Classification of annual non-stand replacing boreal forest change in Canada using Landsat time series: a case study in northern Ontario

Standardized protocols for forest change detection and classification based on Landsat time series data are becoming more common for use in characterizing multi-decadal history or trends in forest dynamics. One such protocol, referred to as Composite-2-Change (C2C), is a highly automated process developed in Canada that is applicable across extensive forest regions and includes change detection and typing based on Best-Available-Pixel image compositing, spectral trend analysis of breakpoints, object-based segmentation, and Random Forest classification. The aim of this article is to assess the classification accuracy of the Composite-2-Change (C2C) protocol in an eastern Canadian boreal forest environment in northern Ontario. Results demonstrated that the Landsat-derived change detection and attribution approach was approximately 90% accurate for stand-replacing forest change (fire, harvesting, roads), and approximately 75% for four non-stand replacing forest changes caused by spruce budworm and forest tent caterpillar defoliation, wetland and forest flooding caused by localized hydrological variations, and one class of multiple/other disturbances. The C2C protocol approach offers unique independent data layers for modelling that can be used to relate and inform on a range of substantive and subtle changes, which in turn can be labelled and tracked, offering otherwise unavailable information on forest dynamics over large areas.