primary conclusion was that estimates obtained with both sets of estimators were acceptably precise, but that the model-assisted estimators were easier to implement and facilitated aggregation of estimates from smaller sub-areas to estimates for larger areas.