In forest inventory, traditional ground-based resource assessments are often expensive and time-consuming forcing managers to reduce sample sizes to meet budgetary and logistical constraints. Small area estimation (SAE) is a class of statistical estimators that uses a combination of traditional survey data and linearly related auxiliary information to improve estimate precision. These techniques have been shown to improve the precision of stand-level inventory estimates in loblolly pine plantations using lidar height percentiles and thinning status as covariates. In this study, the effects of reduced lidar point-cloud densities and lower digital elevation model (DEM) spatial resolutions were investigated for total planted volume estimates using area-level SAE models. In the managed Piedmont pine plantation conditions evaluated, lower lidar point-cloud densities and DEM spatial resolutions were found to have minimal effects on estimates and precision. The results of this study are promising to those interested in incorporating SAE methods into forest inventory programs.

中文翻译：

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辅助信息分辨率对人工林清单小面积估算的影响

在森林资源清查中，传统的地面资源评估通常是昂贵且费时的，迫使管理人员减少样本量以满足预算和后勤的限制。小面积估算（SAE）是一类统计估算器，它结合了传统调查数据和线性相关辅助信息来提高估算精度。使用激光雷达高度百分位和间伐状态作为协变量，已证明这些技术可提高火炬松人工林林分水平库存估算的精度。在这项研究中，使用面积级SAE模型研究了降低的激光雷达点云密度和较低的数字高程模型（DEM）空间分辨率对总种植量估计的影响。在评估的皮埃蒙特（Piedmont）松树人工种植条件下，较低的激光雷达点云密度和DEM空间分辨率对估计和精度影响最小。这项研究的结果对有兴趣将SAE方法纳入森林清查计划的人们很有希望。