Research on Forest Fire Prognosis based on Multi-Source Data

Abstract

The occurrence of forest fires not only destroys the ecological balance and devours valuable natural resources, but also threatens the safety of human life and property, so the prevention and control of forest fires is of great significance. At this stage, the technology for forest fire monitoring is very mature, but there is a lack of systematic research on the prediction of fire probability for the time being. Therefore, this paper provides a study of forest fire prognosis by comprehensively analyzing multi-source remote sensing data and natural factor data. Firstly, this paper begins with an overview of the types of multi-source remote sensing data and their applications in forest fire research, including optical remote sensing, thermal infrared remote sensing, radar remote sensing, and LiDAR data. These data provide critical information for fire risk area identification, fire behavior simulation, and post-disaster assessment. Secondly, this paper analyzes the influence of natural factors such as vegetation characteristics, topography and soil conditions, and climatic conditions on the occurrence and spread of forest fires. Further, this paper explores the application of multi-source data in fire risk assessment, behavioral analysis and simulation, and post-disaster assessment and recovery planning, and demonstrates the potential of UAV remote sensing data in fire monitoring through a case study. Finally, this paper summarizes the research findings, discusses the advantages and limitations of the forest fire prediction methods based on multi-source data, and proposes suggestions for future research directions. This study expects to provide a scientific basis for forest fire prevention and response and to contribute to the sustainable management of forest resources and ecological environmental protection.