Abstract
Pine forests are increasingly threatened by needle diseases, including Brown Spot Needle Blight (BSNB), caused by Lecanosticta acicola. BSNB leads to needle loss, reduced growth, significant tree mortality, and disruptions in global timber production. Due to its severity, L. acicola is designated as a quarantine pathogen in several countries, requiring effective early detection and control of its spread. Remote sensing (RS) technologies provide scalable and efficient solutions for broad-scale disease surveillance. This study systematically reviews RS-based methods for detecting BSNB symptoms, assessing current research trends and potential applications. A comprehensive bibliometric analysis using the Web of Science database indicated that direct RS applications for BSNB remain scarce. However, studies on other needle diseases demonstrated the effectiveness of multisource RS techniques for symptom detection, spatial mapping, and severity assessment. Advancements in machine learning (ML) and deep learning (DL) have further improved RS capabilities for automated disease classification and predictive modeling in forest health monitoring. Climate-driven factors, such as temperature and precipitation, regulate the distribution and severity of emerging pathogens. Geospatial analyses and species distribution modeling (SDM) have been successfully applied to predict BSNB pathogen's range expansion under changing climatic conditions. Integrating these models with RS-based monitoring enhances early detection and risk assessment. However, despite these advancements, direct RS applications for BSNB detection remain limited. This review identifies key knowledge gaps and highlights the need for further research to optimize RS-based methodologies, refine predictive models, and develop early warning systems for improved forest management.