Exploring Progress in Forest Fire Detection, Prediction, and Behavior: An In-Depth Survey

Abstract

Forest fires present a substantial environmental challenge, constituting a dual menace to human life and ecological well-being. The imperative for forest fire prevention and management underscores the indispensability of robust detection, prediction, and behavior analysis systems. This scholarly paper offers a thorough exploration of diverse methodologies and techniques employed in the realms of forest fire detection, prediction, and behavior analysis. Encompassing both ground-based and aerial surveillance systems, as well as remote sensing technologies, machine learning-based approaches, and social media-driven systems, the survey provides a comprehensive overview of the multifaceted landscape of forest fire monitoring. In addition to delineating the current state of the art, the paper critically examines the challenges and limitations inherent in existing systems. Furthermore, it imparts valuable insights into prospective avenues for research and development within this domain. In essence, this paper underscores the significance of amalgamating data from various sources and employing diverse analytical methods to enhance our comprehension of forest fire behavior. The overarching goal is to foster the formulation of efficacious strategies for mitigating the impact of this environmental menace.