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.
Microtopography Determines How CO2 and CH4 Exchanges Respond to Temperature and Precipitation in Polygonal Tundra
Microtopographic variation among troughs, rims, and centers strongly affects the movement of surface water and snow and thereby affects soil water contents and active layer development.
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.
PeRL: A Circum-Arctic Permafrost Region Pond and Lake Database
Ponds and lakes affect high-latitude carbon, water, and energy budgets, however, there is no good observationally-constrained characterization of waterbodies for high-latitude systems. The Permafrost Region Pond and Lake (PeRL) database addresses this problem. PeRL includes 69 maps covering a wide range of environmental conditions.