Abstract
One of the goals of the Forest Service, U.S. Department of Agriculture's Forest Inventory and Analysis (FIA) program is large-area mapping. FIA scientists have tried many methods in the past, including geostatistical methods, linear modeling, nonlinear modeling, and simple choropleth and dot maps. Mapping methods that require individual model-based maps to be produced are time and labor intensive. FIA needs a method that will enable efficient production of large numbers of landscape-scale maps for State reports. The current study presents a case study for the State of Ohio of a nearest neighbor classification method that uses multivariate similarity as a criterion for attaching FIA plot data to pixels with unknown forest attributes to make continuous maps of any FIA attribute. The goal of the study was to devise a landscape scale mapping method that could be easily implemented at a national scale.
Parent Publication
Citation
Lister, Andrew. 2009. Landscape scale mapping of forest inventory data by nearest neighbor classification. In: McRoberts, Ronald E.; Reams, Gregory A.; Van Deusen, Paul C.; McWilliams, William H., eds. Proceedings of the eighth annual forest inventory and analysis symposium; 2006 October 16-19; Monterey, CA. Gen. Tech. Report WO-79. Washington, DC: U.S. Department of Agriculture, Forest Service. 265-272.