We present a Reinforcement Learning-based Adaptive Digital Twin (RL-ADT) model designed for forest ecosystems, utilizing advanced IoT data collection and spatiotemporal graph modeling. It focuses on dynamically representing forests for optimized health resource management and sustainability, simulating environmental interactions, and adapting to changing conditions for real-time monitoring and efficient resource usage. The implementation of the model substantially improved the energy and resource efficiency of the digital twin. The construction of spatiotemporal graphs within the model has led to a more accurate and precise representation of the complex interactions within forest ecosystems. This improvement in model fidelity is crucial to understanding and managing the dynamic nature of forests effectively. The adaptability of the RL algorithms is instrumental in managing the dynamic aspects of forests. The RL algorithm has optimized the trade-off between model accuracy and computational overhead, which is vital for the real-time application of the model in forest management. The insights gained from this study have substantial implications for the sustainable management of forest resources. By improving efficiency in resource use, technology aligns closely with sustainability goals and responsible stewardship of natural resources.