National Forest Inventory serves as the primary source of forest information, however, maintaining these inventories requires labor-intensive on-site campaigns by forestry experts to identify and document tree species. Embeddings from deep pre-trained remote sensing models offer new opportunities to update NFIs more frequently and at larger scales. While training new deep learning models on few data points remains challenging, we show that using pre-computed embeddings can proven effective for distinguishing tree species through seasonal canopy reflectance patternsin combination with Random Forest. This work systematically investigates how deep embeddings improve tree species classification accuracy in the Netherlands with few annotated data. We evaluate this question on three embedding models: Presto, Alpha Earth, and Tessera, using three tree species datasets of varying difficulty. Data-wise, we compare the available embeddings from Alpha Earth and Tessera with dynamically calculated embeddings from a pre-trained Presto model. Our results demonstrate that fine-tuning a publicly available remote sensing time series pre-trained model outperforms the current state-of-the-art in NFI classification in the Netherlands, yielding performance gains of approximately 2-9 percentage points across datasets and evaluation metrics. This indicates that classic hand-defined features are too simple for this task and highlights the potential of using deep embeddings for data-limited applications such as NFI classification. By leveraging openly available satellite data and deep embeddings from pre-trained models, this approach significantly improves classification accuracy compared to traditional methods and can effectively complement existing forest inventory processes.

翻译：国家森林资源清查是森林信息的主要来源，然而维护这些清查需要林业专家进行劳动密集型的实地调查以识别和记录树种。基于深度预训练遥感模型生成的嵌入为更频繁、更大规模地更新森林资源清查提供了新机遇。尽管在少量数据点上训练新的深度学习模型仍具挑战性，但我们证明：通过结合随机森林算法，使用预计算的嵌入能够有效区分基于季节性冠层反射模式的树种。本研究系统探讨了在荷兰地区，如何利用深度嵌入在少量标注数据条件下提升树种分类精度。我们基于三个不同难度的树种数据集，评估了三种嵌入模型（Presto、Alpha Earth和Tessera）在此问题上的表现。数据层面，我们将Alpha Earth和Tessera的现有嵌入与基于预训练Presto模型动态计算的嵌入进行对比。实验结果表明：对公开可用的遥感时间序列预训练模型进行微调，在荷兰森林资源清查分类任务中超越了当前最优方法，在不同数据集和评估指标上实现了约2-9个百分点的性能提升。这表明传统手工定义的特征对此任务过于简单，同时凸显了深度嵌入在森林资源清查分类等数据受限应用中的潜力。通过利用公开可用的卫星数据及预训练模型的深度嵌入，该方法相较于传统方法显著提升了分类精度，并能有效补充现有的森林资源清查流程。