Enhancing Near-Real-Time Forest Monitoring: Foundation Models and Harmonized Landsat-Sentinel (HLS) Time Series for Selective Logging Detection

Near-real-time forest monitoring is a critical component for managing climate risk and resource conservation in the Brazilian Legal Amazon (ALB). The DETER program, managed by the National Institute for Space Research (INPE), has played a pivotal role for over two decades by producing warnings of deforestation and forest degradation to support environmental enforcement by agencies such as IBAMA. However, the effectiveness of these warnings is highly dependent on temporal efficiency — the speed at which a disturbance is detected and published after the activity begins.

Recent objective evaluations of DETER’s performance regarding selective logging — a major driver of forest degradation — revealed a significant median delay of approximately 312 days between the start of logging activities and the corresponding warning publication during 2022-2023. This temporal gap highlights the challenge of applying traditional monitoring to complex spatiotemporal datasets, where factors like cloud cover and sensor resolution can hinder early detection.

To address these challenges, this research proposes a novel approach within the framework of AI and Machine Learning in Spatiotemporal Contexts. We leverage Foundation Models and Deep Learning architectures designed to process the complex temporal dynamics of tropical forests using Harmonized Landsat-Sentinel (HLS) time series. A key contribution of using foundation models in this pipeline is their ability to learn robust representations from large-scale data, significantly reducing the requirement for vast volumes of manually annotated samples — a known bottleneck for AI-based remote sensing monitoring systems. By applying these models to HLS data, we aim to improve spatiotemporal predictions and the reliability of the modeling pipeline, facilitating the production of more agile and efficient early warnings.

This work contributes to the development of the next generation of forest monitoring systems, focusing on interpretability and transferability across the Amazonian landscape. By reducing the detection lag of selective logging, this approach seeks to enhance technological sovereignty in environmental monitoring and provide more effective decision-making support for forest preservation.