Finnish forest structures vary from even-aged planted forests to two- and multi-storied mixed stands. Also, the range of silvicultural systems in use has increased because thinning from above and continuous cover management are gaining popularity. The data currently available for modelling stand dynamics are insufficient to allow the development of unbiased and reliable models for the simulation of all possible transitions between various current and future stand conditions. Therefore, the models should allow temporal and regional calibration along the accumulation of new information on forest development. If the calibration process is automated, the simulators that use these models constitute a self-learning system that adapts to the properties of new data on stand dynamics. The current study first developed such a model set for stand dynamics that is technically suitable for simulating the stand development in all stand structures, silvicultural systems and their transitions. The model set consists of individual-tree models for diameter increment and survival and a stand-level model for ingrowth. The models were based on the permanent sample plots of the 10th and 11th national forest inventories of Finland. Second, a system for calibrating the models based on additional data was presented. This optimization-based system allows different types and degrees of calibration, depending on the intended use of the models and the amount of data available for calibration. The calibration method was demonstrated with two external datasets where a set of sample plots had been measured two times at varying measurement intervals.

中文翻译：

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用于在不断变化的条件下模拟林分动态的自学习生长模拟器

芬兰的森林结构从均匀树龄的人工林到两层和多层混合林分不等。此外，由于从上方疏伐和连续覆盖管理越来越受欢迎，正在使用的造林系统的范围也有所增加。目前可用于模拟林分动力学的数据不足以开发无偏且可靠的模型，以模拟各种当前和未来林分条件之间的所有可能过渡。因此，模型应该允许随着森林发展新信息的积累进行时间和区域校准。如果校准过程是自动化的，那么使用这些模型的模拟器将构成一个自学习系统，该系统可以适应新数据的动态特性。目前的研究首先开发了这样一个林分动力学模型集，该模型集在技术上适用于模拟所有林分结构、造林系统及其过渡的林分发展。该模型集包括用于直径增加和生存的个体树模型和用于向内生长的林分模型。这些模型基于芬兰第 10 次和第 11 次国家森林清单的永久样本地块。其次，提出了一个基于附加数据校准模型的系统。这种基于优化的系统允许不同类型和程度的校准，具体取决于模型的预期用途和可用于校准的数据量。使用两个外部数据集演示了校准方法，其中一组样本图以不同的测量间隔测量了两次。