Fuzzy Classification of Ecological Habitats from FIA Data
Abstract:Fuzzy c-mean classification (FCM), multi-layer perceptron neural network (MLP), fuzzy ARTMAP, and linear discriminant analysis (LDA) were used to classify Forest Inventory and Analysis (FIA) plots into six ecological habitats in the U.S. Northeast. Among the four classifiers, both FCM and MLP produced “soft” classifications based on fuzzy membership values. In contrast, fuzzy ARTMAP and LDA generated only “hard” classifications in which a plot is assigned to the dominant habitat class, based on binary logic, without considering any coexisting classes. The error matrix and several accuracy indices were calculated to assess the classification accuracy of the methods and to test the differences among the four classifiers. The classification accuracies of FCM and MLP were 98% and 97%, respectively, for overall classification, while fuzzy ARTMAP and LDA had an overall classification accuracy of 92% and 85%, respectively. The χ2 test showed that there was no significant difference between the FCM and MLP methods in classification accuracy. But they were significantly better than both fuzzy ARTMAP and LDA methods at a significant level of α = 0.05. This study showed that the fuzzy classifiers such as FCM and MLP were preferable in the classification of ecological habitats using FIA plots, especially for the classification of ambiguous plots with mixed overstory and understory species compositions. FOR. SCI. 50(1):117–127.
Keywords: Fuzzy c-means classification; accuracy assessment; ecological habitat; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; fuzzy ARTMAP; fuzzy membership value; linear discriminant analysis; multi-layer perceptron (MLP); natural resource management; natural resources
Document Type: Regular Article
Affiliations: 1: Associate Professor Faculty of Forest and Natural Resources Management State University of New York, College of Environmental Science and Forestry One Forestry Drive Syracuse NY 13210 Phone: 315-470-6558, Fax: 315-470-6535, Email: email@example.com 2: Research Assistant Faculty of Forest and Natural Resources Management State University of New York, College of Environmental Science and Forestry One Forestry Drive Syracuse NY 13210 Phone: 315-446-0980, Email: firstname.lastname@example.org 3: Professor Faculty of Forest and Natural Resources Management State University of New York, College of Environmental Science and Forestry One Forestry Drive Syracuse NY 13210 Phone: 315-470-6569, Email: email@example.com 4: (Deceased) Research Forester Northeastern Research Station The USDA Forest Service Durham NH 03824 5: Professor College of Natural Resources, Forestry and Agriculture University of Maine Orono ME 04469 Phone: 207-581-2836, Email: firstname.lastname@example.org 6: L.E. Caldwell Co. P.O.Box 563 Turner ME 04282 Phone 207-225-3955, Email: email@example.com
Publication date: 2004-02-01
- Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2015 Impact Factor: 1.702
Ranking: 16 of 66 in forestry
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