<p>National Forest Inventories (NFIs) are large-scale surveys that typically employ low sampling intensity, sufficient for national-level estimations. However, this low sampling intensity can make it difficult to produce reliable estimates for specific domains of interest due to limited sample sizes. NFIs use models (model-assisted or model-based approaches) for small area estimation to make estimations in the domain of interest with minimal or no sample. However, the reduced sample size can also be challenging for fitting models. Increasing the sampling intensity would resolve these issues. In this paper, we propose solutions to complement an existing NFI sample in order to improve estimation. We compare several sampling designs for intensification. This intensification poses the issue of integrating two dependent and non-overlapping samples with varying sampling intensities: the regular NFI sample and the intensified sample. We provide estimators of totals and ratios, and associated variance estimators for the domain of interest and the entire territory using a conditional approach. Our results show that intensification reduces the variance for an estimation at the level of both the domain of interest and the whole territory, and that the sampling design has a limited impact on the outcome of estimation, at least for the designs (simple random sampling, systematic sampling, Poisson sampling with equal probabilities, spatial systematic sampling) considered in the simulation study.</p>