Abstract The ability to expand the use of predictive Airborne Laser Scanning (ALS)-derived Forest Resource Inventory (FRI) models to broader regional scales is crucial for supporting large scale sustainable forest management. This research examined the transferability of ALS-based FRI attributes between two forest estates located in the eastern and western boreal forest regions of Canada. The sites were structurally diverse due to a strong east-to-west gradient in climate conditions and disturbance regimes. We first examined the ALS–FRI attribute relationships between the sites. Second, we applied Ordinary Least Squares regressions and Random Forest, to predict four FRI attributes. Third, we tested if the inclusion of calibration data from the target location improved the performance of the transferred models. As the sites were located on opposing sides of a bioclimatic gradient, inclusion of target calibration data improved transferred model performance. However, attribute prediction accuracy varied with modeling approach, attribute, and site. The best transferability models fell within a ± 5% relative RMSE of the local predictive models but increased up to 10% in relative bias. These results have implications for forest researchers and managers on both the number, and location, of FRI plots when considering undertaking forest inventories over large disparate areas.

中文翻译：

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加拿大东部和西部北方森林混交林地之间 ALS 衍生的森林资源清单属性的可转移性

摘要 将预测性机载激光扫描 (ALS) 衍生的森林资源清单 (FRI) 模型的使用扩展到更广泛的区域尺度的能力对于支持大规模可持续森林管理至关重要。这项研究检查了位于加拿大东部和西部北方森林地区的两个森林庄园之间基于 ALS 的 FRI 属性的可转移性。由于气候条件和扰动机制具有很强的东西向梯度，这些地点在结构上是多样的。我们首先检查了站点之间的 ALS-FRI 属性关系。其次，我们应用普通最小二乘回归和随机森林来预测四个 FRI 属性。第三，我们测试了包含来自目标位置的校准数据是否提高了传输模型的性能。由于这些站点位于生物气候梯度的相对两侧，因此包含目标校准数据提高了传输模型的性能。然而，属性预测精度因建模方法、属性和站点而异。最好的可转移性模型落在本地预测模型的 ± 5% 相对 RMSE 范围内，但相对偏差增加了 10%。这些结果对森林研究人员和管理人员在考虑对大面积不同区域进行森林清查时对 FRI 地块的数量和位置都有影响。最好的可转移性模型落在本地预测模型的 ± 5% 相对 RMSE 范围内，但相对偏差增加了 10%。这些结果对森林研究人员和管理人员在考虑对大面积不同区域进行森林清查时对 FRI 地块的数量和位置都有影响。最好的可转移性模型落在本地预测模型的 ± 5% 相对 RMSE 范围内，但相对偏差增加了 10%。这些结果对森林研究人员和管理人员在考虑对大面积不同区域进行森林清查时对 FRI 地块的数量和位置都有影响。