Automated detection of thermoerosion in permafrost ecosystems using temporally dense Landsat image stacks

Mark J. Lara, Melissa Chipman, Feng Sheng Hu

Research output: Contribution to journalArticle

Abstract

Anthropogenic climate change has been linked to the degradation of permafrost across northern ecosystems, with notable implications for regional to global carbon dynamics. However, our understanding of the spatial distribution, temporal trends, and seasonal timing of episodic landscape deformation events triggered by permafrost degradation is hampered by the limited spatial and temporal coverage of high-resolution optical, RADAR, LIDAR, and hyperspectral remote sensing products. Here we present an automated approach for detecting permafrost degradation (thermoerosion), using meso-scale high-frequency remote sensing products (i.e., Landsat image archive). This approach was developed, tested, and applied in the ice-rich lowlands of the Noatak National Preserve (NOAT; 12,369 km2) in northwestern Alaska. We identified thermoerosion (TE) by capturing the spectral signal associated with episodic sediment plumes in adjacent water bodies following TE. We characterized and extracted this episodic turbidity signal within lakes during the snow-free period (June 15–October 1) for 1986–2016 (continuous data limited to 1999–2016), using the cloud-based geospatial parallel processing platform, Google Earth Engine™. Thermoerosional detection accuracy was calculated using seven consecutive years of sub-meter high-resolution imagery (2009–2015) covering 798 (~33%) of the 2456 lakes in the NOAT lowlands. Our automated TE detection algorithm had an overall accuracy and kappa coefficient of 86% and 0.47 ± 0.043, indicating that episodic sediment pulses had a “moderate agreement” with landscape deformation associated with permafrost degradation. We estimate that lake shoreline erosion, thaw slumps, catastrophic lake drainage, and gully formation accounted for 62, 23, 13, and 2%, respectively, of active TE across the NOAT lowlands. TE was identified in ~5% of all lakes annually in the lowlands between 1999 and 2016, with a wide range of inter-annual variation (ranging from 0.2% in 2001 to 22% in 2004). Inter-annual variability in TE occurrence and spatial patterns of TE probability were correlated with annual snow cover duration and snow persistence, respectively, suggesting that earlier snowmelt accelerates permafrost degradation (e.g. TE) in this region. This work improves our ability to detect and attribute change in permafrost degradation across space and time.

Original languageEnglish (US)
Pages (from-to)462-473
Number of pages12
JournalRemote Sensing of Environment
Volume221
DOIs
StatePublished - Feb 1 2019
Externally publishedYes

Fingerprint

Permafrost
permafrost
Landsat
Ecosystems
Lakes
Degradation
degradation
ecosystems
lowlands
lakes
Snow
lake
snow
remote sensing
Remote sensing
Sediments
sediments
snowmelt
snowpack
meters (equipment)

Keywords

  • Alaska
  • Arctic
  • Google Earth Engine
  • Lake drainage
  • Lake expansion
  • Landsat
  • Noatak
  • Permafrost thaw
  • Retrogressive thaw slump
  • Thermoerosion
  • Thermokarst
  • Tundra

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

Cite this

Automated detection of thermoerosion in permafrost ecosystems using temporally dense Landsat image stacks. / Lara, Mark J.; Chipman, Melissa; Hu, Feng Sheng.

In: Remote Sensing of Environment, Vol. 221, 01.02.2019, p. 462-473.

Research output: Contribution to journalArticle

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