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

分层的双重抽样是一种抽样设计，广泛用于世界各地的森林和资源清查，特别是在德国公共和私人林区的定期森林清查中得到了很好的应用。第二阶段单位的空间分组抽样实际上代表了第三阶段的抽样，可望减少差旅费用，但也会降低估计的精度。因此，拟议项目的目的是在新的三阶段抽样设计下，编制森林清单中常见目标变量的总数和每公顷值以及相关抽样误差的估计值。使用真实数据，将分析聚类精度和聚类量之间的权衡。一个特别的重点将是基于以前的双重抽样地区库存的临时区域或全州库存。在这种情况下，可以通过使用增长模型更新以前的库存来获得更高的精度。这些增长预测应与基于样本的估计值相结合，以形成精度更高的综合估计值。

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.