Mapping attributes of Canada’s forests at moderate resolution through kNN and MODIS imagery

• Published in: Canadian journal of forest research Vol. 44; no. 5; pp. 521 - 532
• Main Authors: BEAUDOIN, A, BERNIER, P. Y, GUINDON, L, VILLEMAIRE, P, GUO, X. J, STINSON, G, BERGERON, T, MAGNUSSEN, S, HALL, R. J
• Format: Journal Article
• Language: English
• Published: Ottawa, ON NRC Research Press 01.05.2014; National Research Council of Canada; Canadian Science Publishing NRC Research Press
• Subjects: Analysis; attribut de peuplement; Biological and medical sciences; Biomass; biomasse; boreal forest; Canada; composition; forest inventory; Forestry; Forests; Forests and forestry; forêt boréale; Fundamental and applied biological sciences. Psychology; inventaire national de référence; Inventory control; issues and policy; land cover; moderate resolution imaging spectroradiometer; Mountain regions; méthode non paramétrique; national baseline inventory; National forests; new products; Nonnative species; nonparametric method; Nonparametric statistics; Plant species; Production management; Relative abundance; Remote sensing; Sampling; spatial data; spectral analysis; stand attribute; Taigas; Topography; trees; télédétection; Canada; North America; America; Cartography; Forests; Imagery; Attribute; Forestry
• ISSN: 0045-5067; 1208-6037; 1208-6037
• DOI: 10.1139/cjfr-2013-0401

Canada’s National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 10 6 km 2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data ( http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401 ). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km 2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests ( https://nfi.nfis.org ).