Estimating amounts of change in forest resources over time is a key function of most national forest inventories (NFI). As this information is used broadly for many management and policy purposes, it is imperative that accurate estimations are made from the survey sample. Robust sampling designs are often used to help ensure representation of the population, but often the full sample is unrealized due to hazardous conditions or possibly lack of land access permission. Potentially, bias may be imparted to the sample if the nonresponse is nonrandom with respect to forest characteristics, which becomes more difficult to assess for change estimation methods that require measurements of the same sample plots at two points in time, i.e., remeasurement. To examine potential nonresponse bias in change estimates, two synthetic populations were constructed: 1) a typical NFI population consisting of both forest and nonforest plots, and 2) a population that mimics a large catastrophic disturbance event within a forested population. Comparisons of estimates under various nonresponse scenarios were made using a standard implementation of post-stratified estimation as well as an alternative approach that groups plots having similar response probabilities (response homogeneity). When using the post-stratified estimators, the amount of change was overestimated for the NFI population and was underestimated for the disturbance population, whereas the response homogeneity approach produced nearly unbiased estimates under the assumption of equal response probability within groups. These outcomes suggest that formal strategies may be needed to obtain accurate change estimates in the presence of nonrandom nonresponse.

估算森林资源随时间的变化量是大多数国家森林资源清查 (NFI) 的一项关键功能。由于此信息被广泛用于许多管理和政策目的，因此必须根据调查样本做出准确的估计。稳健的抽样设计通常用于帮助确保人口的代表性，但由于危险条件或可能缺乏土地使用许可，通常无法实现完整样本。如果不响应在森林特征方面是非随机的，则可能会给样本带来偏差，这对于需要在两个时间点对同一样本地块进行测量（即重新测量）的变化估计方法进行评估变得更加困难。为了检查变化估计中潜在的无反应偏差，构建了两个合成人群：1) 由森林和非森林地块组成的典型 NFI 种群，以及 2) 模拟森林种群中大型灾难性扰动事件的种群。使用后分层估计的标准实施以及将具有相似响应概率（响应同质性）的地块分组的替代方法，对各种无响应情景下的估计值进行了比较。使用后分层估计量时，NFI 人口的变化量被高估，干扰人口的变化量被低估，而响应同质性方法在组内响应概率相等的假设下产生几乎无偏的估计。