Date of Award

Fall 12-1-2005

Document Type

Thesis

Degree Name

Master of Arts (MA)

Department

Geography, Geology, and Anthropology

First Advisor

Ryan Jensen

Second Advisor

Paul Mausel

Third Advisor

Jim Speer

Abstract

This study assessed the performance of different biomass estimation methods using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) lll data in a temperate forest. Multiple linear regression statistics of spectral band data and derived indices with biomass values were compared with advanced neural network models created with spectral band data and indices to model biomass values. Biomass for the study area was estimated using both of these methods and the results were discussed. The data were analyzed using multivariate statistical analysis. Correlation analysis and regression analysis were employed to understand the relationship of biomass to the spectral data and which form of spectral data explained most of the variation in biomass. Advanced neural networks, a non linear technique of modeling a complex function that represents the relationship between the input and output variables being modeled were used for modeling biomass using spectral band data and derived indices. It was observed that spectral band data in a multiple regression model explained more variation in biomass as compared to the band ratios for both the statistical and neural network models, though the relationship was not very strong. The different neural networks tested did not perform better than linear statistics and the estimation error is comparable or higher. These results indicate that ASTER spectral data obtained over a forest area contain biomass information; however neither of the two methods compared can be used for accurate estimation of biomass for an area of size used in this study.

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