Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space

By using more sophisticated sampling designs in forest field inventories, it is possible to select more representative field samples. When full cover auxiliary information is available at the planning stage of a forest inventory, an efficient strategy for sampling is formed by making sure that the sample is well spread in the space spanned by the auxiliary variables. We show that by using such a sampling design, we can improve not only design-based estimation, but also estimation based on nearest neighbour techniques. A new technique to select well-spread probability samples, in multidimensional spaces, from larger populations is introduced. As an application, we illustrate how this strategy can be applied to a forest field inventory. We use an artificial dataset corresponding to a full cover forest remote sensing inventory of a 30 000 ha area of Kuortane, western Finland. The target variable (growing stock volume) has been generated for the entire area by a copula technique. The artificial population has been validated by utilizing the Finnish National Forest Inventory.