Maine Space Grant Consortium

Almost One-third of U.s. Economic and Cultural Well-being Strongly Depends on a Healthy Forest Land-base and Its Sustainable Management. the Northeastern U.s. Is the Most Heavily Forested Part of the Country With Maine Being the Most Forested State at 89%. the Forests of This Region Provide Numerous Products and Services for Rural Livelihood Wildlife and the Environment. Sound Scientifically-based Management of This Resource Requires a Significant Investment in Forest Inventory and Health Monitoring. Recent Advances in Remote Sensing (RS) Technology Are Revolutionizing the Way in Which Forests Are Measured and Monitored. the Focus of This Research Is on Providing More Detailed Finer Resolution Accurate and Near-real Time Data on I) Forest Tree Species Identification and Ii) Forest Tree Decline Detection and Damage Assessment Using Optical Air- and Space-borne Hyperspectral and Multi-spectral Data. the Goal Is to Address Immediate Information Needs for Precise Pest/pathogen Control for Current Outbreaks in Northeastern Forests as Well as for Early Intervention and Host Tree Protection. This Information Is Also Needed for Pest Risk Prediction Estimation of Economic Impacts of Outbreaks Quantifying Wood Supply Losses Identifying Changes in Wildlife Habitat and as Inputs for Landscape-scale Forest Succession and Disturbance Simulation Models. Since 2000 a Large Body of RS Sensors and Platforms Has Become Available to Overcome Shortcomings Related to the Unavailability of Optical RS Imagery and Their Cost. These Sensors Can Produce Enhanced Geospatial Data Products to Meet Research and Decision Support Needs. Despite These Considerable Advances RS Methods to Adequately Apply These Data by Combining Several Sensors and Platforms Are Not Yet Developed in Particular for Forestry Applications. Additionally High-resolution Maps of Forest Species Composition Are Not Yet Available Mainly Attributed to Limited RS Data Availability in the Past Challenges Related to the Detection of Pest Induced Damages Compared to Other Forest Disturbances and High Cost of Satellite RS Data at a Very Fine Resolution. Consequently the Long-term Goal of This Project Is to Develop Sound Approaches for Providing Detailed Geospatial Products on Forest Tree Identification and Composition Forest Defoliation/damage Caused by Recent Destructive Pest/pathogen Outbreaks Such as Spruce Budworm (SBW) Emerald Ash Borer (EAB) Caliciopsis Canker and Eastern White Pine Decline and Mortality Using RS Data. the Realization of This Goal Will Provide I) Better Models Compared to the Traditional Aerial Survey Methods to Detect and Quantify Pest-induced Defoliation/damage Ii) Potential Tools to Address Shortcomings of Current Satellite Derived Forest Disturbance Products and Algorithms and Iii) Detailed Information on Tree Species Distribution Tree Decline Indicators and Their Drivers of Change. This Proposal Addresses This Knowledge Gap by Leveraging Various Nasa RS Technologies Including Landsat and in Particular Recent Nasa Goddard S Hyperspectral-lidar-thermal System (g-liht) Data. Rather Than a Single Use Technology the Continually Updated Products Offered Here Will Both Support a Strong and Sustainable Research Program at the University of Maine (UM) as Well as Provide Stakeholders With Enhanced Forest Health Information Needed for Decision Making. While the Immediate Application of These Models and Products Will Be for Maine the Developed Models Can Be Applicable for the Entire Northeastern U.s. and Canada and in Other Regions With Similar Problems Including Western U.s. Finally Research Educational and Synergistic Activities Conducted in This Project Will Foster a Collaborative Network of Junior and Senior Faculty Across Various Disciplines as Well as Post-doctoral Researchers Graduate/undergraduate Students Working With Nasa Scientists and Researchers and Other Institutes Within and Outside Maine.