Result: Object-Based Segmentation and Classification of One Meter Imagery for Use in Forest Management Plans
Title:
Object-Based Segmentation and Classification of One Meter Imagery for Use in Forest Management Plans
Authors:
Source:
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
Publisher Information:
DigitalCommons@USU
Publication Year:
2010
Collection:
Utah State University: DigitalCommons@USU
Subject Terms:
Document Type:
Academic journal
text
File Description:
application/pdf
Language:
unknown
Relation:
DOI:
10.26076/b745-a368
Availability:
Rights:
Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact digitalcommons@usu.edu.
Accession Number:
edsbas.35F6DED7
Database:
BASE
Further Information
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.