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LWEC TH phase 1 - Early Detection and Management of Tree Pathogens and Pest - TH0123

This CCBP aims to establish initial feasibility of the use of new technologies and processes for early detection of tree(s) under stress from pathogens and pests. The project will disseminate initial views on the potential use of a range of new and cross-disciplinary technologies and approaches, and will develop networks and consult with relevant organisations to draw views, ideas and commitment to the approach. The project will recruit additional Consortium members to build capacity for a comprehensive longer term project which would also encompass an approach to early stage pathogen and pest management.
The project falls primarily within the DETECTION call theme, with strong links to BIOSECURITY and SYSTEMS. The project draws strongly on translation of knowledge and evidence, including:
1. Application of UAV remote sensing to detect pests and diseases of forests, agricultural crops and pathogens such as P. ramorum in larch and Dothistroma in pine, (CLA, ESL, FR, UoE)
2. Remote sensing experience in forestry and in tree health including use of thermal cartography to map carotenoids in leaves and investigate anomalous variations in canopy temperature (FR, UoE)
3. Early stage research to establish feasibility for forest pest and disease management using unmanned vehicles, incorporating users’ viewpoint on practical opportunities for application. (CLA, ESL, FR)
4. Validity of public engagement in environmental monitoring / crowd-sourced data architecture(ESL, FR).
5. Forestry expertise in ground truthing, and correlation with remote sensing data (BU, FR, UoE)
6. Experience in building networks and collaborative projects (ALL).
The potential for impact of this project is high. Modern high resolution images and sharply reduced RS costs offer exciting opportunities for the practical application of RS for early detection in tree health, as already being introduced elsewhere in Europe. Integration of bulk EO data with other Tree Health Incident databases will extend FR and Fera’s current initiatives to improve surveillance database inter-operability (OPAL and LIFE+ ObservaTREE projects) and will dramatically improve the evidence base for tree health policy and management. The consortium’s cross-disciplinary scientific experience provides a strong background in detection and management methods; its mix of academic and private sector partners provides a breadth of approaches and will assist these new technologies transfer rapidly from scientific research to practical implementation, achieving large cost savings versus current approaches.
i) Preliminary meeting of Consortium partners to scope activities and case study development; review conventional knowledge and how conventional forestry data is built up. Review how new technologies in areas such as remote sensing are beginning to be utilized and how this can add value. Identify how examples of this data and knowledge can be drawn upon in a case study for dissemination and consultation. Key consultees will be identified and approached, including international practitioners.
ii) Develop case study -review and further analysis of existing remote sensing multispectral data from forest stands infected with P ramorum. Overall architecture of approach, additional ground truthing, potential tree stress factors & means of detection. Use multi-party Extranet (Huddle) to share material.
iii) Include early stage findings from existing projects such as COBWEB which seeks to design, develop and validate the necessary software infrastructure to facilitate and make possible the opportunistic harvesting and quality control of crowd sourced environmental data.
iv) Document and circulate case study as basis for 1-day workshop and discussion with key consultees.
v) Plan workshop and approach which aims to discuss the case study in the context of future opportunities for early detection of pathogens and pests, and the technologies and facilities needed.
vi) Facilitate and document workshop outcomes and where possible ensure links and relevance to other Tree Health projects.
vii) Report on outcomes of consultation, including core technologies required, likely methodologies, initial feasibility and risk assessment.
1. A case study of a woodland(s) infected with P. ramorum, including initial assessment of feasibility of new and or translational technologies / processes for generic early detection of tree(s) under stress from pathogens and pests using multispectral data.
2. A workshop and discussion with key consultees will review new approaches to early stage detection.
3. Report of workshop outcomes.
4. Post-workshop development of an outline plan for future work to develop a practical and cost-effective process for early pathogen and pest detection utilizing remote sensing and other techniques. The creation of a higher capacity consortium incorporating the main technologies and approaches for the plan for future work, including the role of public and stakeholder engagement in early detection.
Project Structure: In view of the tight Phase 1 timescales, project management is critical for progress and integration of activities. Sue Wolfe will act as the single Project Manager (PM) on behalf of the Consortium to ensure co-ordination and progress against the project plan. Each collaborator will identify a main Point of Contact (PoC) who, with the PM will form the Project Management Group (PMG).
Key Reviews: This short project has two major review points: i) a project kick-off to finalise project details, project dependencies and risk management; and ii) final review prior to drafting the final documentation. Ongoing Project Management: Supplementing the key reviews, PMG will hold short fortnightly tele-conferences to review progress and any potential issues and ensure strong integration of the work streams.
See project description
Project Documents
• EVID4 - Final project report : TH0123 Bangor Final Report   (1501k)
Time-Scale and Cost
From: 2013

To: 2013

Cost: £96,674
Contractor / Funded Organisations
Bangor University
Plant health