Accurate Monitoring of the Forests

Forest fires have become a significant threat to the stability of ecosystems, biodiversity, and human life. Similarly, illegal tree cutting poses serious challenges to forest management efforts worldwide, leading to environmental degradation, biodiversity loss, and disruption of ecosystem services. It is therefore crucial to prevent the negative effects of such activities by detecting them at an early stage and taking timely action. This project focuses on the implementation and utilization of advanced remote sensing methods for the monitoring and management of forests. It also aims to combat deforestation by applying remote sensing techniques through a satellite network, integrated with Internet of Things (IoT)-based monitoring. The combination of these information sources helps identify and monitor forest fires efficiently, providing the opportunity for timely responses and coordination of rescue operations. Sentinel-2 and Landsat-8 satellite data serve as the primary tools for extensive monitoring, while drones are employed to capture high-definition imagery. Continuous environmental data from IoT ground sensors including temperature, humidity, smoke, and gas detectorsenhances the accuracy of the detection system and supports real-time monitoring. A combination of 3D–2D Convolutional Neural Networks (CNN) is used to classify land types, while a conventional deep neural network predicts fire occurrences and Normalized Difference Vegetation Index (NDVI) values for vegetation health assessment. This integrated systems approach addresses the weaknesses of individual technologies, where satellites are affected by cloud cover and drones have restricted flight durations. Experimental results demonstrate a high degree of precision in early-stage fire detection, land cover classification, and vegetation health prediction. Current studies reveal that the combined use of drone, satellite, and sensor technologies is more effective in all critical areas offering faster results, improved detection reliability, and greater operational efficiency. The research delivers real-time monitoring and predictive analytics, enabling forest conservation teams to proactively prevent disasters. Future work will focus on enhancing system performance, improving scalability, and developing real-time decision-making capabilities for environmental agencies and policymakers.