Efficiency of Data Clustering for Stratification and Sampling in the Two-Phase ALS-Enhanced Forest Stock Inventory

Within the last few decades, ALS-enhanced two-phase forest inventory has emerged as viable alternative to standard inventory designs. As a relatively new and compound method, there still remains significant potential for its optimisation. One key aspect concerns the design of the second-phase sampling. Apart from well-known designs such as random, systematic, or stratified sampling—which often involve some degree of uncertainty regarding their realisations—there are relatively less common, structurally guided sampling designs (SGS), which can facilitate the unambiguous allocation of balanced and well-optimised samples. Unlike traditional stratification, the SGS design does not rely on fixed divisions, which may induce additional errors due to pre-defined and potentially non-representative strata. Instead of geographical (spatial) sample deployment, the SGS uses the multidimensional space of covariates, e.g., ALS metrics, to optimise sample allocation. SGS can be powered by different engines. While some algorithms for SGS, such as the cube method or local pivotal method, have been briefly tested in recent studies, no thorough attention has yet been paid to data clustering algorithms. Therefore, this study compares the performance of several popular data clustering algorithms for structurally guided sampling to train the model for growing stock volume estimation in a two-phase ALS-enhanced forest inventory design. The results showed that hierarchical clustering was competitive with other methods but outperformed them in terms of the highest stability of estimates, even at lower sampling intensity levels. The use of data clustering methods can ensure unambiguous yet more optimal sample distribution, minimising sampling variation or estimation error caused by the randomness of other sampling methods or the inflexibility of pre-defined strata.