Research on Remote Sensing Dynamic Early Warning of Forest Fire Danger Based on Computer Language

Sichuan Province has large three-dimensional climate differences and fragmented terrain. It is in urgent need of refined dynamic forest fire warning services. For the automation of remote sensing-based forest fire warning through computer language. Based on remote sensing data and Python, refined dynamic early warning of forest fire hazards with grid pixels as the smallest unit can be realized, and the pertinence of forest fire prevention work can be improved. The degree of fire risk is affected by many factors. The results show that in March and April, when forest fires are prone to occur, the fire danger value fluctuates greatly. Spatially, the high fire danger value areas are concentrated in the central and southern Sichuan Plateau and the climbing area. In the western region, the high-value areas of fire risk expanded significantly in March and April, and the high-value areas of fire risk were more concentrated in April. The fire danger value in the fire area is higher than that in the same period of previous years, and there is also a short period before the disaster. Therefore, the forest fire early warning service can be effectively integrated into the forest and grassland fire prevention work through remote sensing data, which is helpful to form a joint force of disaster prevention and mitigation with efficient linkage among multiple departments. The simplicity and speed of Python is of great help to the processing of satellite data, and it has great potential in the visualization of meteorological satellite data products through computer language, and is worthy of promotion in the industry.