INTELLIGENT FOREST MONITORING SYSTEM BASED ON DEEP LEARNING AND MULTIMODAL DATA FUSION

This paper proposes a multimodal forest ecosystem monitoring system based on the integration of remote sensing and ground-based sensor data using deep learning methods. The developed architecture ensures high accuracy in detecting fires, pest outbreaks, and illegal logging by combining visual and temporal data. The results demonstrate the system's potential for application in operational environmental monitoring and digital forest resource management.