TY - JOUR
T1 - Alternative representations of in-stream habitat
T2 - Classification using remote sensing, hydraulic modeling, and fuzzy logic
AU - Legleiter, Carl J.
AU - Goodchild, Michael F.
N1 - Funding Information:
The Probe-1 data collected by Earth Search Science, Inc., and ADAR imagery acquired by Positive Systems, Inc., were purchased through a NASA EOCAP grant (Stennis Space Flight Center) administered through Yellowstone Ecosystem Studies. Special thanks are due to W. Andrew Marcus and Robert L. Crabtree for the opportunity to pursue this project and to Jim Rasmussen and Rob Ahl for their help in the field. Peter Steffler made the Kananaskis River data set available, Jingxiong Zhang provided valuable advice, Phaedon Kyriakidis developed sections of code adapted for use in this study, and three thoughtful reviewers helped improve the original manuscript. The American Society for Engineering Education, National Science Foundation, and California Space Institute provided financial support.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2005/1
Y1 - 2005/1
N2 - Improved techniques are needed to characterize complex fluvial systems and monitor ecologically important, yet highly vulnerable riverine environments. This paper explores potential alternatives to traditional mapping of in-stream habitat and presents fuzzy set theory as a means of departing from the rigid, Boolean, object-based framework. We utilize hydrodynamic modeling, remotely sensed data, and fuzzy clustering to obtain classifications that allow for continuous partial membership and gradual transitions among habitat types. Methods of assessing cluster validity are available, but data quality is a crucial consideration. Crisp, vector-based representations can be derived from raster fuzzy classifications by applying a threshold to maximum membership values. This process results in conditional objects separated by ambiguous transition zones, and a compromise must be reached between the proportion of the channel assigned to polygons and the certainty with which this assignment can be made. Spatial patterns of classification uncertainty can also be used to identify areas of confusion, infer boundaries of variable width, and highlight areas of increased habitat diversity. Hydraulic modeling and remote sensing complement one another and, together with field work, could provide a more realistic representation of the fluvial environment.
AB - Improved techniques are needed to characterize complex fluvial systems and monitor ecologically important, yet highly vulnerable riverine environments. This paper explores potential alternatives to traditional mapping of in-stream habitat and presents fuzzy set theory as a means of departing from the rigid, Boolean, object-based framework. We utilize hydrodynamic modeling, remotely sensed data, and fuzzy clustering to obtain classifications that allow for continuous partial membership and gradual transitions among habitat types. Methods of assessing cluster validity are available, but data quality is a crucial consideration. Crisp, vector-based representations can be derived from raster fuzzy classifications by applying a threshold to maximum membership values. This process results in conditional objects separated by ambiguous transition zones, and a compromise must be reached between the proportion of the channel assigned to polygons and the certainty with which this assignment can be made. Spatial patterns of classification uncertainty can also be used to identify areas of confusion, infer boundaries of variable width, and highlight areas of increased habitat diversity. Hydraulic modeling and remote sensing complement one another and, together with field work, could provide a more realistic representation of the fluvial environment.
KW - Fuzzy classification
KW - Hydraulic modeling
KW - Indeterminate boundaries
KW - Remote sensing
KW - Rivers
KW - Uncertainty
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U2 - 10.1080/13658810412331280220
DO - 10.1080/13658810412331280220
M3 - Article
AN - SCOPUS:12744255429
VL - 19
SP - 29
EP - 50
JO - International Journal of Geographical Information Science
JF - International Journal of Geographical Information Science
SN - 1365-8816
IS - 1
ER -