Science Highlights

Mapping Vegetation Communities using Convolutional Neural Networks

Artificial intelligence applied to multiple remote sensing datasets accurately represents shrub distribution across hillslopes at the Kougarok field site.

Photosynthesis by Tundra Plants Sensitive to Low Temperatures

Light-harvesting processes involved in photosynthesis by tundra plants are unexpectedly sensitive to growth at low temperatures.

NGEE Arctic and EMSL collaborate to provide molecular-scale insights into SOM degradation

Biochemical composition is proposed to improve process-based models of SOM degradation and climate feedbacks

Landscape Mapping using Remote Sensing and Neural Networks

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.

Spatial and temporal controls on nitrogen availability in polygonal tundra landscapes

Several scaling strategies based on geomorphology were evaluated for complex polygonal landscapes.