Stephanie Willsey is a 2nd year Masters student under Dan Hayes in the School of Forest Resources at the University of Maine. Her current research focuses on how calibration plot data affects enhanced forest inventories in Maine. Before becoming a member of the Barbara Wheatland Geospatial Lab, she was a Master of Forestry student at […]
Xinyuan’s research focuses primarily on understanding the terrestrial biosphere carbon cycle with process-based earth system models. His work involves analyzing the effects of fire on the global terrestrial biosphere carbon cycle, investigating the interactions between climate extremes and the land-atmospheric carbon flux, modeling the land-to-ocean dissolved organic carbon flux.
UMaine & NASA to map carbon in forests!
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)
Moving Beyond Enhanced Forest Inventory (EFI): Accelerating the Digitalization of the Forest Value-Chain
Please join us for next week’s Wheatland Seminar! Adam Dick Forest Research Project Leader Canadian Wood Fibre Centre / Canadian Forest Service Wednesday, October 14, 2020 3:00 pm ET via Zoom (https://maine.zoom. us/j/95874189170)
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; […]
The Effects of Climate Change on Forest Ecosystem Phenology and its Impacts on Tourism in Maine
Over the past 25 years, the average annual temperature in Maine increased by nearly 2 °C and is predicted to continue rising if global carbon emissions are not reduced. Climate warming is affecting the social and ecological systems of the northeastern U.S., including changes in the seasonal timing and duration of biophysical processes. Phenology is […]
A synthesis study of the terrestrial ecosystem carbon cycle at various temporal and spatial scales
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 […]