Prediction of Understorey Vegetation using Remote Sensing in Fennoscandian Forests

Abstract. Understorey vegetation (USV) contributes to forest structure, nutrient cycling, species diversity, habitat functions, and disturbance processes in Fennoscandian forests. It also provides non‑wood forest products such as wild berries. Mapping USV is important for understanding ecosystem functioning and its links to overstorey conditions. Although remote sensing (RS) enables large‑scale forest monitoring, its use for USV mapping remains limited because the layer is often obscured by upper‑canopy foliage. This study assesses the accuracy of USV cover prediction (i.e., the ground area covered by USV) using multiple RS data sources, identifies key predictors, and evaluates how canopy cover influences model performance. Field data were collected in 2024 from 487 plots in the Krycklan catchment. Sentinel‑2 summer and autumn imagery provided spectral reflectance, spectral indices, and grey‑level co‑occurrence matrix (GLCM) texture variables. Additional texture variables were derived from canopy height models (CHMs) generated using airborne laser scanning (ALS; 1–2 points/m²) and Pléiades tri‑stereo image matching (0.5 m; 1.5 points/m²). Beta regression and random forest regression (RFR) models were trained on 70% of plots and validated on 30%. Important predictors included seasonal red‑edge differences, greenness‑based indices, CHM texture variables, and ALS‑based canopy cover. Model performances indicated obstruction due to overstorey canopy cover remains for USV cover prediction. Beta regression with Sentinel‑2 data performed slightly better (RMSE = 21.7 m², variance explained = 5%) than RFR. However, best results occurred in low‑canopy plots (≤40%) using RFR with Sentinel‑2 and Pléiades‑derived CHM texture variables (RMSE = 14.6 m², variance explained = 32%).