Tree Species Mapping in Temperate Forests From Sentinel-2 Imagery Using OAPCNet: An Optical Flow Alignment and Physical Constraint Network

Accurate tree species mapping via satellite imagery is essential for forest resource management at both global and regional scales. Forest phenological processes, captured in Sentinel-2 satellite image time series (SITS), generate distinct spatial and spectral features across different stages. However, previous studies primarily focused on temporal features and sequential relationships, often overlooking pixel-level distortions and spatial shifts at canopy boundaries, which hinder accurate mapping in transition zones between tree species. In this study, we propose the optical flow alignment and physical consistency constraint network (OAPCNet), a novel deep learning approach designed to track pixel-level spatial drift and dynamically correct feature distortions in SITS data. By incorporating a physical consistency loss, OAPCNet reduces alignment errors and physical distortions. We applied OAPCNet to map 16 tree species across three distinct temperate climate zones in the Northern Hemisphere—temperate monsoon, oceanic, and continental—and compared its performance with six state-of-the-art (SOTA) models. Results show that OAPCNet achieved the highest mapping accuracy, with overall accuracy (OA) (kappa) ranging from 89.73% to 91.12% (86.17%–87.93%) and low standard deviation (<0.9%) across different regions. Furthermore, it outperformed SOTA models, improving OA (kappa) by 1.92%–9.29% (2.76%–12.63%). Ablation experiments demonstrate the critical contribution of optical flow alignment with constraints and the temporal feature enhancement–fusion (TFEF) strategy to robust temporal feature representation. This study introduces a robust deep learning approach for global temperate forest species mapping, crucial for biodiversity monitoring and phenology assessment using Sentinel-2 imagery.