NGEE Arctic
Next-Generation Ecosystem Experiments
Advancing the predictive power of Earth system models through understanding
of the structure and function of Arctic terrestrial ecosystems
Media Mentions
Science Highlights
September 12th, 2018
A convolutional neural network (CNN) approach produced highly accurate vegetation classifications. Hyper-spectral datasets (e.g., AVIRIS) were most useful for our machine learning approaches. Accurate and high-resolution datasets generated using our approach are needed for Arctic models.
September 12th, 2018
Biochemical composition is proposed to improve process-based models of SOM degradation and climate feedbacks
September 12th, 2018
Several scaling strategies based on geomorphology were evaluated for complex polygonal landscapes.
Progress and Accomplishments
Investigations of topographic control on thermokarst development and the ground thermal regime in ice wedge polygons using the Advanced Terrestrial Simulator
NGEE Arctic researchers found that rim height and trough depth in ice wedge polygons considerably influence the vulnerability of the underlying permafrost, shaping feedbacks which ultimately control topographic deformation and increased soil aeration in t
NGEE Arctic scientists attend NASA ABoVE annual meeting
Shawn Serbin represented NGEE Arctic at the NASA ABoVE meeting, showcasing our science and identifying opportunities for synergy with NASA ABoVE.
Characterization of permafrost landscapes using an unmanned aerial system: LiDAR mapping the NGEE Arctic Teller field site
A state-of-the-art unmanned aerial system was deployed to collect centimeter-resolution imagery and topographic data of the NGEE Teller research site.