Stratified, Spatially Balanced Cluster Sampling for Cost‐Efficient Environmental Surveys

Large‐scale environmental surveys relying on intensive fieldwork are expensive, but survey sampling methodology offers several options to improve their cost‐efficiency. For example, sites selected for field assessments can be arranged in clusters to reduce the time spent moving between the sites, and auxiliary data can be utilized to stratify the survey region and sample less important strata less densely. Geographically balanced and well‐spread sampling can yield further improvements since the target variables of environmental surveys tend to be spatially autocorrelated. A combination of these ideas was illustrated and evaluated in the context of a national forest inventory, and alternative methods of spatially balanced sampling were compared. The main findings were that (i) both the local pivotal method and the generalized random‐tessellation stratified design guaranteed a clearly better spatial regularity than systematic sampling when applied to fragmented regions resulting from stratification and (ii) they also ensured better global balance in unstratified sampling. In our case study, where stratification and sample allocation were based on high‐quality auxiliary data, stratified sampling was clearly more efficient than unstratified for the primary survey target parameter. However, our results also illustrate that highly nonproportional sample allocation can be dangerous in a multi‐purpose survey.