A Spatio-Temporal AI Framework for Ecosystem Monitoring and Climate-Resilient Community Planning

Abstract

Rapid environmental changes driven by climate variability and human activities are placing unprecedented stress on ecosystems and the communities that rely on them. Traditional ecological monitoring systems, while valuable, often struggle to capture the complexity of spatio-temporal processes influencing biodiversity loss, vegetation dynamics, soil erosion, hydrological fluctuations, and hazard exposure. Spatio-temporal machine learning (ML) models offer a transformative analytical approach, integrating satellite imagery, sensor data, historical climate records, and ecological metrics to assess ecosystem health with high spatial precision and temporal continuity. This paper examines how spatio-temporal ML architectures—including Convolutional LSTMs, Spatio-Temporal Graph Neural Networks (ST-GNNs), and hybrid CNN-Transformer models—can be leveraged to monitor ecosystems and strengthen community resilience. We propose a multi-scale Spatio-Temporal Resilience Monitoring Framework (STRMF) that synthesizes environmental data across scales to identify early warning signals of ecosystem stress, land-cover changes, vegetation decline, floodplain shifts, wildfires, and water-resource exploitation. Evaluations using simulated datasets and historical Earth Observation archives demonstrate that spatio-temporal ML models outperform static spatial or purely temporal predictors. Convolutional LSTM networks, for instance, accurately predict vegetation health by capturing spatial neighborhood dependencies alongside weekly temporal dynamics, while ST-GNNs reveal latent connectivity patterns in watersheds and ecological corridors, highlighting upstream disturbances that may threaten downstream communities. Spatio-temporal insights further enhance community resilience by linking ecological indicators with social exposure metrics such as agricultural dependence, water accessibility, settlement patterns, and vulnerability to hazards. The predictive risk maps generated through STRMF support climate-sensitive planning, sustainable resource allocation, and disaster-risk mitigation, guiding interventions such as riverbank reinforcement, selection of drought-resistant crops, early-warning activation, and sustainable land-use zoning. Incorporating Explainable AI (XAI) methods ensures transparency in ecological drivers, enabling policymakers and communities to understand the underlying causes of ecosystem stress and the consequences of inaction. Overall, this research indicates that next-generation climate governance can be informed by spatio-temporal ML models, facilitating data-driven, anticipatory, and ecosystem-focused resilience planning. By integrating ecological science, climate technology, and community-informed decision-making, these models empower societies to proactively respond to environmental changes, safeguarding biodiversity, livelihoods, and long-term sustainability.