A Novel Geographic Graph-Regulated Disentangling Mamba Model with Sparse Tokens for Enhanced Tree Species Classification from MODIS Time Series

Abstract

Tree species classification from Moderate Resolution Imaging Spectroradiometer (MODIS) time series is critical for environmental monitoring, yet challenging due to subtle inter-species signatures, strong spatial-spectral-temporal coupling, and the difficulty of modeling large-scale geographical correlations. This letter presents a Graph-regulated Disentangled Sparse Mamba model (Geo-GDS-Mamba) with three contributions. First, a mini-batch graph-regulated stream models geographic correlations among samples via a Radial-Basis-Function kernel over their physical coordinates, independent of spectral content, and fuses this context through a graph convolutional network without full-batch memory costs. Second, a disentangling Mamba architecture separates the spatial, spectral, and temporal information coupled within each sample into three independent processing streams. Third, sparse token modules adaptively retain the top 30\% most informative tokens per branch via learned attention or similarity scores, mitigating Mamba's correlation-decay limitation while reducing computational cost. Experiments on annual MOD13Q1 data across two Canadian provinces show Geo-GDS-Mamba achieves 95.35\% OA (94.73\% Kappa) in Alberta, outperforming eleven state-of-the-art baselines, and generalizes without fine-tuning to unseen Saskatchewan data at 87.63\% OA, using only 1.74M parameters and 0.18 GFLOPs. Ablation studies confirm each component's contribution, led by a 4.15-point OA gain from the geographic graph stream alone.