Accurate vegetation-community classification is essential for ecological monitoring, habitat assessment, and evidence-based environmental management in heterogeneous landscapes. Existing studies often rely on standalone tree ensembles or generic neural networks, although fine-grained ecological classes frequently exhibit overlapping spectral, topographic, and structural characteristics.
The study maps tree species across Denmark using National Forest Inventory plots and Earth Observation data, comparing manually engineered spectral‑temporal features (STF) from Sentinel‑1 and Sentinel‑2 with embeddings from the foundation models TESSERA and AlphaEarth. Random forest, XGBoost, and MLP classifiers were evaluated, with the STF‑based MLP achieving the highest macro F1 scores for pure and mixed stands. The best model was applied nationally to produce a 10 m resolution tree species map, achieving 79.9% area‑adjusted accuracy and released as an open‑access product.
By Alkiviadis Koukos, Spyros Kondylatos, Thomas Nord-Larsen, Lotte Nyborg, Christian T{\o}ttrup, Kenneth Grogan
arXiv:2606. 32023v1 Announce Type: cross Abstract: Forest attributes are essential for national-scale resource monitoring.
By Emilie Vautier, Cl\'ement Mallet, C\'edric Vega
arXiv:2608. 11638v1 Announce Type: new Abstract: Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable.
By Pann Thinzar Seint, Bryan Atwood, Subas Chhatkuli
The study maps tree species across Denmark using National Forest Inventory plots and Earth Observation data, comparing manually engineered spectral‑temporal features (STF) with embeddings from foundation models TESSERA and AlphaEarth. Random forest, XGBoost, and MLP classifiers were evaluated, with the STF‑based MLP achieving the highest macro F1 scores (0.843 for pure stands, 0.653 for mixed stands). The best model was applied nationally to produce a 10 m resolution tree species map, achieving 79.9% area‑adjusted accuracy and released as an open‑access resource.
The paper introduces DWFF‑Net, a Dynamic Weighted Feature Fusion Network designed to improve multi‑scale segmentation for agricultural habitat recognition. It employs a frozen DINOv3 encoder, a data‑level adaptive dynamic weighting strategy, and a decoder with a dynamic weight calculation network and hybrid loss. Experiments on an agricultural habitat dataset show significant gains in mIoU and mF1 over static fusion and several baseline models, especially for tiny features such as scattered trees.
By Kesong Zheng, Zhi Song, Peizhou Li, Shuyi Yao, Tong Li, Yonglin Shen, Zhenxing Bian