Background
When we deliver data from our inventories, we want the quality of the data to be high, that the statistical basis for data collection is robust and that the results we present meet the requirements demanded, e.g. in terms of the accuracy of estimates. The challenge of being able to achieve these goals for both common and rarer habitat types within the same framework started our work to develop a new sample design.
The fundamental principles of the common sampling design are described here. How the sampling design has been modified for each inventory is described in their respective sections.
The need for a modern sampling design
One great benefit of environmental monitoring is when it can provide long time series that indicate how a phenomenon has changed over time. However, this poses several challenges, including changes in the phenomena of interest over time, their locations (so we cannot only follow places where they currently exist), and the constantly technological advances. To ensure that our new inventories contribute to long-term time series in the future, we need to ensure that they are adaptable to changing conditions and requirements. This is where our new sampling design comes into play, offering the flexibility needed to address changing needs and conditions.
A general framework that can be adapted according to the rarity of a nature type.
The flexibility of the sampling design is crucial today as our inventories must provide data on both common and relatively uncommon nature types. . In order for us to be able to report results regionally, such as for the continental region, the same system can also be used to apply denser sampling as needed.
The NILS inventories of alpine areas, deciduous forests, grasslands, , and seashores all follow the same basic principles of the sampling design. In a first step, a sample of tracts is selected, and in a second step, sample plots or intersection points are selected for field visits based on remote sensing. In addition to a two-step design, the sampling design incorporates several other statistical methods that, when combined, create a flexible, long-term, and sustainable inventory methodology. These methods have been used in previous in various inventories. The new is that we are integrating them within the same design.
Strength of the Design
Two-step inventory. The utilization of remote sensing as a basis for selecting field plots in a two-step process enhances the cost-effectiveness of the inventory. Plots that do not contain the sought-after phenomena are identified using remote sensing and do not need to be visited in the field. As a result, this allows us to use large and dense sampling to inventory rare nature types. Furthermore, it also allows us to continuously develop and improve our inventory as new remote sensing techniques and methods are developed without negatively impacting our inventory, as it is included as part of our sampling design.
A balanced selection of sample units (tracts) ensures that the sample are representative.
A coordinated selection of sample areas increases the lifespan of the inventories.
A hierarchical design where sparse samples are subsets of denser samples. The hierarchical design provides flexibility and scalability, allowing for easy selection or combination of sample densities based on needs and budget.