Fluctuations in weather conditions, particularly precipitation and water availability, may strongly affect growth rate patterns and lead to interannual height growth variation. Consequently, height growth models developed using airborne laser scanning (ALS) data collected at short time intervals may over- or underestimate long-term height growth trends and finally result in different growth forecasts. The objective of this study was to develop height growth models for Norway spruce, including the effect of weather conditions. We used ALS-derived top height (TH) estimates and meteorological data from the research area collected for 2007-2012 and 2013-2018 to develop a weather-sensitive height growth model. The top height (TH) growth of Norway spruce was affected by the mean annual precipitation sum (APS) in the studied periods, and a higher APS resulted in faster TH growth. This study demonstrates the high potential of repeated ALS for detecting short-term variation in the tree height increment and the development of weather-sensitive height growth models.

天气条件的波动，尤其是降水和可用水量，可能会强烈影响生长速度模式并导致年际高度增长变化。因此，使用以短时间间隔收集的机载激光扫描 (ALS) 数据开发的身高增长模型可能会高估或低估长期身高增长趋势，最终导致不同的增长预测。本研究的目的是开发挪威云杉的高度生长模型，包括天气条件的影响。我们使用 ALS 得出的最高高度 (TH) 估计值和 2007-2012 年和 2013-2018 年收集的研究区域的气象数据来开发天气敏感的高度增长模型。在研究期间，挪威云杉的顶高（TH）生长受年平均降水量总和（APS）的影响，更高的 APS 导致更快的 TH 增长。这项研究证明了重复 ALS 在检测树木高度增量的短期变化和开发天气敏感的高度增长模型方面的巨大潜力。