Clustered Subsampling of Double Sampling for Stratification and Growth Model Based Updates of Past Forest Inventories

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Double Sampling for Stratification is a sampling design that is widely used for forest and resource inventories worldwide and, particularly, well established for periodic forest inventories of districts in public and private forests in Germany. Spatially clustered subsampling of second-phase units, actually representing a third phase of sampling, can be expected to reduce travelling costs, but will also decrease precision of estimates. Therefore, the proposed project is intended to develop estimators for totals and per hectare values of usual target variables in forest inventories as well as related sampling errors under that new three-phase sampling design. Using real data the trade-off between precision and amount of clustering will be analyzed. A special focus will be on temporary regional or state-wide inventories based on previous double sampling district inventories. In this case additional precision can be gained by updating the previous inventories using growth models. These growth predictions shall be combined with the sample based estimator to form a composite estimator of higher precision.