Fine-Scale Forest Fire Monitoring and Early Warning via IoT and Remote Sensing

Abstract

Forest fires pose a significant threat to natural habitats, leading to profound changes in the biological, chemical, and physical properties of forests. Accurate and timely prediction of fire occurrence is essential for proactive forest management and disaster mitigation. However, existing monitoring systems lack the precision and timeliness required for effective early fire detection and risk mitigation. This study proposes a hybrid wildfire monitoring and early alert framework that combines IoT ground sensors with remote sensing data to forecast forest fire risk, enabling proactive early warnings for forest protection and disaster mitigation. By collecting IoT sensors and satellite imagery, the data are refined through preprocessing steps that involve filtering out noise and intelligently estimating missing values, as well as satellite image correction. Followed by multi-modal feature fusion through locality-sensitive hashing and rank Gauss normalisation to create a uniform feature set. From the fused data, a convolutional neural network-based analytical model enhanced with triplet loss is applied to predict fire ignition probability and classify risk levels (high risk/ no risk). The findings indicate that the proposed model achieved an accuracy of 97.20%, a precision of 96.98%, a recall of 97.71%, and an F1-score of 97.34%, significantly outperforming existing architectures. These results validate the framework as a scalable, data-driven early warning solution for fine-scale wildfire prediction and sustainable forest management.