Multi-Temporal Remote Sensing Analysis for Deforestation Detection Using a Siamese CNN-Transformer Hybrid Network

Deforestation detection remains a critical engineering challenge due to inconsistent temporal observations, frequent cloud interference, and natural vegetation variability within satellite imagery. Current monitoring systems often rely on limited temporal snapshots, restricting the ability to recognize subtle canopy changes and fragmented forest disturbances. These constraints reduce reliability, hinder operational readiness, and delay actionable environmental decision-making. To address these limitations, a hybrid deep-learning framework is developed using a Siamese-based spatial encoder combined with a temporal attention mechanism capable of interpreting extended multi-temporal sequences. This integrated design strengthens spatial-temporal feature representation, enabling clear differentiation between seasonal fluctuations and true forest degradation. Multi-scale decoding enhances structural boundary recovery, facilitating accurate detection of abrupt clearing as well as small-area degradation. The proposed system produces consistent outputs across diverse ecological conditions, increases robustness against cloud-related data gaps, and improves interpretability through attention-based feature highlighting. Engineering relevance is emphasized through automated processing pipelines, scalable computation, and efficient sequence modeling suitable for large-area remote-sensing applications. The framework supports reliable forest disturbance mapping required for environmental assessment, conservation planning, and long-term land-use monitoring. The resulting model contributes to advanced sensing solutions aimed at improving ecological data quality, enhancing remote-sensing analytics, and supporting sustainable resource governance across broad geographic regions.