Brian Groenke

Brian Groenke

A data-centric workflow for autonomous monitoring of Arctic land surface parameters

Land surface is the interface between the solid Earth and the atmosphere (climate). This layer can be regarded as the “skin of the Earth” and it sets the fluxes of energy, carbon and water between land and atmosphere. In particular albedo governs how the land surface functions in the Earth’s climate system. Dramatic seasonal variations in albedo are driven by snow cover, vegetation characteristics and topography at spatial scales from local to global. Longer growing seasons due to shorter snow cover (but thicker snow packs) and a shift in vegetation community (e.g., the spread of shrubs) are rapidly changing the Arctic land heat balance.
We need new methods to monitor and understand the impact of these changes. However, most Arctic land remote sensing data are available only at too coarse a resolution. Only a sparsely and unevenly distributed network of Arctic observational and validation sites exists. Data from these disparate and heterogeneous sources is available but cleansing and collation of the data at multiple spatial scales is needed
In this project, we will develop a workflow for monitoring of the spatial and temporal variability of snow, vegetation and topography of the Arctic. It will harvest data from existing field sites of the circumpolar network and combine them with remote sensing products to fill the gap across scales. The project will deliver: i) a data processing hub that integrates all available imagery and data to infer snow surface, vegetation and topography parameters, ii) a data acquisition platform that employs low-cost sensors (cameras, an inertial measurement unit, GPS and a compass) to collect local measurements. The workflow, once validated at two long term observational sites (Siberia and Svalbard), will be made available for use at circum-Arctic sites to monitor the land surface properties over time. In addition to the data collected at tower locations, the data acquisition platform will collect local measurements, registered using artificial intelligence (e.g. SLAM) algorithms. Such a platform would be attached to a low-cost vehicle (blimps or drones) or be carried by a person. The workflow may be applied at any site to match and interpret available satellite-derived data.

Peer-reviewed Publications (journal or conference)

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Other (presentations at conferences or preprints)

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