The k-Nearest Neighbor (kNN) method of forest attribute estimation and mapping has become an integral part of national forest inventory methods in Finland in the last decade. This success of kNN method in facilitating multi-source inventory has encouraged trials of the method in the Great Lakes Region of the United States. Here we present results from applying the method to Landsat TM and ETM+ data and land cover data collected by the USDA Forest Service's Forest Inventory and Analysis (FIA) program. In 1999, the FIA program in the state of Minnesota moved to a new annual inventory design to reach its targeted full sampling intensity over a 5-year period. This inventory design also utilizes a new 4-subplot cluster plot configuration. Using this new plot design together with 1 year of field plot observations, the kNN classification of forest/nonforest/water achieved overall accuracies ranging from 87% to 91%. Our analysis revealed several important behavioral features associated with kNN classification using the new FIA sample plot design. Results demonstrate the simplicity and utility of using kNN to produce FIA defined forest/nonforest/ water classifications.
Bibliographical noteFunding Information:
This research was supported by the College of Natural Resources and the Minnesota Agricultural Experiment Station, University of Minnesota, St. Paul, the McIntire-Stennis Cooperative Forest Research Program, the USDA Forest Service, NCASI, and NASA. The authors gratefully acknowledge the assistance of the staff of the USDA North Central Research Station FIA unit, especially Dr. Mark H. Hansen and Dr. Ronald E. McRoberts. Additionally, we thank Dr. Erkki Tomppo of the Finnish Forest Research Institute, Helsinki, Finland for several helpful suggestions in support of this work.