AI-Driven Environmental Monitoring: Integrating Remote Sensing, Ecology, and Data Science

Artificial intelligence (AI) is rapidly transforming environmental monitoring by enabling the integration of remote sensing technologies, ecological knowledge, and data science. The growing availability of satellite imagery, sensor networks, and large ecological datasets has created new opportunities for understanding environmental change, but also presents major analytical challenges. AI-based approaches, particularly machine learning and deep learning, provide powerful tools for processing complex, multi-scale data and generating timely insights into land-use dynamics, biodiversity patterns, ecosystem health, and pollution trends. This editorial examines recent advances in AI-driven environmental monitoring and highlights how interdisciplinary integration enhances the detection and prediction of ecological change. Key applications include automated land-cover mapping, species distribution modeling, real-time air and water quality assessment, and climate-related risk forecasting. Despite these advances, important challenges remain, including data quality limitations, model interpretability, computational accessibility, and ethical governance. Addressing these issues requires explainable AI frameworks, hybrid ecological–computational models, and collaborative data-sharing platforms. This paper argues that meaningful integration of AI with ecological principles is essential to move beyond purely data-driven solutions toward scientifically grounded and socially responsible monitoring systems. By fostering interdisciplinary collaboration and transparent methodologies, AI-driven environmental monitoring can play a critical role in supporting evidence-based decision-making and advancing global sustainability goals.