Research on the Comprehensive Forest Fire Monitoring System in Central Yunnan Based on Satellite Monitoring Hotspot Data and Smoke Collection Devices

Abstract

Central Yunnan, as a major urban agglomeration in Yunnan Province, is characterized by the interplay of complex topographic conditions, highly flammable vegetation, and frequent human activities, making it a high-incidence area for forest fires and a challenging zone for prevention and control. Traditional monitoring methods that rely solely on satellite remote sensing or ground-based observations are constrained by insufficient spatiotemporal resolution and limited coverage, making it difficult to achieve early and accurate forest fire warning. In recent years, multi-source monitoring strategies integrating satellite hotspot data and ground-based sensor networks have emerged as a key pathway to enhance forest fire early warning capabilities. This paper systematically reviews the research progress of the integrated forest fire monitoring system in Central Yunnan, focusing on the spatiotemporal distribution patterns and trend prediction methods derived from satellite hotspot data such as MODIS and VIIRS, revealing the spatiotemporal coupling characteristics of forest fires—high incidence in spring and winter, and clustering around coniferous forest belts and road networks. Meanwhile, it elaborates on a self-developed smoke and dust collection device suitable for the complex plateau environment, which integrates laser scattering sensing, LoRa spread-spectrum communication, and solar-powered long-duration power supply modules, enabling real-time collection and long-distance transmission of multiple parameters including PM2.5, temperature, and humidity. On this basis, a three-level early warning model based on the random forest algorithm and an LSTM trend prediction model are constructed. Through multi-source data fusion and spatiotemporal alignment, the early warning response time is shortened to within one hour, and the fire detection efficiency in pilot areas has increased by 60%. The study points out that future efforts should further optimize data acquisition frequency, improve model generalization under extreme climatic conditions, and expand device deployment density to achieve precise and dynamic control of fire risks in Central Yunnan and even the broader southwestern forest regions.