This paper presents a novel approach to monitoring land use change by integrating foundation models within a dual-timescale Digital Twin (DT) framework. While existing Earth system DTs primarily focus on atmospheric variables, our architecture explicitly addresses the dynamics of land use transformation. The proposed system utilizes a hierarchical structure of Digital Twin Instances and Aggregators that operate on two temporal scales: a rapid change component for near real-time vegetation monitoring and a gradual change component for long-term land use classification. O ur i mplementation r elies o n cloud-based data pipelines and foundation models for analyzing satellite imagery. It features a cognitive user interface that transforms complex geospatial data into contextually relevant insights. By incorporating pre-trained foundation models and physics-informed neural networks, our framework is designed to improve change detection accuracy while reducing computational requirements. Implementation experiences in Ukrainian and Swiss landscapes demonstrate the framework’s effectiveness across diverse geographic contexts.