Abstract: Traditional forest fire monitoring technologies, such as video surveillance, infrared thermal imaging, satellite remote sensing, and LiDAR, have certain limitations under conditions of complex terrain, variable weather, and long-range observation, which hinder high-precision, wide-coverage, and all-weather early fire detection. This study employed a cooled HgCdTe infrared detector, targeting the strong infrared radiative characteristics of CO₂ at the 4.3 μm wavelength. A panoramic scanning optical structure with a spatial coverage of 360° × 80° was constructed, and a dual-axis rotation system driven by stepper motors was implemented to enable spatial matrix data acquisition. The system integrated a high-speed analog-to-digital sampling platform, in which a Field-Programmable Gate Array (FPGA) performed a 144 s wide-area scan of the surveillance field and executed 46.08 million high-speed AD sampling cycles. Spatial signals were converted into panoramic spectral images using matrix transposition and column-reverse algorithms. A convolutional neural network, deployed on the cloud-based remote image analysis platform, was used to identify and quantify CO₂-featured regions, achieving AI-based precise fire detection. Fire localization was achieved through radar ranging combined with spatial positioning algorithms based on the BeiDou Navigation Satellite System. Field experiments were conducted using small-scale test fire sources under various weather conditions. The results showed that the CO₂-based spectral LiDAR system could accurately detect a small fire source (50 cm×80 cm) at a distance of 2.05 km. The system achieved an overall identification accuracy of 97.31%, with localization errors ranging from 25.2 m to 345.7 m. Spectral LiDAR monitoring technology based on CO₂ infrared radiation features offers high detection sensitivity, strong spatial resolution, and a high level of intelligent recognition, providing a novel solution for early forest fire identification. In the future, it can be integrated with satellite remote sensing and video surveillance to establish a space-air-ground integrated, multi-source forest fire monitoring system, thereby providing technical support for emergency response and ecological security in China's forest and grassland fire management.

Key words: forest fire monitoring, spectral lidar, infrared detection, CO? radiation, image recognition, information fusion