Abstract In-situ field measurements of sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories. Field measurements on new inventory areas can be reduced by utilizing existing stand attribute models from former inventory areas. We constructed a nationwide model for stem volume, and examined seven different calibration scenarios using 22 inventory areas distributed evenly throughout Finland. These scenarios can be divided into three main categories: 1) calibration with additional predictor variables, 2) calibration with 200 geographically nearest sample plots, and 3) calibration with MS-NFI (Multi-source National Forest Inventory of Finland) volume at the target inventory area. Calibration with degree days, precipitation, and proportion of birch resulted in the greatest decrease in error rate of stem volume predictions. The mean of the root mean square errors (RMSE) among the 22 inventory areas decreased from 28.6% to 25.9%, and the standard deviation of RMSEs from 4.3% to 3.9% using three additional predictor variables. Correspondingly, the mean and standard deviation of absolute values of mean differences (|MD|) decreased from 8.3% to 5.6% and from 5.6% to 4.4%, respectively. All calibration scenarios decreased the error rate, especially the high |MDs| observed in the northern part of Finland. Calibration with sample plots from geographically nearest inventory areas was useful when there were sample plots that offered a good representation of the target area. MS-NFI based calibration was also a reasonable option if loggings and other inconsistencies between datasets could be detected and accounted for.

中文翻译：

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基于全国机载激光扫描的茎体积模型的校准

摘要 样地的现场实地测量是基于机载激光扫描 (ALS) 的森林清单的主要成本组成部分。通过利用以前库存区域的现有林分属性模型，可以减少对新库存区域的现场测量。我们构建了一个全国范围的茎体积模型，并使用 22 个均匀分布在芬兰的库存区域检查了七种不同的校准方案。这些场景可以分为三个主要类别：1) 使用额外的预测变量进行校准，2) 使用 200 个地理上最近的样本地块进行校准，以及 3) 使用目标处的 MS-NFI（芬兰多源国家森林清单）体积进行校准库存区域。用度数、降水量校准，和桦木的比例导致茎体积预测的错误率下降最大。使用三个额外的预测变量，22 个库存区域的均方根误差 (RMSE) 的平均值从 28.6% 下降到 25.9%，RMSE 的标准差从 4.3% 下降到 3.9%。相应地，平均差绝对值的平均值和标准差 (|MD|) 分别从 8.3% 下降到 5.6% 和从 5.6% 下降到 4.4%。所有校准场景都降低了错误率，尤其是高 |MDs| 在芬兰北部观察到。当样本地块能够很好地代表目标区域时，使用地理上最近的清单区域的样地块进行校准非常有用。