Comparison of Digital Image Processing Techniques for Classifying Arctic Tundra

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Mary B. Hall-Brown, Lecturer (Creator)
Roy S. Stine, Associate Professor and Director of Graduate Studies (Creator)
The University of North Carolina at Greensboro (UNCG )
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Abstract: The arctic tundra vegetation classified in the study area, Toolik Lake Field Station, Alaska, was relatively small in stature (with varying species growing in clusters) and must therefore be placed in different communities. This study compared different digital image processing classification techniques, including unsupervised, supervised (using spectral and spatial features), and expert systems. The dataset was a pan-sharpened 5 × 5 meter spatial resolution SPOT image. Accuracy assessments based on field inspections of each final map were performed. The expert system classification yielded the highest overall accuracy of 74.66%, with a Kappa coefficient of agreeement of 0.6725.

Additional Information

GIScience & Remote Sensing. 47(1), 78-98
Language: English
Date: 2010
Geology, Tundra, Vegetation, Remote Sensing

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