Large-scale probabilistic identification of boreal peatlands using Google Earth Engine, open-access satellite data, and machine learning

PLoS One. 2019 Jun 17;14(6):e0218165. doi: 10.1371/journal.pone.0218165. eCollection 2019.


Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands-a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage-in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alberta
  • Biodiversity
  • Carbon / chemistry
  • Climate Change
  • Conservation of Natural Resources / methods*
  • Earth, Planet
  • Ecosystem
  • Machine Learning
  • Radar
  • Taiga
  • Wetlands


  • Carbon

Grant support

Funding in support of this work was received from the Alberta Environment and Parks and the Government of Alberta’s Land Use Secretariat and from the Alberta Biodiversity Monitoring Institute (ABMI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.