Stereo-imagery-based post-stratification by regression-tree modelling in Swiss National Forest Inventory

Abstract Swiss National Forest Inventory (NFI) produces estimates of characteristics related to the state and change of the forest resources in the whole country as well as in smaller regions and domains. The estimates are based on measurements on permanent field plots placed on a systematic grid over the country. Digital stereo aerial images are an important source of auxiliary information that can be used with post-stratified estimation to increase the precision of the estimates. We examined post-stratification based on stereo image information, available on a much denser grid than the field plots, for the estimation of forest area, total growing-stock volume and growing-stock volume per unit of accessible forest area. We considered 31 post-stratification schemes that consisted of (1) forest/non-forest stratification by four alternative methods based on manual or semi-automated stereo image interpretation, and (2) sub-stratification within the forest stratum by regression-tree models constructed between field-measured growing-stock volume and stereo-image-retrieved vegetation height characteristics. We applied the schemes in two real NFI estimation cycles, and found that the most precise schemes reduced the variance of the whole-country estimators of forest area, total growing-stock volume and growing-stock volume per unit of accessible forest area by 81%, 67% and 34%, respectively, compared to the estimator without post-stratification. Based on this study, we consider regression-tree modelling well-suited for post-stratification, as it produces stratum boundaries as a direct result of modelling, appears robust against deviating values in fitting data, and avoids problems with unrealistic model predictions in application.