Object-Based Segmentation and Classification of One Meter Imagery for Use in Forest Management Plans
Date of Award:
5-2010
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Wildland Resources
Committee Chair(s)
R. Douglas Ramsey
Committee
R. Douglas Ramsey
Committee
Mark W. Brunson
Committee
Michael R. Kuhns
Abstract
This research developed an ArcGIS Python model that extracts polygons from aerial imagery and assigns each polygon a vegetation type based on a modified set of landcover classes from the Southwest Regional Gap Analysis Project. The model showed an ability to generate polygons that accurately represent vegetation community boundaries across a large landscape. The model is for use by the Utah Division of Forestry, Fire, and State Lands to assist in the preparation of forest management plans. The model was judged useful because it was easy to use, it met a designated 50% threshold of useable polygons, and it met a designated 50% threshold of vegetation class assignment accuracy.
Checksum
89df5d2a832ea3f61872340b7a1d4273
Recommended Citation
Wells, W. Kevin, "Object-Based Segmentation and Classification of One Meter Imagery for Use in Forest Management Plans" (2010). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 653.
https://digitalcommons.usu.edu/etd/653
Included in
Agricultural and Resource Economics Commons, Forest Management Commons, Geographic Information Sciences Commons
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