发表机构
DHI; University of Copenhagen; eometrics（DHI机构; 哥本哈根大学; eometrics机构）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究对比光谱-时间特征与TESSERA、AlphaEarth嵌入等输入，结合冠层高度，用多种分类器制图，获丹麦首份10米分辨率全国树种图，在少训练数据时TESSERA更优。
AI 中文摘要
我们利用国家森林样地和地球观测（EO）数据对丹麦的树种进行制图，同时评估基础模型在大规模森林特征刻画中的潜力。我们对比了用于树种分类的两种替代输入表示：（i）从多时相Sentinel-1和Sentinel-2观测数据中提取的人工设计的光谱-时间特征（STF）；（ii）由EO基础模型（FM）TESSERA和AlphaEarth生成的嵌入向量。两种表示均结合了冠层高度信息。我们对所有输入表示评估了随机森林、XGBoost和多层感知器（MLP）分类器，并分别对纯林和混林林分进行评估。基于STF的MLP分类器取得了最高的分类性能，其在纯林和混林林分中的宏F1分数分别为0.843和0.653。基于TESSERA嵌入训练的MLP在纯林中表现出有竞争力的性能，其结果与表现最佳的模型相差在1.1个百分点以内。当训练样地数量少于约25%时，TESSERA始终优于基于STF的模型，在训练数据有限的情况下展现出显著优势。多年观测相比单年输入系统地提升了分类准确率，而消融实验揭示了Sentinel-1后向散射、光谱指数和冠层高度数据的互补贡献。随后，表现最佳的模型被应用于全国范围，生成了丹麦10米分辨率的树种图。经面积调整的验证显示，该图的整体准确率为79.9%。生成的图作为开放获取产品发布，是丹麦首份高分辨率全国树种图，为森林监测、生态研究和土地管理应用提供了宝贵资源。
英文摘要
We map tree species across Denmark using National Forest Inventory plots and EO data, while evaluating the potential of foundation models for large-scale forest characterization. We compare two alternative input representations for tree species classification: (i) manually engineered spectral-temporal features (STF) derived from multi-temporal Sentinel-1 and Sentinel-2 observations, and (ii) embeddings generated by the EO FMs TESSERA and AlphaEarth. Both representations are complemented with canopy height information. Random forest, XGBoost, and Multi-Layer Perceptron (MLP) classifiers are evaluated for all input representations, with separate assessments for pure and mixed forest stands. The STF-based MLP achieves the highest classification performance, yielding macro F1 scores of 0.843 and 0.653 for pure and mixed stands, respectively. The MLP trained on TESSERA embeddings delivers competitive performance for pure stands, achieving results within 1.1 percentage points of the best-performing model. TESSERA consistently outperforms STF-based models when fewer than approximately 25% of training plots are available, demonstrating a substantial advantage under limited training data. Multi-year observations systematically improve classification accuracy relative to single-year inputs, while ablation experiments reveal the complementary contributions of Sentinel-1 backscatter, spectral indices, and canopy height data. The best-performing model is subsequently applied at the national scale to generate a 10 m tree species map of Denmark. Area-adjusted validation indicates an overall map accuracy of 79.9%. The resulting map, released as an open-access product, is the first high-resolution national tree species map of Denmark and provides a valuable resource for forest monitoring, ecological research, and land management applications.
CommentsThis preprint presents a national-scale tree species mapping framework for Denmark using Sentinel-1/2 time series, National Forest Inventory data, and EO foundation model embeddings. The resulted national map can be found here: https://zenodo.org/records/22108850
