Context

Climate change and global trade have increased the risk of pests and diseases affecting forests. Remote sensing (satellite imaging) technology enables scientists to detect pathogens (disease-causing microorganisms) and signs of stress in forests, such as changes in colour, moisture, and canopy structure, long before these signs and symptoms become visible.  

Detecting these issues early enables forest managers to implement proactive measures to reduce ecological damage and avoid costly interventions. Ongoing monitoring of these subtle changes supports adaptive management strategies, which help to strengthen forest resilience.  

Research aims and objectives

Aim:

Establish the groundwork for a National Monitoring System (NMS) for the early detection of forest pathogens in the UK.

Objectives:

Calibration of metabolite detection (understanding tree stress signals) 

• Controlled experiments will help to differentiate between tree stress caused by drought, and stress caused by early signs of disease (pathogen detection). Special lab equipment (Liquid Chromatography-Mass Spectrometry [LC-MS]) will be used to detect chemical changes in the tree linked to pathogen infection. Foliar (leaf) analysis and hyperspectral reflectance (light reflection pattens) will be used to spot early warning signs of both environmental and disease-related stress.  

Species mapping 

• High resolution LiDAR (remote sensing) and satellite images will be combined to create detailed maps of forest species that may act as pathogen hosts (trees that may carry or spread diseases). Image analysis will identify tree species by examining phenological cycles (seasonal timing of recurring life events such as budburst and leaf development) and differentiate them using remote sensing and machine learning techniques.

Early detection of stress 

• Remote sensing will be expanded from small-scale tests to landscape monitoring. Drone surveys will collect clear, detailed images and data over the target forest areas, and satellite imagery will enable long-term monitoring of tree health and stress patterns on a broader scale.  

End-user engagement 

• To ensure these new remote sensing technologies are useful in the field, the project will collaborate with forest managers and tree health inspectors to ensure the project outputs are accessible through a user-friendly web application.  

Expected outcomes

• Identification of key chemical signals produced by trees under environmental or disease-related stress, using remote sensing technologies for early detection.  

• Development of detailed, spatially accurate maps of individual tress, including information on species, height, canopy dimensions, and volume, linked to the National Grid for future monitoring.  

• Establishment of a drone-based survey method, incorporating LiDAR and multispectral imaging (image data captured within specific wavelength ranges across the electromagnetic spectrum), to assess early indicators of stress and potential presence of pathogens.  

• Creation of a satellite-based monitoring system that uses time-series imagery to identify abnormal changes in tree health, supporting early detection of disease outbreaks.  

• Delivery of a web-based platform that enables forest managers and forest health inspectors to explore data, monitor disease outbreaks, and plan forest surveys efficiently.