Interdisciplinary Collaboration

The Wheatland Lab provides researchers opportunities to apply cutting-edge geospatial technologies and tools to interdisciplinary projects

Interdisciplinary projects


This project has organized and carried out initial investigations into the use of LiDAR remote sensing analysis to enhance the design and operation of inventory programs for Maine’s forest industry stakeholders.


The University of Maine conducts research in collaboration with Brookhaven National Laboratory and Oak Ridge National Laboratory toward a subset of science objectives outlined in the Phase 3 proposal of the Next Generation Ecosystem Experiments (NGEE) in the Arctic.


This research is focused on (1) the impacts of wildfires on the global terrestrial biosphere carbon cycle, (2) the patterns of biome carbon fluxes in response to droughts at different time scales, (3) modeling the dissolved organic carbon flux, and (4) estimating carbon fluxes with Terrestrial Ecosystem Model.


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. This project focuses on analyzing satellite imagery to determine the extent to which climate influences the timing and duration of phenological cycles in Maine’s forests and how these changes may influence the timing and way in which people recreate throughout Maine’s state parks.


This research is focused on the advanced detection of health stress in agricultural fields and forests, which can prompt management responses to mitigate detrimental conditions such as nutrient deficiencies and disease. New applications in hyperspectral data and imaging spectroscopy to agriculture and forests have shown potential for early stress detection. We build on previous work by assessing three different systems; drought stress in wild blueberry vegetation through multi-temporal observations, disease classifiers in potato fields, and vigor ratings in ash trees on various scales of emerald ash borer (EAB) infestation.


This research is focused on the development of regional deep learning models using three dimensional convolutional neural networks to estimate forest inventory metrics (stem volume, tree count, biomass, ect..) from LiDAR.  These models are applied to public LiDAR to generate 10m maps of the New England and Atlantic Canada’s forests.


This research is focused on utilizing imagery from the Landsat archive to produce maps of forest disturbance from 1985 to 2017 for the New England states and the Canadian Maritime provinces.


This Master of Science thesis details a range of comparisons and analyses of aerial image acquisition methods for the creation of 3D point clouds to derive forest structure metrics.  

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