Details

Autor(en) / Beteiligte

• DeLancey, Evan Ross
• Kariyeva, Jahan
• Bried, Jason T
• Hird, Jennifer N
• Aldrich, Stephen P.

Titel

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

Ist Teil von

• PloS one, 2019-06, Vol.14 (6), p.e0218165

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2019

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.