Adequate numbers of replicated, dispersed, and random samples are the basis for reliable sampling inference on resources of concern, particularly vegetation cover across large and heterogenous areas such as rangelands. Tools are needed to predict and assess data precision, specifically the sampling effort required to attain acceptable levels of precision, before and after sampling. We describe and evaluate a flexible and scalable process for assessing the sampling effort requirement for a common monitoring context (responses of rangeland vegetation cover to post-fire restoration treatments), using a custom R script called “SampleRange.” In SampleRange, vegetation cover is estimated from available digital-gridded or field data (e.g., using the satellite-derived cover from the Rangeland Assessment Platform). Next, the sampling effort required to estimate cover with 20% relative standard error (RSE) or to saturate sampling effort is determined using simulations across the environmental gradients in areas of interest to estimate the number of needed plots (“SampleRange quota”). Finally, the SampleRange quota are randomly identified for actual sampling. A 2022 full-cycle trial of SampleRange using the best available digital and prior field data for areas treated after a 2017 wildfire in sagebrush-steppe rangelands revealed that differences in the predicted compared with realized RSEs are inevitable. Future efforts to account for uncertainty in remotely sensed−based vegetative products will enhance tool utility.

足够数量的重复、分散和随机样本是对相关资源（特别是牧场等大面积异质区域的植被覆盖）进行可靠采样推断的基础。需要工具来预测和评估数据精度，特别是在采样之前和之后达到可接受的精度水平所需的采样工作。我们使用名为“SampleRange”的自定义 R 脚本描述并评估了一种灵活且可扩展的流程，用于评估常见监测环境（牧场植被覆盖对火灾后恢复处理的响应）的采样工作要求。在SampleRange 中，植被覆盖度是根据可用的数字网格或现场数据估算的（例如，使用来自牧场评估平台的卫星覆盖度）。接下来，通过对感兴趣区域中的环境梯度进行模拟来确定估计覆盖范围为 20% 相对标准误差 (RSE) 或饱和采样工作所需的采样工作量，以估计所需样地的数量（“采样范围配额”）。最后随机确定SampleRange配额进行实际采样。2022 年对 SampleRange 进行的全周期试验使用了 2017 年山艾草原野火后处理区域的最佳可用数字和先前现场数据，结果表明，预测的 RSE 与实际的 RSE 之间的差异是不可避免的。未来解释基于遥感的植物产品的不确定性的努力将增强工具的实用性。