Advisor

Parker,Robert

Committee Member

Matney, Thomas

Committee Member

Evans, David

Committee Member

Schultz, Emily

Committee Member

Baca, Jullie

Date of Degree

5-1-2011

Document Type

Dissertation - Open Access

Degree Name

Doctor of Philosophy

College

College of Forest Resources

Department

Department of Forestry

Abstract

Multiple linear and ordinal logistic regression methods were used to develop cubic foot volume (outside bark to a pulpwood diameter top) estimation models for the central Mississippi Institute for Forest Inventory (MIFI) inventory region of Mississippi, USA based on multi-scene Landsat derived variables. These models were used to stratify the region into volume classes to estimate the statistical gains made from a stratified random sample versus a complete random sample. Ordinal logistic regression produced higher accuracy statistics for all forest cover classes except the mixed forest cover class and the method is recommended to be used to estimate cubic foot volume (outside bark to a pulpwood diameter top) for the study area. Statistical gains from ordinal logistic regression averaged 30.34% and relative precision averaged 1.53 for the study area. For each forest cover type volume model that was produced, it was found that the interaction variable between Landsat TM band 5 and the GIS age variable was statistically significant.

URI

https://hdl.handle.net/11668/14897

Comments

remote sensing||forest inventory||stratification||Landsat

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