The School of Forest Resources at the University of Maine is currently recruiting potential students for graduate research assistantship positions (M.S. and PhD) in remote sensing of forests. This represents an exciting opportunity for skilled and motivated students to gain experience in – and contribute to – high level science and state-of-the-art applications in the […]
Check out this article in Mainebiz about our new project with NASA and the Center for Research on Sustainable Forests to map forest carbon with spaceborne LiDAR!
Leveraging High Resolution Forest Inventory and Satellites to Predict Fire Damage Severity at the Micro-Stand Level
Please join us for our next Wheatland Seminar! Cassidy Rankine Director of Remote Sensing High Resolution Inventory Solutions (HRIS) Wednesday, November 4, 2020 3:00 pm ET via Zoom (https://maine.zoom. us/j/95874189170)
Kevaughan Smith is an MS candidate in the School of Forest Resources. He earned his BS in Environmental Studies from the University of Maine at Fort Kent in May 2020. His most recent work involved combining high-resolution aerial hyperspectral scans of Boreal and Arctic tundra communities with specimen-based ground-truthed data, to generate fine-scale, species-level vegetation […]
Two recent articles on an interdisciplinary research project to develop AI tools for automated seabird surveys in coastal Maine using plane and UAV-based aerial imagery. Bangor Daily News article here UMaine News article here
Forest Disturbance Detection and Aboveground Biomass Modeling Using Moderate-Resolution, Time-Series Satellite Imagery
Human-induced and natural disturbances are an important feature of forest ecosystems. Disturbances influence forest structure and composition and can impact crucial ecosystem services. However, deriving spatially explicit estimates of past forest disturbance across a large region can prove challenging. Researchers have recognized that remote sensing is an important tool for monitoring forest ecosystems and mapping […]
Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Forest Inventories of New England
For two decades Light Detection and Ranging (LiDAR) data has been used to develop spatially-explicit forest inventories. Data derived from LiDAR depict three-dimensional forest canopy structure and are useful for predicting forest attributes such as biomass, stem density, and species. Such enhanced forest inventories (EFIs) are useful for carbon accounting, forest management, and wildlife habitat […]
Airborne Hyperspectral Data Application in Health Stress Detection of Blueberry Fields and Ash Trees
Advanced detection of health stress in agricultural fields and forests can prompt management responses to mitigate detrimental conditions such as nutrient deficiencies, disease, and mortality. New applications in hyperspectral data and imaging spectroscopy on agriculture and forests have shown the potential for early stress detection. We build on previous work by assessing two different systems; […]
This research includes analyzing the impacts of wildfires on the global terrestrial biosphere carbon cycle, assessing the patterns of biome carbon fluxes in response to droughts at different time scales, and estimating carbon fluxes with Terrestrial Ecosystem Model. This includes modeling the lateral dissolved organic carbon (DOC) flux into an earth system model. The lateral […]
Wheatland Lab members Xinyuan Wei & Dan Hayes contributed to the newly released State of Maine Carbon Budget.