Applied Journal of Physical Science

Forests are crucial for biodiversity and climate regulation, yet they are confronted with rising risks from illegal logging and wildfires, compounded by insufficient real-time monitoring. This study tackles this major problem by developing an intelligent forest monitoring and analytics system which blends remote sensing, IoT, and artificial intelligence. The aim of this study is for the development of an intelligent forest surveillance and analytics system. Specific objectives included modelling forest surveillance using machine learning, designing the system, implementing the system and evaluating the system. Utilising multimodal datasets, including Sentinel-2, Landsat-8/9, Moderate Resolution Imaging Spectroradiometer (MODIS) imaging, and meteorological data, deep learning models (U-Net, ResNet-50 and InceptionV3) were deployed for illegal logging identification, while ensemble models (Random Forest and Long Short-Term Memory (LSTM)) predicted wildfire risk. The results showed remarkable accuracy, with ensemble models successfully predicting wildfire danger levels and the U-Net obtaining 96.8% overall accuracy in logging detection. The system gives strategic guidance through interactive dashboards and geographical disturbance maps. According to the study's findings, combining AI with geographic data greatly improves forest monitoring and provides a performance-oriented backed solution for better conservation and policy enforcement in accordance with the objectives of sustainable forest management.