A design-based assessment of an expanded set of auxiliary information for forest growth estimation

Abstract

To improve the design-based precision of gross increment estimates from forest inventories, we propose an assessment of an expanded set of auxiliary information grouped from five sources: (i) a vegetation height model, (ii) satellite imagery, (iii) spatial data, (iv) topography, and (v) variables identified by external forest monitoring and research networks. The former two are from optical remote sensing and the latter three are chosen on the basis of interpretable and proven connections with forest growth. We evaluate each source individually and collectively for the Swiss National Forest Inventory using two-phase estimation with the elastic net method. In terms of relative efficiency, all individual groups demonstrated improvement over one-phase estimation by 7% to 29% with the interpretable sources consistently outperforming those based on optical remote sensing. However, the interpretable sources do not provide significant additional gains when combined together, whereas the optical remote sensing consistently and substantially supports other sources of auxiliary data when combined, leading to a 50% to 71% improvement overall. Given the availability of data, such as that from international monitoring programs, expanding the set of auxiliaries to include both interpretable sources and optical remote sensing is a feasible and promising option for national forest inventories.