Date of Award
Fall 2012
Project Type
Thesis
Program or Major
Natural Resources
Degree Name
Master of Science
First Advisor
Russell G Congalton
Abstract
A study was performed to evaluate remote sensing methods for classifying land cover and land cover change throughout a two-county area in Northeastern Oregon (1986-2011). In the past three decades, this region has seen significant changes in forest management -- changes that can be readily identified from the synoptic perspective. This study employs an accuracy assessment-based empirical approach to test a number of advanced digital image processing techniques that have recently emerged in the field of remote sensing. The accuracies are assessed using traditional and area-based error matrices. It was determined that, for single-time land cover classification, Bayes pixel-based classification using samples created with segmentation parameters of scale 8 and shape 0.3 resulted in the highest overall accuracy. For land cover change detection, it was determined that Landsat 5 TM band 7 with a change threshold of 1.75 SD resulted in the highest accuracy for forest harvesting detection.
Recommended Citation
Campbell, Michael James, "An empirical study of image processing methods for land cover classification and forest cover change detection in Northeastern Oregon's timber resource-dependent communities (1986-2011)" (2012). Master's Theses and Capstones. 720.
https://scholars.unh.edu/thesis/720