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Forest Type Classification Accuracy Assessment for Michigan's State and National Forests

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Michigan has significant forest resources, and there is much debate about the use and protection of the forests. Managers, analysts, and the public have multiple sources for data regarding Michigan's forests, but these sources vary in how accurately they characterize forests. A close examination of three data sources shows considerable differences in accuracies of public land data sets. Forest Inventory and Analysis (FIA) plots were treated as the reference classification system, given their consistent application procedures on the ground. National forest and state forest tactical data sets and the Michigan Department of Natural Resources' remotely sensed data sets were compared with 753 FIA plots. The accuracies of the on-the-ground tactical data sets exceeded 82%, but remotely sensed data were accurate only 65% of the time. The latter may serve as a proxy for private lands that do not have tactical inventory data sets. Aggregating remotely sensed data and combining tactical and remotely sensed data would improve overall accuracy across landscapes with both public and private lands.

Keywords: Forest Inventory and Analysis; accuracy assessment; national forests; remotely sensed data; state forests

Document Type: Research Article


Publication date: March 1, 2012

More about this publication?
  • Each regional journal of applied forestry focuses on research, practice, and techniques targeted to foresters and allied professionals in specific regions of the United States and Canada. The Northern Journal of Applied Forestry covers northeastern, midwestern, and boreal forests in the United States and Canada.
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