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Landscape scale mapping of forest inventory data by nearest neighbor classification

Informally Refereed

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.
https://www.fs.usda.gov/research/treesearch/17314