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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. [ABSTRACT FROM AUTHOR]

Emerald Basin on the Scotian Shelf off Nova Scotia, Canada, is home to a globally unique aggregation of the glass sponge Vazella pourtalesi, first documented in the region in 1889. In 2009, Fisheries and Oceans Canada (DFO) implemented two Sponge Conservation Areas to protect these sponge grounds from bottom fishing activities. Together, the two conservation areas encompass 259 km2. In order to ascertain the degree to which the sponge grounds remain unprotected, we modelled the presence probability and predicted range distribution of V. pourtalesi on the Scotian Shelf using random forest modelling on presence-absence records. With a high degree of accuracy the random forest model predicted the highest probability of occurrence of V. pourtalesi in the inner basins on the central Scotian Shelf, with lower probabilities at the shelf break and in the Fundian and Northeast Channels. Bottom temperature was the most important determinant of its distribution in the model. Although the two DFO Sponge Conservation Areas protect some of the more significant concentrations of V. pourtalesi, much of its predicted distribution remains unprotected (over 99%). Examination of the hydrographic conditions in Emerald Basin revealed that the V. pourtalesi sponge grounds are associated with a warmer and more saline water mass compared to the surrounding shelf. Reconstruction of historical bottom temperature and salinity in Emerald Basin revealed strong multi-decadal variability, with average bottom temperatures varying by 8°C. We show that this species has persisted in the face of this climatic variability, possibly indicating how it will respond to future climate change. [ABSTRACT FROM AUTHOR]

Understanding the development of landscape patterns over broad spatial and temporal scales is a major contribution to ecological sciences and is a critical area of research for forested land management. Boreal forests represent an excellent case study for such research because these forests have undergone significant changes over recent decades. We analyzed the temporal trends of four widely-used landscape pattern indices for boreal forests of Canada: forest cover, largest forest patch index, forest edge density, and core (interior) forest cover. The indices were computed over landscape extents ranging from 5,000 ha (n = 18,185) to 50,000 ha (n = 1,662) and across nine major ecozones of Canada. We used 26 years of Landsat satellite imagery to derive annualized trends of the landscape pattern indices. The largest declines in forest cover, largest forest patch index, and core forest cover were observed in the Boreal Shield, Boreal Plain, and Boreal Cordillera ecozones. Forest edge density increased at all landscape extents for all ecozones. Rapidly changing landscapes, defined as the 90th percentile of forest cover change, were among the most forested initially and were characterized by four times greater decrease in largest forest patch index, three times greater increase in forest edge density, and four times greater decrease in core forest cover compared with all 50,000 ha landscapes. Moreover, approximately 18% of all 50,000 ha landscapes did not change due to a lack of disturbance. The pattern database results provide important context for forest management agencies committed to implementing ecosystem-based management strategies. [ABSTRACT FROM AUTHOR]