Quantification of active layer depth at multiple scales in Interior Alaska permafrost

Much of Interior Alaska is underlain by permafrost that has been thawing at an unprecedented rate. Top-down expansion of the seasonally thawed ‘active layer’ and development of thermokarst features are increasing across the landscape. This can be attributed primarily due to a warming climate and dis...

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Bibliographic Details
Published in:Environmental research letters 2024-03, Vol.19 (3), p.34013
Main Authors: Brodylo, David, Douglas, Thomas A, Zhang, Caiyun
Format: Article
Language:English
Subjects:
Climate change
Disturbances
Global warming
Lidar
Machine learning
Mathematical models
Permafrost
permafrost thaw estimation
Regional analysis
remote sensing
Satellite observation
Statistical analysis
Thawing
Thickness
Time measurement
Uncertainty
upscaling
Wildfires
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Summary:Much of Interior Alaska is underlain by permafrost that has been thawing at an unprecedented rate. Top-down expansion of the seasonally thawed ‘active layer’ and development of thermokarst features are increasing across the landscape. This can be attributed primarily due to a warming climate and disturbances like wildfires which have accelerated summer season permafrost thaw. Quantification of active-layer thickness (ALT) is critical to understanding the response of permafrost terrains to these disturbances. ALT measurements are time consuming, and point based. As a result, there are large uncertainties in ALT estimates at regional/global scales (100 km 2 or larger) using field scale (1 m 2 ) measurements as direct inputs for calibrating/validating large scale process-based or statistical/empirical models. Here we developed a framework to link field scale ALT measurements with satellite observations to a regional scale (100 km 2 ) via an intermediary upscaling of field scale ALT to the local scale (1 km 2 ) with fine-resolution airborne hyperspectral and light detection and ranging data, thus leading to a characterization of ALT across space and time at multiple scales. We applied an object-based machine learning ensemble approach to upscale field scale (1 m 2 ) measurements to the local (1 km 2 ) and regional scale (100 km 2 ) and achieved encouraging results across three permafrost experimental sites in Interior Alaska that represent a variety of terrain types. Our study demonstrates that generating local scale data products is an effective approach to bridge the gap with field scale measurements and regional scale estimations as it seeks to reduce upscaling uncertainty.
ISSN:1748-9326
1748-9326
DOI:10.1088/1748-9326/ad264b