Performance of Selected Sampling Methods for Growing Stock Volume Estimation in Model-Based Two-Phase Forest Inventory

Selection of an adequate sampling method is perhaps one of the most crucial stages of any survey. Researchers and decision-makers often face difficulties in selecting appropriate sampling techniques and sample sizes in relation to the specific research problem and the desired accuracy of estimates. Although general rules of thumb are relatively well established, the optimization of these elements is often based on rough approximations. Direct comparisons of the accuracy of different sampling methods in large-scale, real-world forest inventories are, in most cases, not practically feasible. Consequently, some studies rely on resampling limited observational datasets, whereas others generate artificial populations from which predefined samples can be drawn to evaluate sampling efficiency. Nevertheless, such populations tend to be simplified representations of the often complex structures of real forests. This study proposes an approach that could serve as a bridge between real and artificial data. Specifically, the efficiency of three popular sampling designs—simple random sampling (SRS), systematic sampling (SYS), and stratified sampling (StrS)—was compared for growing stock volume (GSV) estimation in a two-phase model-based forest inventory. The comparisons were performed using semi-synthetic forest populations, in which only the target variable was generated using a machine-learning approach, whereas all other parameters were derived from real forest inventory and remote sensing (RS) data. Stratification and sampling were performed using hierarchical data clustering and the concept of structurally guided sampling (SGS), which, in addition to sampling in geographical space, enables sampling in the multidimensional space of auxiliary variables. The proposed stratification technique allows for potentially more effective delineation of strata than predefined spatial layers, which should be crucial for the overall inventory performance. The results showed that hierarchical stratification (clustering) combined with SGS in the airborne laser scanning (ALS) data space was competitive with SRS and SYS, and could even outperform them at lower sampling intensity levels. The convergence zone in the accuracy and precision of SGS-GSV estimates could be reached at lower sampling intensities than with the classical sampling methods. These results demonstrate a potential for optimizing sampling in ALS model-based forest inventories and highlight opportunities for reducing sample sizes, without practically compromising the accuracy and precision of GSV estimates at the forest district level. Nonetheless, further validation on real-world datasets is needed, as synthetic populations may not fully capture the heterogeneity of actual forest structures, which would most likely require more intensive sampling. Therefore, the results presented in this study should be regarded as indicative of potential trends rather than as definitive guidelines for conducting remote sensing-supported forest inventories.