Lincoln University

MISSOURI'S Forests, Home to at Least 730 Wildlife Species, Are Facing Urgent Health Issues From Invasive Species Pests and Their Impacts Necessitating Effective Monitoring Methods. This Project Aims to Improve Forest Health Monitoring by Integrating Geospatial Technologies With Advanced Ai Models to Develop a Forest Health Early Warning System (fhews). We Will Combine Satellite Imagery, Uav-based Multispectral and Lidar Data, Ecological Information and Location-based Forest Health Metrics to Create a Comprehensive Geospatial Database. Ai Models Will Analyze This Data for Forest Health Monitoring. Key Objectives of This Integrated Project Include: 1. Developing and Training Ai Models to Detect Early Signs of Forest Health Issues and Creating a User-friendly Interface for Missouri Stakeholders. 2. Promoting Educational Programs at Lincoln University (LU) and Increasing Student Involvement in Forestry, Geospatial Science and Ai Through Mentoring Scholarships and Providing Student Experimental Learning Opportunities. 3. Hosting Workshops to Disseminate Findings and Train Missouri Stakeholders in Using Fhews to Facilitate Knowledge Transfer and Practical Application. Co-pd With Expertise in Remote Sensing and Forest Health Will Be Critical in Developing and Validating Ai Models. Aifarms Institute at Uiuc Will Enhance LU'S Ai Capabilities. Brad Graham From the Missouri Department of Conservation Will Organize Workshops to Ensure Effective Knowledge Transfer. This Project Aligns With the CBG Program by Educating Students, Strengthening Partnerships and Enhancing Education, Research and Extension Programs at Lu. It Supports USDA'S Focus on Building Ai and Machine Learning Capacity in Food and Agricultural Sciences.