Remote sensing-based detection of brown spot needle blight: a comprehensive review, and future directions

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.

Keywords: Forest health monitoring; Geospatial analysis; Lecanosticta acicola; Needle disease; Pine forestry; Remote sensing.

Figure 1. Methodology flowchart of VOSviewer software.

Figure 2. Bibliometric network visualization of needle disease research trends.

Figure 3. Co-occurrence network of BSNB Research.

Figure 4. Close-up of infected needles and branch showing characteristic brown spots, some with yellow halos, and necrosis.

Figure 5. Remote sensing platforms and sensor combinations.

Figure 6. Bibliometric network visualization of research trends related to pine needle diseases and remote sensing.

The network illustrates major themes, including disease classification, climate change, spectral analysis, and ML/DL-based detection. While Pine wilt disease (PWD) is a vascular wilt disease and not a needle disease, its frequent association with RS studies suggests its role as a leading case study in ML/DL applications for pine disease detection.