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.
Field Measurements of Photosynthetic Biochemistry Provide Improved Representation of Gas-Exchange in ESMs
Study highlights the poor representation of Arctic photosynthesis in TBMs, and provides the critical data necessary to improve our ability to project the response of the Arctic to global environmental change.