Canadian Forest Service Publications

Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. 2016. Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Campbell, L.B. International Journal of Digital Earth.

Year: 2016

Issued by: Pacific Forestry Centre

Catalog ID: 36954

Language: English

Availability: PDF (download)

Available from the Journal's Web site.
DOI: 10.1080/17538947.2016.1187673

† This site may require a fee

Mark record


Free and open access to the Landsat archive has enabled the implementation of national and global terrestrial monitoring projects. Herein, we summarize a project characterizing the change history of Canada’s forested ecosystems with a time series of data representing 1984–2012. Using the Composite2Change approach, we applied spectral trend analysis to annual best-available-pixel (BAP) surface reflectance image composites produced from Landsat TM and ETM+ imagery. A total of 73,544 images were used to produce 29 annual image composites, generating ∼400 TB of interim data products and resulting in ∼25 TB of annual gap-free reflectance composites and change products. On average, 10% of pixels in the annual BAP composites were missing data, with 86% of pixels having data gaps in two consecutive years or fewer. Change detection overall accuracy was 89%. Change attribution overall accuracy was 92%, with higher accuracy for stand replacing wildfire and harvest. Changes were assigned to the correct year with an accuracy of 89%. Outcomes of this project provide baseline information and nationally consistent data source to quantify and characterize changes in forested ecosystems. The methods applied and lessons learned build confidence in the products generated and empower others to develop or refine similar satellite-based monitoring projects.

Plain Language Summary

The Canada Centre for Remote Sensing has been active in receiving imagery since 1972 and the archive has over 600,000 images representing Canada. While this number is large, the distribution of the images varies in space and time, and contains observations with atmospheric interference such as clouds, haze, smoke, and related shadows. Pixel-based image compositing is an approach that can be implemented to address possible shortcomings related to image availability, atmospheric interference, phenology, and sun angles. The time-series based change mapping method presented in Hermosilla et al. (2015a), hereafter referred to as Composite2Change (C2C), utilizes best-available-pixel (BAP) composites of surface reflectance values generated from archival Landsat imagery. The described image composite data represent a spatially and temporally comprehensive, nationally consistent source of information that has the spatial (30 m) and temporal (annual) resolutions necessary to characterize natural and anthropogenic changes.