Landsat ETM+ classification of Fluxnet Canada flux stations

Changes in land use and cover and natural disturbance are thought to be major controls of the dynamic sink/source balance for the immense boreal terrestrial carbon stock. Fluxnet-Canada is a national research network developed to study the influence of climate and disturbance on terrestrial carbon cycling along an east-west transect of Canadian forest and peatlands. The purpose of the present work was to create large-area land cover classifications from satellite imagery to support Fluxnet scaling and modeling studies. The methodology presented in Cihlar et al. (2003) was implemented on seven Fluxnet monitoring sites across Canada. A Landsat ETM+ image covering each site was clustered to 1501 classes using unsupervised K-Means classification prior to 50-cluster merging through classification by generalization (CPG). The resulting clusters were merged to 16-class landcover maps through interactive labeling using cluster bitmaps to create spatial context for the 50 clusters. The bitmaps aided the analyst when ground data was scarce or nonexistent. The products were sent to Fluxnet-Canada site managers for ground validation and the classifications were refined according to the feedback provided. The final landcover maps were used in the calculation of leaf area index (LAI) for the Landsat ETM+ scenes, thus allowing for upscaling of carbon flux measurements based on the correlation of LAI to carbon flux.