Canadian Forest Service Publications

Integration of forest inventory and satellite imagery: a Canadian status assessment and research issues. 2005. Remmel, T.K.; Csillag, F.; Mitchell, S.; Wulder, M.A. Forest Ecology and Management 207: 405-428.

Year: 2005

Issued by: Pacific Forestry Centre

Catalog ID: 25391

Language: English

Availability: Not available through the CFS (click for more information).

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Canada's ability to sustainably manage approximately 10% of the global forest cover is a critical environmental and economic issue. The capacity to meet such demands and to deliver on national and international commitments regarding forest management is enabled through collaboration between federal, provincial, and territorial agencies. A principal collaborator is the National Forest Inventory (NFI); a systematic photo-plot based monitoring system designed specifically for reporting purposes and as an important input for scientific models. Satellite imagery is illustrated here as a support data set to ensure the quality of the NFI, for auditing the photo-plot contents, and to detect spatial biases. The Canadian Forest Service, in collaboration with the Canadian Space Agency and other federal and provincial agencies, is producing a national land cover database of the forested area of Canada (Earth Observation for Sustainable Development of Forests (EOSD)) using Landsat-7 ETM+ data for circa 2000 conditions. The integration between the plot-based NFI with classified EOSD data is presented for central British Columbia, an area comprising 6 Landsat scenes and 324 2 km × 2 km photo-plots. Traditional accuracy assessment measures based on the analysis of coincidence matrices are reported as levels of agreement for hierarchically aggregated land cover categories (overall agreements of 91%, 79%, 64% and 26% for 3, 4, 6 and 20 classes respectively) to demonstrate coincidence between the different data products. Local agreement between NFI and EOSD is demonstrated as a means of photo-plot auditing while spatial biases are detected through investigations of geographic pattern in the coincidence values. The illustrated approaches may be expanded or applied to different mapped attributes (e.g., biomass) that are of utility to those attempting to characterize large areas in a consistent and rigorous fashion.