AI-Driven Environmental Foresight: Integrating Machine Learning and Remote Sensing for Predictive Ecosystem Management

Abstract

The accelerating pace of ecosystem change—driven by climate variation, land-use conversion, pollution and biodiversity loss—places increasing demands on environmental monitoring and management systems. Artificial intelligence (AI), especially machine learning (ML), and advancements in remote sensing (RS) technologies now offer unprecedented opportunities for predictive ecosystem management and environmental foresight. This review examines how ML algorithms applied to multi-source remote sensing data enable the forecasting of ecosystem dynamics, early detection of environmental stressors and more informed decision-making. We first outline the key roles of ML in environmental prediction, then review how RS platforms (satellites, UAVs, LiDAR) contribute spatial-temporal data streams. Next, we explore integrated frameworks combining AI and RS for real-time ecosystem monitoring and predictive modelling. We highlight the major challenges—data heterogeneity, model interpretability, transferability and ethical considerations—and outline future pathways toward nextgeneration frameworks, including digital twins, federated learning and AI-enabled policy tools. A key message is that while technological tools are maturing rapidly, achieving truly proactive environmental governance demands interdisciplinary integration, transparent modeling and stakeholder-centric design. With strategic deployment, AI-driven environmental foresight can transform how we anticipate, mitigate and manage ecosystem change.