Mapping historical forest types in Baraga County Michigan, USA as fuzzy sets

Mapping historical forest types in Baraga County Michigan, USA as fuzzy sets Data on tree location and species in a portion of Northern Michigan were gathered from General Land Office (GLO) survey notes (ca. 1850), digitized, and generalized to represent forest types. Fuzzy membership values describing the degree of membership of each species in each forest type were derived from (a) semantic information in the forestry literature and (b) a fuzzy clustering routine applied to data from randomly placed circular plots. The fuzzy membership values assigned to each tree point for each forest type were interpolated to form continuous surfaces using kriging and co-kriging. Advantages of this method over traditional discrete mapping methods include: (a) multiple options are available for the display and analysis; (b) classification uncertainty and the continuity of natural vegetation can be represented; and (c) the classification scheme is applied systematically across the entire map area and can be altered to produce alternative maps. The subset of available display and analytical products presented include: discrete forest type maps; a surface representing the confusion between forest types; fuzzy logical overlays of forest types; and discrete class maps with color value altered within each class to indicate degree of confusion at each location. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Plant Ecology Springer Journals

Mapping historical forest types in Baraga County Michigan, USA as fuzzy sets

Plant Ecology, Volume 134 (1) – Sep 28, 2004

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Publisher
Springer Journals
Copyright
Copyright © 1998 by Kluwer Academic Publishers
Subject
Life Sciences; Plant Sciences
ISSN
1385-0237
eISSN
1573-5052
D.O.I.
10.1023/A:1009796502293
Publisher site
See Article on Publisher Site

Abstract

Data on tree location and species in a portion of Northern Michigan were gathered from General Land Office (GLO) survey notes (ca. 1850), digitized, and generalized to represent forest types. Fuzzy membership values describing the degree of membership of each species in each forest type were derived from (a) semantic information in the forestry literature and (b) a fuzzy clustering routine applied to data from randomly placed circular plots. The fuzzy membership values assigned to each tree point for each forest type were interpolated to form continuous surfaces using kriging and co-kriging. Advantages of this method over traditional discrete mapping methods include: (a) multiple options are available for the display and analysis; (b) classification uncertainty and the continuity of natural vegetation can be represented; and (c) the classification scheme is applied systematically across the entire map area and can be altered to produce alternative maps. The subset of available display and analytical products presented include: discrete forest type maps; a surface representing the confusion between forest types; fuzzy logical overlays of forest types; and discrete class maps with color value altered within each class to indicate degree of confusion at each location.

Journal

Plant EcologySpringer Journals

Published: Sep 28, 2004

References

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