Science Highlights

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.

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.

Intermediate Model Overcomes Computational Costs of Simulating Thermal-Hydrology for Polygonal Landscapes

Modeling approach improving representations of permafrost dynamics in evolving landscapes.

Isotope Labelling Study Identifies Importance of Rooting Depth in Nitrogen Uptake of Tundra Vegetation

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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.

Analysis of Pond and Lake Quantifies Size-Class Distribution of Water Bodies in High-Latitude Ecosystems

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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.

A Global Trait-Based Approach to Estimate Leaf Nitrogen Functional Allocations from Observations

Observationally-constrained photosynthetic traits for land models.

Integration of Unmanned Aerial System, Surface Geophysics & other Multi-Scale Data to Map Arctic Snow Depth

The ability to map snow depth in high resolution and over a large areas permits for the first time a dataset that can be used to quantify the impact of heterogeneous snow distribution on hydrology and carbon exchange.