Agreement Assessment of Spatially Explicit Regression-derived Forest Cover and Traditional Forest Industry Stand Type Maps

Forest regeneration assessment is an important forest management goal that requires accurate data about site-specific forest type and stand density. In this study, a methodology was developed to convert regression model output to maps of predicted softwood and hardwood percent cover at the scale of a Landsat ETM+ pixel. These maps provide forest type and percent cover at higher spatial scale (0.09 ha) than traditional GIS forest stand databases employ (e.g., 2 to 4 ha minimum mapping units). A modified accuracy assessment was performed between the Landsat regression derived maps and GIS type maps to evaluate their relative agreement. Two variations of the traditional error matrix were examined. The first was a "plus-one" matrix, where values next to the diagonal were included in the agreement calculations. The second variation, considered most appropriate for this study, included the use of "fuzzy logic" where the off-diagonal values were weighted for a better approximation of the GIS forest mapping criteria and forest type composition of the northern New England forest. The fuzzy logic error matrix indicated strong agreement between the regression derived and GIS forest type maps with an overall agreement ranging from 76 percent to 79 percent. Producer's agreement from the fuzzy-logic error matrices ranged from 89 percent to 97 percent for softwood classes and 72 percent to 77 percent for hardwood. User's agreement for softwood ranged from 71 percent to 82 percent and 80 percent to 87 percent for hardwood. These results suggest that the Landsat-derived maps can provide objective and reliable site-specific forest type and percent cover information that is not dependent on subjective photo interpretation methods. These maps will be evaluated in future studies to demonstrate practical forest regeneration management applications.