title: Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?
abstract: Seventy-two squares of 100 ha were selected by stratified random sampling with probabilities proportional to size (pps) to survey landscape changes in the period 1996-2003. The area of the plots times the urbanization pressure was used as a size measure. The central question of this study is whether the sampling with probabilities proportional to size leads to gain in precision compared to equal probability sampling. On average 1.03 isolated buildings per 100 ha have been built, while 0.90 buildings per 100 ha have been removed, leading to a net change of 0.13 building per 100 ha. The area with unspoiled natural relief has been reduced by 2.3 ha per 100 ha, and the length of linear relicts by 137 m per 100 ha. On average 74 m of linear green elements have been planted per 100 ha, while 106 m have been removed, leading to a net change of -31 m per 100 ha. For the state variables 'unspoiled natural relief', 'linear relicts', 'removed linear green elements', and 'new-removed linear green elements' there is a gain in precision due to the pps-sampling. For the remaining state variables there is no gain or even a loss of precision ('new buildings', 'removed buildings', 'new-removed buildings', 'new linear green elements'). Therefore, if many state variables must be monitored or when interest is not only in the change but also in the current totals, we recommend to keep things simple, and to select plots with equal probability.
authors.affiliation: Alterra, Wageningen University and Research Centre, Netherlands. dick.brus@wur.nl
authors.forename: D J
authors.initials: DJ
authors.lastname: Brus
authors.forename: W
authors.initials: W
authors.lastname: Nieuwenhuizen
authors.forename: A
authors.initials: A
authors.lastname: Koomen
mesh_terms: D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D009426:Netherlands; D012106:Research / Q000706:statistics & numerical data*; D012107:Research Design; D012424:Rural Population