Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Cooke, William H.
Committee Member
Evans, David. L.
Committee Member
Jonkman, Jeffery
Committee Member
Dewey, Chris D.
Date of Degree
5-13-2006
Document Type
Graduate Thesis - Open Access
Major
Geosciences
Degree Name
Master of Science
College
College of Arts and Sciences
Department
Department of Geosciences
Abstract
There is a lack of precise and universally accepted approach in the quantification of carbon sequestered in aboveground woody biomass using remotely sensed data. Drafting of the Kyoto Protocol has made the subject of carbon sequestration more important, making the development of accurate and cost-effective remote sensing models a necessity. There has been much work done in estimating aboveground woody biomass from spectral data using the traditional multiple linear regression analysis approach and the Finnish k-nearest neighbor approach, but the accuracy of these methods to estimate biomass has not been compared. The purpose of this study is to compare the ability of these two methods in estimating above ground biomass (AGB) using spectral data derived from Landsat ETM+ imagery.
URI
https://hdl.handle.net/11668/19615
Recommended Citation
Prabhu, Chitra L., "Comparison of the Utility of Regression Analysis and K-Nearest Neighbor Technique to Estimate Above-Ground Biomass in Pine Forests Using Landsat ETM+ imagery" (2006). Theses and Dissertations. 1121.
https://scholarsjunction.msstate.edu/td/1121